Applying the taguchi method to study the influence of process parameters in waam tig welding without filler material on the tensile strength of test samples

HUYNH DO SONG TOAN NGUYEN TRAN HUU THANG TRAN ANH TU Ho Chi Minh city, July 2024 APPLYING THE TAGUCHI METHOD TO STUDY THE INFLUENCE OF PROCESS PARAMETERS IN WAAM TIG WELDING WITHOUT FILL

INTRODUCTION

Reasons for choosing the project

The rapid advancement of 3D printing technology, or additive manufacturing, is transforming various sectors, including industrial production, medicine, architecture, and construction While primarily utilized with plastic materials for model creation, traditional 3D printing faces challenges related to product strength and heat resistance To overcome these limitations, metal 3D printing technology has emerged as a robust solution, enhancing the durability and performance of printed products.

Metal 3D printing technology has advanced significantly worldwide, but in Vietnam, its accessibility is limited due to high machine and material costs To address these challenges, the Department of Mechanical Engineering is exploring 3D printing using sheet materials and a CNC system, leveraging TIG welding arc energy to fuse metal layers This innovative approach not only reduces equipment and material expenses but also shortens manufacturing time compared to traditional machining methods.

Advancing scientific and technical development in our country is essential; however, the machining process can be hindered by undefined noise factors that lead to residual stress and product deformation due to high temperatures, ultimately affecting formability To safely implement 3D printing technology, it is vital to research and identify optimal parameter sets Various methods exist for parameter optimization, yet they face challenges: theoretical approaches often lack reliability, while experimental methods, though reliable, necessitate extensive experimentation The Taguchi method emerges as an effective solution to these challenges.

The Taguchi method is an experimental optimization technique aimed at developing processes that minimize the impact of noise factors By employing orthogonal arrays in experimental design, this method streamlines the number of experiments required and facilitates rapid parameter adjustments, enabling the efficient attainment of optimal performance levels.

This thesis explores the application of the Taguchi method to investigate how process parameters affect the tensile strength of test samples in WAAM TIG welding without filler material, aiming to enhance and optimize the research and development process of this emerging technology.

Scientific and practical significance of the project

The experimentation of metal 3D printing through the melting of stacked metal sheets will produce an optimal parameter table, forming a foundational basis for advancing metal 3D printing technology This research will also enhance students' understanding of both traditional and metal 3D printing methodologies.

Combining metal 3D printing with TIG welding and CNC machines offers Vietnamese businesses an affordable entry point into advanced manufacturing technology This innovative approach not only reduces initial investment costs but also serves as a valuable resource for engineering students, providing essential research and learning materials on metal 3D printing techniques.

Research objectives of the project

Objective of the thesis is:

− Conduct a comprehensive overview of metal 3D printing technology

− Experiment with creating 3D printed models using different printing parameters

− Synthesize the results obtained from the tests

− Determine the optimal set of parameters.

Subjects and scope of research

The research objective is to conduct a comprehensive overview of the metal 3D printing method using the combined approach of non-filler wire TIG welding and CNC machining 1.4.2 Scope of research

This research investigates the welding of metal sheets through the non-filler wire TIG welding method, with a particular focus on experimenting with the metal 3D printing process By melting stacked metal sheets in a linear arrangement, the study synthesizes and analyzes data to identify the optimal welding parameters.

− Parameters: welding current, machine speed, welding distance, sheet thickness

Research methods

This research focuses on analyzing documents concerning TIG welding technology, CNC machines, tensile testing methods, the implementation of the Taguchi method in experimental design, and the application of Artificial Neural Networks (ANN).

Structure of the thesis

The contents of the graduation thesis consist of the following seven chapters:

− Chapter 4: Conducting tensile test specimen preparation and testing

− Chapter 5: Application of Taguchi method to analyze tensile test results

− Chapter 6: Application of ANN method to predict results

OVERVIEW OF METAL 3D PRINTING TECHNOLOGY

Understanding metal 3D printing technology

Recent advancements in 3D printing technology have transformed multiple industries by enhancing flexibility, creativity, and efficiency This innovative approach not only minimizes waste but also improves precision in manufacturing processes As a result, it provides substantial benefits across various sectors, facilitating the development of advanced products tailored to meet diverse consumer needs.

Metal 3D printing is a cutting-edge technology that enables the production of three-dimensional metal objects by layering interconnected metal materials Unlike conventional manufacturing techniques such as machining, milling, or casting, this innovative process eliminates the need for molds or specialized tools, offering greater design flexibility and efficiency.

Metal 3D printing is an advanced laser-based technology that builds metal components layer by layer, making it ideal for prototyping and creating complex parts while streamlining assembly processes This versatile technology supports a growing range of materials, serving various industries including aerospace, healthcare, jewelry, and plastics manufacturing While some printing processes are tailored to specific materials, others offer the flexibility to work with multiple types, enhancing its applicability across different sectors.

Advancements in plastic 3D printing technology have laid the groundwork for the development of metal 3D printing The invention of the laser sintering system for plastics by Dr Carl Deckard in the late 1980s was a pivotal moment that facilitated the evolution of metal 3D printing technology.

Subsequently, in 1991, Dr Ely Sachs also made a significant contribution to the development of a process called binder jetting, which was later applied to metals, known as Binder Jetting

In the 2000s, companies and universities made significant strides in advancing and commercializing metal 3D printing technology Today, this technology continues to evolve, presenting exciting opportunities across various industries.

Methods of metal 3D printing technology

2.2.1 Selective Laser Melting method (SLM)

Selective Laser Melting (SLM) [1] is known as one of the most widely used methods in metal 3D printing technology This process utilizes a high-power laser beam to melt and fuse

5 metal powder particles together to form metal layers This process is repeated layer by layer until the desired product is completed

2.2.2 Electron Beam Melting method (EBM)

Both the SLM and Electron Beam Melting (EBM) methods employ heat to melt and fuse metal powder particles The key distinction between the two is that EBM utilizes a high-energy electron beam, while SLM relies on a laser beam for the melting process.

2.2.3 Direct Metal Laser Sintering method (DMLS)

Direct Metal Laser Sintering (DMLS) is often confused with Selective Laser Melting (SLM) due to their similarities However, the key difference lies in the laser intensity; DMLS employs a lower-intensity laser that partially melts the metal, allowing for the bonding of metal particles without fully melting them.

Binder Jetting is a key method in metal 3D printing technology that utilizes a liquid binding agent to combine metal powder, sand, ceramics, or composites, resulting in a 3D product based on a design file After the printing process, the product is subjected to a furnace treatment, which solidifies the binding material, enhancing its durability and ensuring it meets the necessary strength requirements for practical applications.

To have a clearer overview of the methods of metal 3D printing technology, we can summarize them based on the table below:

Table 2 1: Comparison of methods in metal 3D printing technology [2]

SLM EBM DMLS Binder Jetting

- Absolutely, all of these methods involve the use of metal powder

- They create 3D products layer by layer according to the designed file

- The process takes place in a closed environment filled with inert gas

- Post-processing is required after the printing process

Utilize high- power laser beams to melt metal

Employ high- energy electron beams to melt metal

Use high- power laser beams to melt metal powder

Use liquid binding agents to bond metal powder

Nearly fully melt metal powder

Do not fully melt metal powder

Equivalent to forged metal, high precision, capable of producing complex details

Equivalent to forged metal, high precision, capable of producing complex details

Lower than SLM, high precision, capable of producing complex details

Low, low precision, difficult to produce complex details

The product requires high precision and high mechanical properties

The product demands high strength, heat resistance, and excellent corrosion resistance

The product requires high precision, intricate details, at a lower cost than SLM

Rapid prototyping, the product does not require very high mechanical properties

Materials used in metal 3D printing

Steel, a vital material in the industrial and construction sectors, is an alloy composed of iron and carbon It is frequently processed and heat-treated to enhance its mechanical properties, making it essential for various applications.

Steel is the predominant material used in metal 3D printing due to its remarkable strength, corrosion resistance, high ductility, and favorable thermal properties This versatility allows it to be utilized across diverse sectors, including industry and healthcare Among the various types of steel, tool steel and stainless steel are the most commonly employed.

Some prominent advantages of using steel in 3D printing technology include:

The table below outlines the fundamental properties of various steel materials, with values intended for reference, as they can differ based on the specific steel type and manufacturing methods Additionally, factors such as corrosion resistance, fatigue strength, and magnetic characteristics may vary considerably due to the alloy composition and heat treatment processes applied to the steel.

Table 2 2: Properties of steel materials [3]

Titanium is a lightweight, silver-colored metal known for its remarkable strength and exceptional corrosion resistance With a lower density than steel, titanium maintains high durability while also offering excellent resistance to heat and various environmental factors.

Titanium, while not widely utilized in conventional manufacturing, is an ideal material for 3D printing due to its favorable strength-to-weight ratio and the high costs associated with both materials and processing The two primary forms of titanium used in 3D printing are titanium alloys and pure titanium Key advantages of using titanium in 3D printing include its durability, lightweight properties, and resistance to corrosion.

− Low specific weight: Titanium has a lower specific weight compared to many other metals, approximately half the density of steel

− Corrosion resistance and heat resistance

− Biocompatibility: Titanium is a biocompatible material, meaning it does not cause allergies or have harmful interactions with the human body

Titanium in metal 3D printing offers a unique combination of physical, mechanical, and biological properties, making it a crucial material in 3D printing technology and related industries

Table 2 3: Properties of titanium materials [3]

Copper is a unique metal that naturally occurs in its metallic form, allowing for direct use without the need for extraction from ore Its significance spans multiple industries and everyday applications, making it an essential material in modern life.

Copper is a highly versatile material in metal 3D printing, known for its excellent machining properties, ductility, and superior thermal and electrical conductivity Its high surface quality and chemical stability, along with the ability to form alloys, make copper ideal for diverse applications across various industries and technological advancements.

Some prominent advantages of Copper when applied in 3D printing technology:

− Ductility and ease of machining: Copper is a soft metal and easy to bend, allowing for the creation of complex-shaped parts

− Good thermal and electrical conductivity

− Good surface quality: The 3D printing process of copper can yield smooth and high- quality surfaces

− Chemical stability: Copper is not easily corroded in normal environments

Copper is a versatile metal known for its ability to form alloys with various other metals, including zinc, tin, and nickel This alloying capability allows for the creation of materials with unique properties and diverse applications, making copper an essential material in numerous industries.

Table 2 4: Properties of copper materials [3]

The emergence of metal 3D printing has revolutionized the production of high-value alloys at lower costs, overcoming the challenges and expenses associated with traditional manufacturing methods This innovative technology allows companies to create high-performance parts and components efficiently Additionally, superalloys utilized in 3D printing demonstrate exceptional durability in extreme conditions, including exposure to corrosive chemicals and high temperatures.

Superalloys like Cobalt Chrome and Inconel are highly favored for 3D printing due to their outstanding mechanical properties, corrosion resistance, and high-temperature stability These characteristics make them ideal for diverse industrial applications, particularly in the aerospace, automotive, and medical sectors.

Some prominent advantages of superalloys when applied in 3D printing technology:

− Good heat resistance and corrosion resistance

Aluminum, a soft silver-white metal, develops a protective oxide layer when exposed to air, which effectively prevents corrosion Its moderate strength, excellent thermal properties, and lightweight characteristics make it a versatile material used in diverse industries, including industrial applications and aerospace.

Aluminum is increasingly preferred in metal 3D printing machines, diverging from its traditional manufacturing uses The limited presence of aluminum parts in 3D printing can be attributed to two main factors: its cost-effectiveness and its comparatively lower printing capabilities than conventional manufacturing methods.

Some prominent advantages of aluminum applied in 3D printing technology:

− Lightweight: Aluminum is extremely lightweight, so utilizing it in metal 3D printing processes helps reduce the overall weight of the product

− Good thermal conductivity: Aluminum has excellent thermal conductivity By using aluminum in metal 3D printing, complex and efficient cooling structures can be created

− Easy to machine and shape into various forms

Process of metal 3D printing technology

Metal 3D printing enables the production of intricate and detailed products that are unattainable through conventional methods This innovative process enhances flexibility and optimizes the manufacturing of metal components, significantly minimizing time, effort, and material waste compared to traditional manufacturing techniques.

Metal 3D printing involves an advanced manufacturing process that enables the creation of complex metal products by layering and bonding metal layers together

Overview of the metal 3D printing process:

1 Preparation and Design: The first step for any metal 3D printing method is to create a Computer-Aided Design (CAD) model of the desired product on a computer

2 Material Preparation: Suitable metal materials (such as aluminum, steel, titanium, nickel, copper, etc.) are prepared in the form of metal powder or metal sheets in appropriate shapes These metals are often finely sized and uniform

3D printing is a process that involves layering metal powder on a platform and using a laser or heat source to selectively melt it according to a designed model By focusing energy onto specific points, the metal powder melts and fuses together, creating the desired shape Additionally, using appropriately sized metal sheets can help save time during assembly.

4 Repeat the Process: Once a layer of metal has been melted and fused together, the platform moves down a small distance, and the next layer of metal powder is placed on top This process repeats until the entire product is complete

5 Finishing and Post-Processing: After the 3D printing process is complete, the product is removed from the platform and moved to the finishing stage Finishing processes may include assembly, surface machining, and heat treatment processes to improve the mechanical properties of the product.

Applications

In the aerospace industry, developing lightweight structures is essential due to the high costs associated with launching payloads into space Metal 3D printing offers significant potential for optimizing structural components, thereby reducing weight and costs.

Metal 3D printing is revolutionizing the medical field by enabling the creation of personalized products that cater to individual anatomical needs A key application of this technology is the production of medical implants made from biocompatible materials, such as titanium, which enhances patient outcomes and customization.

The automotive industry is increasingly embracing metal 3D printing for manufacturing assembly components, driven by the need for high performance and quick turnaround times This technology is becoming a vital option for enhancing production efficiency and meeting the demands of modern automotive manufacturing.

Industrial Manufacturing: Metal 3D printing is widely used to produce functional industrial products These advanced products can significantly enhance the productivity of various processes.

Benefits and limitations of metal 3D printing technology

The benefits of metal 3D printing technology, one of the widely used manufacturing methods today, we can list:

− Ease of handling complex-shaped parts without additional costs, as no specific tools are needed, making structures that cannot be produced through other processes easily achievable through 3D printing

− Optimization in flexible design while creating lightweight structures

− Increased functional efficiency of the product by being able to manufacture parts with internal structures

The integration of clusters into a single component eliminates the need for screws, allowing for multifunctional parts that enhance efficiency This approach significantly reduces labor costs and processing time while also minimizing maintenance and service requirements.

− Producing products with excellent durability

In addition, metal 3D printing technology also has several limitations to consider:

− Higher costs compared to traditional manufacturing In some cases, the strength and durability of printed products may also be limited

− Metal 3D printing is not economically favorable based on size; it may not compete with traditional manufacturing for larger-sized products

Designing parts for metal 3D printing necessitates following a unique set of guidelines distinct from traditional manufacturing Consequently, existing designs may require significant modifications to align with the specific demands of metal 3D printing.

− Post-processing is almost always required Most metal 3D-printed parts will need post- processing before they are ready for use This adds to the overall cost and delivery time

Comparison of 3D printing and other manufacturing methods

Starting with a cost analysis compared to performance when choosing between metal 3D printing technology and subtractive manufacturing (CNC machining) or metal molding (casting) is always essential

In general, production costs are primarily related to production volume, while the performance of a part largely depends on its shape

Metal 3D printing excels in producing intricate and optimized geometries, making it perfect for high-performance component manufacturing However, it falls short in scalability compared to CNC machining and metal molding for large production volumes The high expenses associated with metal 3D printing are justifiable only when they result in substantial improvements in performance or operational efficiency.

The following comparison table outlines the differences between the two methods; however, it is essential to note that this overview is general and may differ based on specific factors, including materials, production scale, and the unique requirements of each application.

Table 2 5: Comparison of cutting and metal 3D printing methods [4]

Principle Removing metal material by cutting

Accuracy Achieving high accuracy Achieving very high accuracy

Shape Limitations in creating complex shapes

Capable of creating complex shapes

Flexibility Limitations in modifying designs and producing rapid prototypes

Capable of modifying designs and producing rapid prototypes

Production process Requires multiple traditional machining steps and tools

Streamlines the production process and reduces the number of tools

Surface quality Can achieve good surface quality Requires post-processing steps after printing to achieve good surface quality

Product size Limitations in producing large parts Can produce large parts and composite products

Production time Fast machining time 3D printing time is relatively long

Application Suitable for large batch production and simple products

Suitable for small batch production, complex products, rapid prototype

Applying non-filler TIG welding to metal 3D printing technology

WAAM (Wire Arc Additive Manufacturing) is an innovative metal 3D printing technology that utilizes welding techniques This process involves melting metal materials with a welding wire and a high heat source, such as electricity or laser, to create strong metallurgical bonds The team utilizes the non-consumable TIG welding method, allowing for the fusion of metals without the necessity for additional filler wire.

TIG welding, or Tungsten Inert Gas welding, utilizes electricity to generate an arc between a non-melting tungsten electrode and the metal being welded This process, also referred to as Gas Tungsten Arc Welding (GTAW), effectively joins metal pieces by harnessing the heat produced by the welding current An inert gas, typically Argon, is supplied to the welding torch to shield the arc, protecting the metal from oxidation and preventing the formation of small voids.

TIG welding produces an extremely high arc temperature, exceeding 6100°C When welding thin materials with tight joints, the weld metal may consist entirely of the base metal, or it can be enhanced with a filler rod The weld pool is effectively protected by inert gas emitted from the gas cup, ensuring a clean and strong weld.

You can observe the diagram below to understand the construction of TIG welding, which includes the main welding machine, TIG torch, welding clamp, etc

Figure 2 1: Main structure diagram of TIG welding set [5]

This method has several notable advantages:

− It produces high-quality welds for most metals and alloys

− No post-weld cleaning is required

− The arc and weld pool can be observed during welding

− There is no spattering of metal

− It can be performed in any position

− Concentrated heat allows for increased welding speed and reduced deformation of the weld joint

Figure 2 2: Welding arc zone and weld puddle [6]

2.8.2 Characteristics and applications of TIG welding

− No slag formation due to the absence of flux

− Arc and weld pool are easily observed and controlled

− Concentrated heat source with high temperature

− Can weld thin or thick metals due to wide range of adjustable welding parameters (from a few amps to several hundred amps)

− High-quality welds can be achieved for most metals and alloys

− Clean and neat welds, free from slag and spatter

− Easy control of penetration depth and weld pool shape

− Relatively high cost due to low productivity, expensive equipment, and materials Applications:

− Effective method for welding aluminum, stainless steel, and nickel alloys

− Often used for root pass welding in pressure pipe welding processes

− Welding of difficult-to-weld metals and alloys such as titanium and red brass

2.8.3 Main components of TIG welding

− Welding power sources can provide either direct current (DC) or alternating current (AC), or both

The choice of welding power source—whether a transformer, rectifier, or generator—depends on the specific welding application It's essential that the welding power source features an external characteristic with a positive slope, akin to the performance observed when using stick electrodes.

Control components for welding systems are usually integrated with the welding power source and consist of a contactor for interrupting welding current, a high-frequency arc starter, and an efficient cooling system featuring cooling fins and fans.

Figure 2 3: TIG welding power source of HK 200E welding machine [7]

− It functions to guide the welding current and shielding gas into the welding zone

The tungsten electrode is firmly secured within the torch body using retaining straps and screws, which are designed to fit the electrode's diameter precisely.

− Gas is supplied to the welding zone through a gas cup The gas cup, threaded onto the torch body, directs and distributes the shielding gas flow

− According to the cooling mechanism, TIG welding torches are divided into two types: +Air-cooled torches are cooled by gas and are suitable for welding currents up to 120A

+Water-cooled torches are cooled by water and are suitable for welding currents above 120A

Figure 2 4: Structure of TIG welding torch [7]

Figure 2 5: Clamp mass and lead wire [7]

Argon gas cylinder and flow control valve:

Figure 2 6: Structure of gas flow control valve [7]

The first step in the TIG welding process is to adjust the machine to the appropriate settings such as current and voltage using the knobs on the machine

To ensure optimal TIG welding performance, it's essential to adjust the shielding gas pressure in the supply cylinder using the flow control valve Additionally, customize the TIG welding torch by selecting the correct diameter electrode, TIG cup, and other necessary components based on product specifications Most importantly, always wear clean protective gear to maintain clear visibility during the welding process.

Once the preparatory work is finished, it's essential to focus on the welding process, paying attention to key factors such as arc length, travel speed, and torch angle to ensure optimal operation TIG welding effectively joins metals by fusing their particles without requiring extra filler material, making it a precise and efficient technique.

The material being welded significantly influences the TIG welding process, as various materials like stainless steel, aluminum, copper, and titanium possess distinct mechanical properties that affect the welding technique and outcomes.

Different materials exhibit varying conductivity and melting temperatures, which significantly influence the welding process The required temperature for effective welding is crucial, as it must be high enough to achieve adequate melting for proper fusion and bonding If the welding temperature is either too high or too low, it can lead to insufficient fusion or excessive melting, compromising the integrity of the weld.

The mechanical properties of materials, particularly ductility and hardness, significantly influence the welding process Materials that are excessively soft can deform or break during welding, whereas overly hard materials can create difficulties in forming strong bonds Therefore, choosing materials with suitable mechanical properties is crucial for achieving optimal welding results.

The chemical properties of a material significantly influence the welding process, particularly its ability to form alloys and interact with shielding gas and flux Materials that struggle to alloy or exhibit inadequate interaction with shielding gas can lead to inferior weld quality and increased risk of cracking.

Materials exhibiting excellent thermal stability retain their mechanical properties and dimensions throughout the welding process Conversely, materials with poor thermal stability may experience deformation, discoloration, or alterations in size following welding.

Figure 2 7: Some types of basic materials [8]

TIG welding, or tungsten inert gas welding, emphasizes the importance of selecting the right electrode, as it significantly influences the overall quality of the welding process.

There are two primary types of tungsten electrodes used in welding: pure tungsten, which contains 99.5% tungsten and is identified by its green tip, and thoriated tungsten, containing about 2% thorium and marked with a red tip Additionally, tungsten electrodes with additives such as Cerium (EWCe) enhance anti-sticking and corrosion resistance, significantly impacting the durability and quality of welds.

Figure 2 8: Types of Tungsten electrodes [7]

Process of steps in project execution

In overview, to complete this research and experimentation process, the team needs to follow 10 steps as outlined below:

Learn how to assemble a TIG welding gun

Cutting samples according to shape Sample extraction

Figure 2 17: Process of steps in project execution

BUILDING EXPERIMENTAL MODELS

Purpose of the experiment

The primary aim of the experiments is to optimize the TIG welding process by examining how various process parameters affect the tensile strength of test samples The goal is to identify the optimal set of parameters that will yield the highest tensile strength in the welded samples.

The Taguchi method is a statistical approach utilized to analyze the influence of different factors on key outcomes in manufacturing Specifically, in non-wire-additive TIG welding, parameters like welding current, voltage, welding speed, and torch angle can be optimized through systematic adjustments and testing to identify the most effective settings.

Experimental model

Figure 3 1: Modeling the TIG welding process

TIG welding sample modeling is presented through a simplified experimental diagram that offers readers a clear overview of the experimental steps involved This diagram serves as an introductory tool, allowing for a quick understanding before engaging in a more detailed analysis of the process.

1 Determine factors and levels of factors (noise factors):

− Factors: The process parameters of wire arc additive manufacturing (WAAM) with TIG welding (e.g., current, voltage, welding speed, torch angle, etc.)

− Levels: Different values for each factor (e.g., current: 100A, 150A, 200A; voltage: 20V, 25V, 30V)

− Use the Taguchi method to design an Orthogonal Array (OA) experimental matrix

− Determine the number and distribution of experiments to include all factors and levels in the experimental matrix

− Conduct experiments according to the designed experimental matrix

− Set up the process parameters of wire arc additive manufacturing (WAAM) with TIG welding based on the values in the experimental matrix

− Test the tensile strength of the test specimens after each experiment

− Perform an analysis of the experimental results to determine the influence of factors and levels on the tensile strength of the test specimens

− Use Artificial Neural Network (ANN) analysis method to analyze the data

− Based on the analysis results, identify the optimal process parameters of wire arc additive manufacturing (WAAM) with TIG welding to achieve the highest tensile strength for the test specimens

− Select the optimal values for each factor based on the analysis results and optimization objectives

− Validate the optimal process parameters by conducting additional experiments and measuring the tensile strength of the test specimens

− Verify the stability and reproducibility of the results when applying the optimal process parameters

Previous studies have shown that the welding process greatly affects the tensile strength of specimens To optimize welding parameters in this research project, the team has decided to incorporate additional output factors to better align with product standards.

External factors play a crucial role in the welding process, influencing the ability to achieve desired parameters Irregular factors, such as variations in material composition and hardness, as well as environmental conditions like extreme temperatures, can significantly impact the manufacturing process For instance, the heterogeneity of processing materials and the effects of hot or cold weather can greatly affect the cooling process of the workpiece post-processing.

Experimental conditions

The machining process is carried out on a 3-axis CNC Figure 3.3 machining machine, commonly referred to as a 3-axis WAAM machine, in the 02HĐ2 mechanical workshop of

Ho Chi Minh City University of Technology and Education has developed essential equipment for advancing metal 3D printing technology The precise operation of the X, Y, and Z axes significantly enhances the metal 3D printing process However, since this CNC machine was conceptualized, researched, designed, and assembled by university faculty and students, it still faces several challenges and does not yet match the high accuracy of contemporary industrial machines.

The WAAM machine has the capability to move along three axis:

– X-axis: moves left and right, with the positive direction towards the right

– Y-axis: moves in and out, with the positive direction outward towards the operator – Z-axis: moves up and down, with the positive direction upward

* Safety when using machines in the workshop:

To prioritize safety for ourselves and those around us, our team diligently follows the workshop regulations established by our instructors while working on our project Given the inherent dangers of using various machines in the workshop, we consistently adhere to these safety rules and implement the 5S principles to maintain a secure working environment.

The 5S principles are widely utilized across various industries, regardless of the size of the organization This effective model enhances business operations by creating a clean, organized, and modern workspace By fully adopting the 5S methodology, organizations can optimize workspace utilization, streamline production processes, and boost both labor productivity and product quality, while fostering a culture of positive engagement.

+ Separate and remove unnecessary materials that are not used in the production process

+ Identify and retain only those materials, tools, and equipment that are truly necessary for the job

+ Determine logical and efficient locations for necessary materials and tools

+ Ensure everything is placed in easily accessible positions to avoid wasting time searching for items

+ Clean and tidy the workspace, including the CNC machine and the surrounding area

+ Keep machines, tools, and equipment clean to ensure good performance and prevent malfunctions

+ Implement standardized management to maintain cleanliness and organization in the workplace

+ Ensure the 5S processes become an integral part of the organization's work culture – Shitsuke (Sustain):

+ Ensure compliance with and maintenance of the established rules and procedures

+ Foster a spirit of self-discipline and personal responsibility in maintaining the cleanliness, organization, and efficiency of the workplace

Figure 3 5: Notification Images for Entering and Exiting the Workshop

* Operation of the 3-Axis WAAM Machine:

Step 1: Open the MCCB (Moulded case circuit breaker) to power the electrical system Open the electrical cabinet → Turn MCCB to ON state as shown below

Figure 3 6: Step 1 Operating the 3-Axis WAAM Machine Step 2: Open the small MCCB → Plug in the power to power the 3-axis WAAM machine

Figure 3 7: Step 2 Operating the 3-Axis WAAM Machine

Step 3: Contact the instructor to borrow the key to the 3-axis WAAM machine → Start the machine

Figure 3 8: Step 3 Operating the 3-Axis WAAM Machine Step 4: Manipulate and operate the machine

Once you have familiarized yourself with the safety rules and completed the initial setup of the CNC machine, the next step is to operate the Mach3 CNC software, which runs directly on the device.

Figure 3 9: Step 4 Operating the 3-Axis WAAM Machine

To operate the 3-axis WAAM machine, our team writes the control program directly on the CNC machine using Notepad, an application available on most generations of Windows operating systems

Our team expertly performs welding along each horizontal line of the sample, creating a continuous weld line that spans from left to right across its surface After finishing one weld line, we adjust the machine table to position the next line adjacent to the previous one, continuing this method until one side of the sample is completely welded Once one side is finished, we rotate the sample and repeat the welding process to ensure both surfaces are fully welded, achieving a thorough and durable result.

Figure 3 10: User Interface of the 3-Axis WAAM Machine

The welding power source is crucial for 3D metal printing, and our team utilizes a Jasic 250A W227 welding machine, supported by our instructor This machine provides a continuous welding current, creating a molten metal arc essential for the metal 3D printing process.

Figure 3 11: Jasic 250A W227 Welding Machine Table 3 1: Jasic Welding Machine Specifications 250A W227

Input Power Voltage AC220V±15% 50 Hz

TIG Welding Power 6.6 KVA /MMA7.2KVA

Adjustable Current Range for TIG 10-200A / MMA:10-180A

Duty Cycle at Maximum Current (40 o C) 30% / MMA: 20%

Gas Post-flow Time 1- 10 giây

+ Ensure the welding machine is plugged into a stable and suitable power source

+ Identify the type of material and metal you are working with

+ Prepare welding materials, TIG electrode, shielding gas, and other necessary welding tools

+ Connect the power cable to the appropriate power source and verify the connection is correct

+ Connect the TIG welding torch to the welding machine

+ Connect the shielding gas hoses (if applicable) to the welding machine and gas cylinder

+ Set the welding current and voltage appropriate for the material and metal being welded

+ Adjust other parameters such as pre-flow time, post-flow time, and other settings depending on specific welding requirements

+ Ensure the welding material surface is clean and free from oil, dust, or other contaminants

+ Adjust the hinge or clamp to secure the welding material stable and convenient for welding

− Step 5: Power On and Check

+ Turn on the welding machine and wait for it to stabilize

+ Check that welding current, voltage, and other parameters are correctly set

+ Position the TIG electrode correctly and prepare it for welding

+ Use hands and feet to hold the welding material during the welding process

+ Perform welding along the desired weld seam, maintaining appropriate welding current intensity and travel speed

+ Upon completing the welding process, turn off the welding machine and ensure safety for all equipment and the surrounding area

+ Clean up the area according to 5S principles

The project's objective is to examine how process parameters in non-deflection compensated WAAM TIG affect the tensile strength of test samples To assess this tensile strength, we utilize a hydraulic tensile testing machine located in workshop 01HĐ1 at the University of Technical Education, which was collaboratively developed by professors and students This machine functions by applying a pulling force against the test sample, allowing us to measure the maximum load the sample can endure before failure.

Figure 3 12: Tensile Sample Testing Machine

Table 3 2: Sample Testing Tensile Machine

* using the test stretching device:

+ After obtaining permission to use the tensile testing equipment, switch on the main circuit breaker of the machine

+ Perform a preliminary inspection and overview

Figure 3 13: Open MCCB and Perform Preliminary Inspection of the Machine

− Step 2: Startup and Machine Control

+ Before operating the machine, it should be instructed by an experienced instructor or student

+ The tensile testing equipment consists of three main components: the main clamp machine, power system, and mode adjustment device

Figure 3 14: Preliminary Guide for the Tensile Testing Machine

Figure 3 15: Guide to the tensile testing machine Consle

Figure 3 16: Preliminary Guide for the Manual Control

Before operating the tensile testing equipment, securely attach the appropriate clamp based on the sample type to ensure safety during the clamping process.

Figure 3 17: Clamps + To start the tensile testing machine, first turn on the electrical switch and the oil switch

Before clamping the sample and after each test, ensure to open the valve on the left side by turning the knob clockwise As you do this, lower the upper clamp to the appropriate level, and then secure the oil line by locking it in the opposite direction.

Figure 3 18: Locking Knob Before Testing the Sample

To ensure proper alignment in the tensile testing machine, first clamp the sample with the upper clamp, followed by the lower clamp It's crucial that only one person handles the clamping process to prevent accidents, and hands should be kept clear of the clamping area when opening or closing the clamps to avoid accidental pinching.

Note: For improved accuracy, ensure the sample is centered between the upper and lower clamps and evenly secured on both sides

Figure 3 19: Images of Sample Mounting for Tensile Testing

− Step 4: Adjusting Mode and Input Parameters on the Computer

To begin the tensile test, launch the TestMaster3 software on your computer and select OK to access your existing account In the application window, choose either "Tensile Test" or "TN KEO THEP," based on the available options, to proceed with the test.

Acces to the TestMaster3 software on the computer

Access the software with the password 123

Figure 3 20: Opening the TestMaster3 program

Choose the Tensile tesst TN KEO THEO

Figure 3 21: Selecting the TN Keo Thep

+ After completing the steps above, the next task is to input the input parameters This allows us to obtain tensile test results after completing the sample pulling process

Figure 3 23: Input and Output Parameters of the Tensile Test

The NotRegular area[So] parameter refers to the weld zone area, calculated by multiplying the width of the weld bead by its height in a cross-sectional view For a clearer understanding, please refer to the accompanying image.

Figure 3 24: Cross-Sectional Area Parameters

− Step 5: Conducting the tensile test

Before starting the tensile test, ensure the machine door is securely closed for safety, and verify that both the speed control handle and release handle are in their initial positions.

To begin the survey, reset all initial settings to zero by pressing "Tare," selecting "Run," and then turning the speed control handle clockwise to increase the machine's pulling speed.

Figure 3 25: Set Initial Parameters to Zero for Survey + Press "Tare all" to reset all parameters to zero

+ Press "Run" to start the pulling operation of the device

Figure 3 26: Set Initial Parameters to Zero for Survey

+ After the pulling device has completed, and it has stopped operating, select "Search report" (choose the sample to export), then click "Export excel"

Figure 3 27: Exporting data After the Excel file is generated→ save it to a USB drive using "Save As."

Study of the taguchi experimental analysis method

The Taguchi Method, developed by Dr Genichi Taguchi in the 1950s, is a statistical approach known as Design of Experiments (DOE) that focuses on optimizing product quality and manufacturing processes By identifying the optimal combination of input factors, this method effectively reduces variability and enhances performance, ultimately minimizing the system's sensitivity to variations.

The Taguchi Method is extensively utilized in engineering, manufacturing, and product development sectors This effective and simple approach focuses on enhancing product quality, minimizing costs, and boosting customer satisfaction through the optimization of product design and production processes.

Additionally, the Taguchi Method employs statistical techniques, such as Analysis of Variance (ANOVA), to analyze experimental data and determine optimal parameter settings

It emphasizes the importance of addressing quality during the design phase rather than relying solely on outcome testing or post-production adjustments

3.4.2 Advantages of the Taguchi method

The Taguchi Method employs Orthogonal Arrays (OA) to streamline measurement scales and experimental protocols, optimizing the experimental process By minimizing the number of necessary tests while maintaining accuracy, this approach significantly reduces costs and minimizes waste.

The Taguchi Method emphasizes minimizing variation in production processes and product quality by optimizing key influencing factors and determining their ideal levels This approach enhances reliability and stability in manufacturing, reduces defect rates, and ensures consistent high quality during mass production.

− Quality Design: Emphasizing quality design within the production process, the Taguchi Method goes beyond final quality control This minimizes production errors and defects, improving product performance and reliability

− Experimental Efficiency: The Taguchi Method uses a minimal number of experiments to identify crucial factors and optimize the production process This saves time and reduces experimental costs

− High Flexibility and Ease of Application: The Taguchi Method does not require in-depth statistical knowledge and can be easily applied in various manufacturing and engineering environments

− Environmental Variable Response: The Taguchi Method can flexibly respond to environmental variables, meaning it can handle process variations without affecting quality

The Taguchi Method provides substantial advantages, including time and resource savings, a strong emphasis on reducing variation, and user-friendliness It significantly enhances production efficiency and quality while increasing customer trust and satisfaction These benefits position the Taguchi Method as a superior alternative to conventional quality management and production optimization strategies.

3.4.3 Experimental design using the Taguchi method

The Taguchi Method employs orthogonal arrays to systematically organize experimental factors and their levels, facilitating an efficient exploration of the parameter space This approach minimizes the number of experiments required while still delivering adequate information to analyze the influence of various factors on quality characteristics.

To optimize experimental parameters using the Taguchi method, follow these 7 basic steps:

1 Identify Influencing Factors: Determine the factors you want to study in the experiment These factors can be variables or parameters that potentially impact the quality characteristics of the product or the research process

2 Determine Representative Levels: Once the factors are identified, determine the representative levels for each factor These are the values that represent the factor during the experiment For example, if you are studying cutting speed in machining, the representative levels might be 100 m/min, 200 m/min, and 300 m/min

3 Select Orthogonal Array: Look up the appropriate orthogonal array from tables or software tools The selection depends on the number of factors and representative levels chosen Orthogonal arrays are pre-designed to ensure that each combination of levels appears equally often This helps you collect data efficiently and determine the significance of each factor

4 Conduct Experiments and Collect Data: Perform the experiments according to the selected orthogonal array and collect data related to product or process quality

5 Analyze Data and Determine Optimal Values: Analyze the collected data using the signal-to-noise ratio (SNR) SNR is calculated from the data to determine the optimal value for each factor and level The goal is to achieve the best quality by optimizing the factors and levels

6 Array Analysis: This step involves using statistical methods to analyze the experimental data It helps identify the importance of each factor and their interactions regarding product

54 or process quality This provides a deeper understanding of how the factors affect the results

To ensure optimal results that align with your goals, it's essential to recalculate the objective function using the identified optimal parameter set or to verify these results through additional experimentation.

Table 3 3: Choosing Taguchi orthogonal array based on degrees of freedom [14]

Maximum Number of Factors by Number of

The Taguchi method employs the Signal-to-Noise Ratio (SNR or S/N) as a key statistical measure to assess the quality performance of products or processes This ratio quantifies the relationship between the desired signal, which reflects the average or target response, and the undesired noise, indicating the variations or deviations from that target.

Signal-to-Noise Ratio (SNR) is essential for evaluating the robustness and quality of a design or process by taking into account both the average value and its variability The goal is to maximize SNR, which signifies that the desired response (signal) outweighs the undesired variation (noise).

There are three types of SNR commonly used in the Taguchi method:

− The Smaller the Better: This type of SNR is used when a lower value of the response variable is desired In this case, SNR is calculated as follows:

(The objective is to optimize SNR, which means minimizing the average squared deviation.)

− The larger, the better: This type of SNR is used when a higher value of the response variable is desired In this case, SNR is calculated as follows:

(The goal is to optimize SNR, indicating that the average squared value is closer to the target squared value is better)

Evaluating the influence of various factors is essential when the response variable needs to closely align with the target value In such scenarios, the Signal-to-Noise Ratio (SNR) is calculated to assess this relationship effectively.

𝑛 − (𝑦 − 𝑦)(The goal is to optimize SNR, meaning to minimize the average squared deviation from the target)

Construction of experimental layout

Our group focuses on 3D metal printing utilizing non-pulse TIG welding as a bonding method for products While traditional TIG welding often relies on pulse technology to achieve optimal results, we have discovered that significant challenges arise without it or without adding extra metal to the weld Through extensive studies and experiments, we have identified several critical parameters that greatly influence the 3D printing process.

The TIG welding machine operates within a current range of 0 to 200 amps (A), where each temperature setting significantly impacts the 3D printing process and the quality of the weld joint, particularly affecting the tensile strength of the final product.

Welding current plays a vital role in metal 3D printing, as it is essential for generating the heat needed to melt the metal and effectively bond the layers By carefully adjusting the welding current, manufacturers can enhance the durability of the connections and improve the surface quality of the printed products.

Adjusting welding current is crucial and varies based on the type of metal and welding speed It is essential to set the welding current to attain the optimal temperature for melting the metal while minimizing excessive deformation.

Through the project experimentation, the welding current is suitable in the range from:

For optimal weld quality, maintain the welding current between 135 and 155 amps (A), as this range ensures effective bonding between layers Currents below 135 A can lead to inadequate melting temperatures and poor fusion, while exceeding 155 A may generate excessive heat, risking excessive melting or burn-through of the base metal during TIG welding.

Figure 3 29: Welding Current Affects the Tensile Strength of the Sample

In this project, welding speed is supported by CNC machines to ensure precision Welding speed is a crucial parameter in metal 3D printing

Excessive welding speed can hinder proper metal melting, resulting in weak and inconsistent bonds between layers This may lead to defects like voids, poor layer adhesion, and cracking Furthermore, a high welding speed can cause deformation, leading to uneven surfaces.

Slow welding speeds can negatively impact the metal 3D printing process by extending welding time and decreasing production efficiency Prolonged thermal exposure from slow welding can cause deformation and weaken the bond between metal layers, ultimately affecting the quality of the final product.

To ensure high quality and durability in welding, it is essential to adjust the welding speed according to the specific material and design Optimal welding speed creates strong, uniform bonds between metal layers, minimizes deformation, and guarantees the accuracy of the final product.

Based on our experimental findings, the suitable welding speed ranges from 30 to 50 mm/min Within this range, the welding bead ensures proper fusion and minimal porosity

When welding speed is excessively slow, it leads to a large welding bead and surrounding burnout, which negatively impacts weld quality Conversely, if the welding speed is too fast, the width of the weld bead decreases, undermining the fusion quality in 3D printing.

Figure 3 30: Welding Speed Affects the Tensile Strength of the Sample

Testing welding speeds under 30 mm/min can lead to overheating and rounding of the electrode tip, causing the arc to disperse and preventing it from remaining focused This lack of focus results in incomplete fusion of the metal layers, which adversely affects the bonding strength between the deposited layers.

Figure 3 31: Electrode Tip Overheating (Welding Speed Below 30 mm/min)

Testing the tensile strength of metal samples requires precise alignment of metal sheets, which are bonded using the heat generated from the non-filler TIG welding process The dimensions of the workpiece significantly influence the tensile strength; however, the primary determinants are the quality and efficiency of the welding process, the inherent properties of the materials used, and the overall design of the sample.

The dimensions of a workpiece can significantly affect the welding process and the resulting tensile strength of the sample Larger workpieces may pose challenges in generating enough heat to effectively melt and bond the sheets, potentially resulting in incomplete welds and defects like voids or scratches These issues can weaken the sample, ultimately decreasing its tensile strength.

Workpiece dimensions significantly influence heat distribution in the welding process Small dimensions can lead to localized heating around the weld area, resulting in deformation and cracking as the material cools This localized heating can also negatively impact the tensile strength of the welded sample.

Optimal workpiece dimensions: To achieve the best welding results and high-quality connections, the workpiece dimensions should be adjusted to suit the welding equipment and

59 design requirements Optimal workpiece dimensions ensure sufficient heat for effective melting and bonding of the metal sheets, while minimizing the risk of deformation and cracking during welding

Selecting the appropriate dimensions for workpieces in metal 3D printing is crucial for maximizing tensile strength Initially, a 1 mm size was tested, but challenges with fixture placement and overheating led to product warping Through testing, optimal sizes of 2 mm, 3 mm, 4 mm, 5 mm, and 6 mm were identified, where welds achieved complete fusion and significantly reduced the risk of warping.

Figure 3 32: Thickness of the workpieces

− The distance between two weld lines

The distance between two weld lines is defined as the measurement from the starting point of one weld bead to the starting point of the adjacent bead This distance can greatly influence the quality of metal connections and the mechanical properties of the weld, particularly due to variations in workpiece sizes.

CONDUCTING TENSILE TEST SAMPLE PREPARATION AND

Tensile test sample dimensions

The shape and size of a test sample are often constrained by the dimensions of the metal product from which it is derived Typically, samples are machined directly from the product, such as billets or castings However, products with uniform cross-sections, including shapes, bars, and wires, as well as cast samples from cast iron and non-ferrous alloys, can undergo testing without the need for machining.

The test sample can have various cross-sectional shapes, including square, circular, rectangular, or annular, and may also match the cross-sections of specific components in special cases For machined samples with varying dimensions, it is essential to include a transition radius between the gripping ends and the parallel section, as the dimensions of these transition radii are crucial for the sample's integrity.

The gripping ends can have any shape suitable for the grips of the testing machine The sample's axis must align with the load application axis

Test samples for tensile strength testing are chosen based on specific criteria outlined in the ASTM E8/E8M -13 standard This standard provides the essential test methods for tension testing of metallic materials, ensuring accurate and reliable results in assessing material strength.

Figure 4 1: Dimensions of the tensile test specimen

Other test samples may be used, such as those specified in relevant product standards or national standards, for example, ISO 3183 (API 5L), ISO 11960 (API 50T), ASTM A370, ASTM E8M, DIN 50125, IACS W2, and JIS Z2201

Table 4 1: Standard dimensions of the tensile test specimen [15]

G-Gauge length 200 mm 50 mm 25 mm

R-Radius of fillet (min) 25 mm 12.5 mm 6 mm

L-Overall length (min) 450 mm 200 mm 100 mm

A-Length of reduced (min) 225 mm 57 mm 32 mm

B-Length of grip section (min) 75 mm 50 mm 30 mm

C-Width of grip section approximate 50 mm 20 mm 10 mm

Based on Figure 4.1 to see the dimensional symbols of the tensile test sample, here are the notes and explanations for each symbol in Table 4.2

− G - Gauge length: Length of the sample for calculating the percentage of elongation

To measure elongation after fracture in a 40mm wide sample, markings should be made on the flat surface or edge within the parallel region (Region A) Users can choose to use either a set of 9 marks spaced 25mm apart or a pair of marks that are 200mm apart.

When there is no requirement to measure the elongation of a 40mm wide sample, the minimum length of the parallel region (Region A) is 75mm, with other dimensions unchanged

− W - Width: Width of the sample

− T - Thickness: Thickness of the sample

The T dimension refers to the thickness of a test sample, which is essential for product characterization For a test sample that is 40mm wide, the minimum thickness required is 5mm In contrast, the maximum allowable thickness for test samples measuring 12.5mm and 6mm in width is 19mm and 6mm, respectively.

− R - Radius of fillet: Radius of the transition fillet between Regions A and B

For samples with a width of 40mm, the minimum radius at each end of the parallel region (A) should be 13mm, which corresponds to the radius of the transition fillet between Regions A and B This specification is suitable for samples with a tensile strength of less than 690 MPa during the machining process.

− L - Overall length: Overall length of the sample

The sample grip should not exceed the transition region between Regions A and B

− A - Length of reduced section: Length of the parallel region

− B - Length of grip section: Length of the grip section

− C - Width of grip section approximate: Width of the grip section

To ensure accurate and reliable tensile test results, it is essential to carefully select the sample dimensions based on experimental conditions Understanding the tensile test sample standards is the first step, followed by a methodical approach to determine the appropriate dimensions for the test sample.

− Determine the applicable standard: First, identify the tensile test sample standard you want to use

− Understand the sample dimension requirements: Read the tensile test sample standard carefully to understand the sample size requirements The standard typically specifies length, width, and other dimensions

− Identify specific application requirements: Evaluate the specific requirements of the project For example: type of metal used, thickness, size and shape of the sample, testing objectives, etc

To ensure compliance with testing standards, it is essential to select an appropriate sample size based on the specified requirements Adhering to the guidelines for sample size and shape is crucial in the testing process to achieve accurate and reliable results.

Figure 4 2: 3D model of the tensile test specimen

Following the outlined steps to determine the dimensions of the tensile test sample according to the specified standard, the team established the workpiece dimensions based on the existing experimental conditions and valuable guidance from the instructor.

Conducting the tensile test sample printing process

Implementing the 5S methodology in the experimental environment is crucial before beginning any experimental process, as it fosters commitment from all team members and promotes ongoing adherence A clean, organized, and orderly workspace enhances productivity and efficiency while also ensuring safety and reducing risks throughout the experimental procedures.

− Step 1: Setting up the TIG welding machine

Figure 4 4: Setting up the TIG welding machine

To properly install a welding machine, begin by inspecting the equipment Next, connect the gas hose from the welding gas cylinder to the TIG welding machine, ensuring the welding gas cylinder hose is securely attached Then, attach the ground clamp to the positive polarity (+) terminal and connect the welding torch cable to the negative polarity (-) terminal of the TIG welding machine Afterward, open the gas cylinder valve to the specified level, connect the power to the machine, and you are ready to proceed with the experiment.

− Step 2: Welding the initial workpiece

The experiment utilizes CT3 steel blanks for testing, with variations in initial blank sizes being the key factor under examination.

Table 4 2: Dimensions of the initial experimental blank

To test tensile strength using the Taguchi method, it is essential to create a Taguchi table after identifying the relevant parameters and factors The team will then analyze the cases outlined in the Taguchi table and follow its specifications for conducting the tests.

Figure 4 5: Workpiece after fixing both ends by welding

Fixing the initial workpiece firmly is crucial because without proper fixation, the part can easily deform and fail to meet standards during subsequent machining steps

Figure 4 6: Workpiece after machining without fixing both ends by welding

− Step 3: Mounting the TIG welding machine on the 3-axis WAAM machine

After completing the above steps, continuing to mount the TIG welding machine onto the 3-axis WAAM machine is an essential process that must be done correctly for all setups

Figure 4 7: The simulation of welding paths on a 3-axis WAAM machine

− Step 4: Setting up the 3-axis WAAM machine and operating it

After completing the setup steps mentioned above, proceed to set up the CNC machine coordinates according to the machining sequence Below is the machining design created in Mastercam X5

Figure 4 8: Simulation of welding paths on a 3-axis WAAM machine

The next step is to adjust the variables using the data from the Taguchi table Following the welding process, each sample will be assigned a sequential number corresponding to its case, ensuring clarity among samples and guaranteeing accurate final results.

Figure 4 9: The blanks after welding experiments

Machining the tensile test sample

After printing all the tensile test specimens, the next step is surface treatment, which is crucial as it directly affects the tensile strength of the specimens

Various machining techniques, including milling, turning, and grinding, are employed based on their cost-effectiveness and time optimization The team focuses on methods that efficiently create flat surfaces, primarily utilizing rough machining to enhance both time efficiency and cost savings.

Figure 4 10: Surface machining using milling method

Since the 3D printed blanks have uneven surfaces on both sides, mounting them on the milling machine is challenging Uneven surfaces can significantly reduce machining efficiency, especially when milling

The team opted for the turning method after thorough research and experimentation, as it is the most efficient approach for saving time By simply adjusting the chuck to match the specimen's dimensions, operators can seamlessly process multiple details in batches.

Figure 4 11: Surface machining using turning method

Figure 4 12: Blanks after surface machining

4.3.2 Machining the tensile test sample profile

Processing the tensile test specimens according to the specified dimensions and standards is crucial to ensure accuracy and comparability of test results across different laboratories and research studies

The tensile test standard has been widely developed and accepted in the industry Adhering to this standard ensures consistency in test results and facilitates comparisons with previous research

The standard outlines precise specifications for specimen dimensions, shapes, and testing methods, facilitating straightforward comparison of test results with past studies Adhering to these standards promotes data sharing among the scientific and industrial communities, enhancing the accuracy and reliability of test outcomes and driving advancements in the field.

Section 5.1 has provided important insights into the tensile test standard Processing test specimens according to these dimensions and standards is a critical step towards achieving optimal test results

Figure 4 13: Deformation of the tensile test specimen

To achieve the desired shape, several options like milling, laser cutting, and wire electrical discharge machining (WEDM) were explored Although milling was initially considered, it proved to be less effective in productivity and precision Therefore, we chose WEDM as the optimal method for shaping this component.

Wire electrical discharge machining (WEDM) emerged in the late 1950s as an innovative technology capable of machining hard materials Although its initial precision was limited, hindering widespread use, significant advancements in WEDM technology over the past few decades have led to the development of modern machines that offer high efficiency and exceptional precision.

Wire electrical discharge machining (WEDM) offers exceptional accuracy and precision, utilizing a thin wire electrode to create intricate details with tolerances from a few micrometers to several tens of micrometers This advanced machining technique effectively cuts through tough materials, including hardened steel, titanium alloys, and 3D-printed substances, making it valuable for a wide range of applications across various industries.

One of the primary benefits of Wire Electrical Discharge Machining (WEDM) is the absence of direct contact between the cutting tool and the material, leading to exceptionally clean cut surfaces This lack of physical interaction minimizes deformation and oxidation, significantly reducing the necessity for additional finishing processes and ultimately saving valuable machining time.

WEDM utilizes electrical pulses to machine materials without direct physical contact, making it ideal for soft and delicate materials, including 3D-printed components that are susceptible to deformation This non-invasive approach ensures precision machining while preventing damage to sensitive materials.

Due to insufficient machining capabilities, our team opted for outsourcing Below are some images of the setup and machining of tensile test specimens using the wire cutting method

Figure 4 14: Wire cutting of the tensile test specimen

Table 4 3: Wire EDM machine specifications DK7755

Maximum machining speed_mm 2 /mi 120

Figure 4 15: Finished specimen after cutting

Conducting the tensile test

In this project, we utilize a hydraulic tensile testing machine from Ho Chi Minh City University of Technology and Education to assess the tensile strength of prepared samples The machine operates hydraulically to apply force, pulling the sample until it reaches its breaking point This process enables us to determine the maximum tensile force, indicating the highest tensile strength that the sample can endure.

The instructions for using the tensile testing machine are detailed in section 4.3.3, and it is essential to follow the steps correctly to achieve the best tensile test results

Figure 4 16: Sample for machine testing before main sample testing

Before conducting the primary tensile tests, we prepare a test sample to ensure the accuracy and stability of the machine We initiate the main testing process only after confirming the machine's performance.

Figure 4 18: Results obtained from tensile testing

After testing the machine with the test sample, we proceed to pull all the main samples to obtain results

Figure 4 19: All 25 experimental cases have been completed

A tensile sample is considered to meet standards when it satisfies two conditions: it meets the appearance standards and meets the tensile strength standards as shown in the tensile graph

Figure 4 20: The tensile sample must break in region A

During the test sample process, if any sample does not meet the standard, it will be necessary to remake the sample and conduct the test again

Figure 4 21: Defective tensile sample cases Regarding the 8 cases of defective tensile samples, they are divided into 2 main groups:

Improper clamping of tensile samples or inconsistent weld quality across the entire surface can lead to transverse failure in tensile testing.

− Tensile samples breaking transversely but not in region A (Case 2): The main cause of this defect is improper clamping, leading to the defect

Evaluation of the tensile test specimen graph

Figure 4 22: Key parameters of the tensile test specimen chart

From the tensile strength testing, the quality of the weld greatly affects the parameters on the graph as follows:

The yield point marks the transition of a material from the linear elastic stage to the nonlinear elastic stage, where it begins to deform non-proportionally under tensile force In the linear stage, the material exhibits elastic deformation that increases proportionally with applied force, but once the yield point is exceeded, this proportionality is lost, leading to nonlinear elastic behavior.

At the yield point, a significant alteration in the mechanical properties of a material occurs, indicating that it has surpassed its elastic limit Once the pressure is released, the material does not revert to its original shape, leading to permanent deformation This transition can happen either abruptly or gradually, depending on the material's unique characteristics.

Figure 4 23: Stress-strain curve for mild steel and low/high alloy steel [16]

Breaking point, or tensile strength, is a critical parameter in tensile strength testing of steel, representing the maximum tensile force a material can endure before failure This point on the stress-strain curve indicates the material's maximum load-bearing capacity, making it essential for assessing structural integrity.

During this stage, the tensile stress rises, leading the sample into a distinct nonlinear elastic phase before cracking occurs As loading persists, the tensile stress peaks and subsequently begins to decrease, typically indicated by a flattening or decline in the stress-strain curve.

Figure 4 24: Stress during material tension [16]

The maximum stress of the steel is calculated using the formula:

𝐴 (𝑁/𝑚𝑚 ) With ∙ 𝑃 : is the maximum force applied (N)

∙σ max : is the maximum stress (𝑁/𝑚𝑚 )

∙ 𝐴 : is the cross-sectional area of the sample (mm 2 )

The Elastic Modulus (E) is a crucial mechanical property that defines a material's elasticity, indicating how well it can stretch and revert to its original shape under pressure or tensile forces without experiencing permanent deformation.

Elongation is a vital metric for assessing the ductility of steel in tensile testing, expressed as a percentage It represents the degree of deformation a steel sample can withstand before failure during the tensile strength evaluation.

APPLICATION OF THE TAGUCHI METHOD TO SURVEY THE

Introduction to Minitab software

Minitab is a powerful statistical software widely used by data analysts, quality control experts, and researchers to analyze data, visualize it, and improve quality on various scales

When talking about Minitab, we cannot ignore the incredibly useful data analysis tasks that this software offers, such as:

Basic statistical analysis involves calculating descriptive statistics such as mean, standard deviation, and variance It includes hypothesis testing to validate assumptions derived from sample data Additionally, regression analysis is utilized to understand the relationship between a dependent variable and one or more independent variables Analysis of variance (ANOVA) is employed to compare the means across two or more groups, providing insights into data variability and group differences.

− Design of Experiments: Designing various experiments such as Orthogonal Array (OA), Latin Square, and Fractional Factorial Identifying factors that influence product quality or process efficiency

− Quality Analysis: Applying statistical tools and methods to control product and process quality Analyzing data on defects, errors, and other quality issues

− Data Exploration: Using data visualization techniques such as charts and tables to explore patterns and trends in data

Effective report generation involves crafting clear and concise reports and charts to showcase data analysis results This process includes exporting data into multiple formats such as Excel, PDF, or HTML, facilitating easy sharing of analysis outcomes with stakeholders.

Application of Taguchi method using Minitab software

Minitab provides numerous powerful tools to support the application of the Taguchi method, making it easy to perform steps in experimental design, data analysis, and product quality optimization

Steps to apply the Taguchi method using Minitab:

Steps to apply the Taguchi method using Minitab:

* Step 1: Identify the input parameters (Factors): Using Table 4.3, which is a univariate table, we identify the four input factors: A (Welding current), F (Machine speed), ∆ (Weld gap), t (Plate thickness)

* Step 2: Determine the levels of the factors (Levels): For the 4 factors, each factor will have 5 different levels

Figure 5 2: Setting up Taguchi table using Minitab

* Step 3: Select the experimental design (Design): Here, we choose the Orthogonal Array (OA) design With 4 input factors and 5 levels each, the software suggests the L25 orthogonal array (corresponding to 25 experiments)

* Step 4: Enter the input values (Input): Using the generated orthogonal array, enter the input values to complete the creation of the Taguchi parameter table

Step 5 involves creating the experiment matrix as part of the experiment design process Once the input values are entered, this matrix generates a comprehensive list of experiments to be conducted, along with the corresponding factor values for each experiment.

Figure 5 5: Creating Taguchi table using Minitab

Utilizing the Taguchi table from Figure 5.5, conduct the experiments according to the specified experiment matrix and record the results, focusing on the quality characteristics of each experiment This process specifically involves measuring the Stress (MPa) values that reflect the tensile strength.

Table 5 1: Tensile strength values of 25 cases

During the experiment, it is necessary to ensure that all samples are performed on the same machine and with the same strategy (See the appendix again)

Figure 5 6: Tensile strength chart of 25 samples

After obtaining the output data, we create an additional column in the Taguchi table representing the tensile strength value

Results obtained after the experiment

Use Minitab's data analysis tools to assess the impact of the input factors on product quality

Since we aim for higher tensile strength in the product, which indicates better product quality, the option chosen will be Larger is better.With the formula being:

The Signal-to-Noise Ratio (S/N) is a crucial parameter that assesses the level of impact from both internal and external factors on results It serves as a quantitative measure to evaluate how these influences affect overall outcomes.

- n: the number of samples in each case

- yi: the experimental result of the i-th trial

On the task bar choose Stat  DOE  Taguchi  Analyze Taguchi Design

Figure 5 8: Using Analyze Taguchi Design module

Figure 5 9: Selecting the option for analysis Table 5 2: Analyzing the experiment and processing results according to Taguchi

Figure 5 10: Results obtained from Analyze Taguchi Design module

Results obtained

After using Minitab to evaluate the data, we obtain the results showing the influence of each input value on the output data, depicted in tables and charts below

Figure 5 11: Response table for S/N ratios

Figure 5 12: Response table for average tensile strength values

Figure 5 13: Chart showing input factors’s influence on the tensile strength

The analysis of the graph indicates that plate thickness (t in mm) significantly influences the tensile strength of the component An increase in plate thickness from 2 mm to 3 mm results in a notable rise in average tensile strength from 334.6 MPa to 401 MPa However, at 4 mm, the tensile strength slightly decreases to 391.1 MPa The maximum average tensile strength of 413.7 MPa occurs at 5 mm, while a minor reduction to 411 MPa is observed at 6 mm Therefore, a plate thickness of 5 mm optimizes the average tensile strength.

Besides the factor Plate thickness t (mm), the remaining three factors also have a significant influence, ranked in ascending order of impact: Welding distance ∆ (mm), A (Welding current), F (Machine speed)

Figure 5 14: Chart showing input factors’s influence on the S/N ratio

In Figure 5.14, we observe four separate charts that depict the influence of experimental factors on the S/N ratio

The average values at the factor levels in Figure 6.10 are determined according to the formula :

𝑁) with S/N is S/N ratio of the cases with the factor levels that need to be calculated

Specifically, for example, factor A (A) at level 135, we will have the average value of the S/N ratio as :

(Perform similar calculations for the remaining factor levels)

Table 5 3: Analyzing, processing experimental results of factors affecting tensile strength

Predict the output results of the optimal parameter set

The primary objective is to identify the ideal combination of influencing factors to achieve maximum tensile strength, thereby ensuring the highest quality for the product.

− For parameter A (Welding current), level 5 corresponds to a value of 155

− For parameter F (Machine speed), level 2 corresponds to a value of 35

− For parameter ∆ (Weld gap), level 2 corresponds to a value of 1.5

− For parameter t (Plate thickness), level 4 corresponds to a value of 5

To summarize, we will predict the output result of this optimized parameter set

Figure 5 16: Using Predict Taguchi Results to predict optimal parameter set

With optimal parameter settings, we obtain the output results, specifically the Stress value (MPa) is 464.077, the S/N ratio is 53.4537

Figure 5 17: Predicting optimal tensile strength based on optimal parameter set

APPLYING ANN METHOD TO PROJECT RESULTS

Introduction to Matlab R2020b software

MATLAB, developed by MathWorks, is a powerful computational software and programming environment designed for matrix calculations and data visualization It offers a range of Toolbox libraries that enhance simulation capabilities, making complex computations easier compared to other programming languages.

MATLAB 2020b is a highly optimized software widely utilized in various fields, including education, making it a preferred choice for students Its compatibility with modern machines ensures it meets the diverse academic requirements of learners.

* The main applications of MATLAB software include:

− Numerical computation: Supports computations in various fields such as linear algebra, integration, differentiation, and more

− Signal and image processing: Provides tools for signal filtering, image processing, feature extraction, and classification

− Simulation and modeling: Allows users to build and simulate mathematical models and systems

− Data analysis and exploration: Handles data processing, statistical analysis, multivariate data analysis, and exploration

− Machine learning and artificial intelligence: Supports tasks such as building neural networks, data classification, and analysis

* The system and features of MATLAB include:

− MATLAB Language: A high-level programming language for numerical computation and application development

− MATLAB Working Environment: An interactive environment for exploration, design, and problem-solving

− Graphics Processing: Integration of charts for easy data visualization and tools for creating custom graphs

− Extensive Library of Computational Functions: Includes Fourier analysis, filtering, optimization, solving linear equations, statistics, integration, and ordinary differential equations

− MATLAB API: Development tools aimed at enhancing code quality maintenance and maximizing performance

− Tools for building applications with custom graphical interfaces and functionalities to simultaneously integrate MATLAB algorithms with external applications and other languages such as C++, Java, NET, and Microsoft Excel

− MATLAB applications in signal processing and communications

+ Image and video quality processing

+ Computational applications in finance, biology

+ Applications in testing, computation, and measurement

Artificial Neural Networks (ANN), commonly known as neural networks, are computational models designed to mimic the information processing of biological neural systems These networks consist of numerous interconnected neurons that collaborate through weighted connections to address specific challenges as a cohesive unit.

An artificial neural network is tailored for specific applications like pattern recognition and data classification through a learning process that utilizes training samples This learning process focuses on adjusting the weights of the connections between neurons to effectively minimize the error function.

There are three primary learning methods in machine learning: supervised learning, unsupervised learning, and reinforcement learning Among these, supervised learning is the most prevalent, with backpropagation recognized as a key technique within this approach.

ANN training

In the realm of Artificial Neural Networks (ANN), "training" refers to the essential process where the neural network fine-tunes the weights of connections between nodes This adjustment is based on training data, aiming to reduce the discrepancy between the actual outcomes and the network's predictions.

* The process of training an ANN includes the following steps:

− Data Preparation: Prepare training data, including input samples and their corresponding output

− Network Initialization: Define the neural network architecture and initialize parameters These can be randomly initialized or set using specific algorithms depending on the training method

− Algorithm Selection: MATLAB provides various training algorithms for ANNs such as backpropagation, Levenberg-Marquardt, Bayesian regularization, and others Choose an algorithm suitable for your problem and requirements

− Training the Network: This process is iterated over multiple epochs (iterations) until optimal results are achieved or stopping criteria are met

− Network Evaluation: After training completes, evaluate the network's performance

Training an ANN is a crucial process that requires technical expertise to achieve optimal performance It involves adjusting network parameters and variables to optimize prediction outcomes

− Step 1: Collecting Input and Output Results

After completing the sample tensile strength experiments and obtaining Stress-Strain diagrams, we use the Plotdigitizer software to extract input and output points from the diagrams

Sorting Input and Output Data Using Excel

The input data consists of 5 variables: current intensity A, welding speed V, welding distance Denta, blank thickness t_phôi, and strain values obtained from experiments

Output data: stress values obtained from experiments

Figure 6 5: Using Excel to compile data

− Step 2: Input Data into Matlab

To train the Artificial Neural Network (ANN) using Matlab 2020b, ensure that all data is thoroughly prepared It is crucial to copy the data accurately to prevent errors that may result in inaccurate prediction outcomes.

Figure 6 6: Importing data into Matlab 2020b

− Step 3: Selecting Conditions for Training the ANN

To initialize the ANN training module, enter the command "nftool" into the Command Window

Figure 6 7: Initializing Neural Fitting module (nftool)

Figure 6 8: Setting conditions for training ANN network

− Step 4: Selecting the ANN Training Algorithm

Choose the algorithm for training The Levenberg-Marquardt algorithm is commonly used After selecting the algorithm, proceed to press 'Train' for Matlab to train the ANN

After completing the training process of the ANN, click on 'Plot Regression' to obtain the results

Figure 6 10: Results of ANN network training

After training, check the value of R; if R is less than 0.96 or negative, click 'Retrain' until

Once R achieves the desired accuracy, click 'Next' until you reach the 'Save Results' step

Once the Artificial Neural Network (ANN) training is complete, it can be utilized to tackle various challenges, including predicting optimal parameters and evaluating the influence of different factors on the Stress-Strain diagram.

Figure 6 11: Evaluating ANN training results

Use ANN to predict the output when changing the value of the input variable

The research team demonstrated the effectiveness of the artificial neural network (ANN) by presenting 25 comparison charts that illustrate the alignment between the experimental results and the ANN's predictions.

We can refer to all 25 cases in the appendix:

From the comparison results between the experimental data and the ANN predicted data, the research team concluded that:

The data predicted by the ANN matches the data trained by the research team

Application of ANN to evaluate the influence of input factors

After training the artificial neural network (ANN), we utilize it to assess how various input factors influence tensile strength based on the ANN's predictive outcomes This process is known as feature importance analysis or sensitivity analysis.

The team employs the Average Importance method to assess the impact on tensile strength, which calculates the average absolute value of remaining factors when a specific factor is considered This approach effectively highlights the most significant influences on tensile strength as determined by the Artificial Neural Network (ANN).

− Step 1: After training the ANN, proceed to select the input factors based on the

Figure 6 12: Obtain input values using the Average Importance method

− Step 2: Create an Excel file of the input factors

Figure 6 13: Create an Excel file with the relevant factors

− Step 3: Import the data into Matlab with the pre-trained ANN, then enter the command 'network(A1)' in the Command Window to start the ANN predictions

Figure 6 14: Steps needed to prepare for ANN to predict results

− Step 4: Press Enter for the ANN to predict the results After obtaining the predicted results from the trained ANN

Figure 6 15: Prepare steps for ANN to predict results

− Step 5: Once you have the Stress-Strain column, use Excel to create a chart showing the impact of that factor

Figure 6 16: Obtain predictions from ANN

Figure 6 17: 4 charts predicting the influence of input parameters using ANN

6.3.1 The impact of Ampe on the predicted tensile strength

Figure 6 18: ANN prediction of the influence of ampere parameters

The current (ampere) is a crucial factor in the TIG welding process for creating tensile test samples without filler wire Achieving the desired tensile strength involves not only the ampere setting but also various other influencing factors.

In a controlled laboratory setting, we can utilize an Artificial Neural Network (ANN) to accurately predict variations in the ampere parameter while keeping other parameters constant.

Choose the smallest possible survey level to observe the shift in the ampere parameter graph according to the ANN's prediction

Therefore, we sequentially select the following ampe parameters: 141, 142, 143, 144, 145

Looking at the graph, we can make the following observations:

− According to the ANN prediction, all 5 cases of failure fall within the strain range of 30% to 50%

− The graph shows a trend of decreasing tensile strength as the ampere value increases

− The average maximum tensile strength of the 5 cases is 𝜎 = 405.7 (𝑀𝑝𝑎)

6.3.2 The impact of Welding Voltage (V_han) on the predicted tensile strength

Figure 6 19: ANN prediction of the influence of the V_welding parameter

The welding speed (V_han) significantly impacts the TIG welding process when creating tensile test samples without filler wire Achieving the desired tensile strength depends not only on the welding speed but also on various other influencing factors.

In a controlled laboratory setting, we can utilize artificial neural networks (ANN) to accurately predict variations in welding speed while keeping other parameters constant.

Choose the smallest possible survey level to observe the shift in the welding speed parameter graph according to the ANN's prediction

Therefore, we sequentially select the following welding speed parameters: 36, 38, 40, 42,

Looking at the graph, we can make the following observations:

− According to the ANN prediction, all 5 cases of failure fall within the strain range of 30% to 40%

− The graph shows a trend of increasing tensile strength as the welding speed (V_hàn) increases

− The average maximum tensile strength of the 5 cases is 𝜎 = 384.44 (𝑀𝑝𝑎)

6.3.3 The impact of Welding Depth (Denta) on the predicted tensile strength

Figure 6 20: ANN prediction of the influence of Denta parameters

Delta (Denta) refers to the distance between two welds and is a crucial factor in the TIG welding process without filler wire, particularly for creating tensile test samples Achieving optimal tensile strength relies not only on the Delta parameter but also on various other influencing factors.

In a controlled laboratory setting, we can utilize artificial neural networks (ANN) to accurately forecast changes in the Delta parameter while keeping other parameters constant.

Choose the smallest possible survey level to observe the shift in the Delta parameter graph according to the ANN's prediction

Therefore, we sequentially select the following Delta parameters: 3.7, 3.9, 4.1, 4.4, 4.6 Looking at the graph, we can make the following observations:

− According to the ANN prediction, all 5 cases of failure fall within the strain range of 30% to 40%

− The graph shows a trend of increasing tensile strength as the Delta (Denta) value decreases

− The average maximum tensile strength of the 5 cases is 𝜎 = 384.44 (𝑀𝑝)

6.3.4 The impact of Workpiece Thickness on the predicted tensile strength

Figure 6 21: ANN prediction of the influence of t_phoi parameter

The thickness of the base material (T_phoi) significantly impacts the TIG welding process without filler wire when producing tensile test samples Achieving the desired strength relies not only on the ampere setting but also on various other factors that influence tensile strength.

To accurately predict the T_phoi parameter, we will consider a controlled laboratory setting, allowing the Artificial Neural Network (ANN) to forecast variations in the T_phoi parameter while keeping other parameters constant.

Choose the smallest possible survey level to observe the shift in the T_phôi parameter graph according to the ANN's prediction

Therefore, we sequentially select the following T_phoi parameters: 1.4, 1.5, 1.52, 1.54, 1.56

Looking at the graph, we can make the following observations:

− According to the ANN prediction, all 5 cases of failure fall within the strain range of 30% to 40%

− The graph shows a trend of decreasing tensile strength as the T_phôi value increases

− The average maximum tensile strength of the 5 cases is 𝜎 = 366.69 (𝑀𝑝𝑎)

Application of ANN to find the optimal parameter set

After training the Artificial Neural Network (ANN), we can predict the impact of input parameters on the tensile strength of test samples Additionally, the ANN can be utilized to identify the optimal parameter set influencing tensile strength using Matlab's Genetic Algorithm module.

Genetic Algorithms (GA) are popular optimization techniques employed to tackle diverse parameter optimization challenges MATLAB offers a robust toolkit for implementing GA, streamlining essential processes such as crossover, mutation, and natural selection.

− Bước 1: Step 1: In the Command Window, enter the optimization function"GA=@(x)- sim(network,x')", then press Enter for Matlab to recognize it

Figure 6 22: Define the Objective function

− Step 2: Initialize the Genetic Algorithm module

Figure 6 23: Initialize the Genetic Algorithm

− Step 3: Enter the necessary values to set up the Genetic Algorithm, then press Start for

GA to generate the optimal parameter set from the ANN

Figure 6 24: Optimal parameters from ANN - GA

− Step 4: After obtaining the optimal parameter set from ANN-GA, consolidate the input data in Excel

Figure 6 25: Consolidate optimal variable parameters from ANN - GA

The application of the Taguchi method and experiments helps streamline the experimental process, enabling us to quickly achieve desired results based on experimental predictions To

118 ensure these results are accurate, it's necessary to re-implement the optimized parameters predicted by Taguchi for confirmation

Figure 6 26: Comparison chart of Taguchi experimental results and GA predicted results

The use of Artificial Neural Networks (ANN) to predict optimal parameters from Genetic Algorithm (GA) modules is essential for minimizing the workload in future research projects While these predictive models offer valuable insights, it is important to recognize that the predictions may not always be entirely accurate, and should not be considered definitive optimal results To ensure the reliability of the ANN-optimized parameters, experimental validation based on the identified parameters is necessary.

The comparison chart presents results from two methodologies: the Taguchi method, based on experimental data, and predictions from Artificial Neural Networks (ANN) It's important to note that the accuracy of the ANN predictions may be limited, as this tool is relatively new and requires further research for comprehensive understanding Due to time constraints in the project, we could not conduct additional experiments to verify these findings Consequently, the results should be considered as reference points rather than a basis for developing new models Table 6.1 displays the optimized parameter sets obtained from both the Taguchi method and the ANN approach.

After conducting the ANN network training and utilizing Taguchi software to create an optimal parameter table for the durability of test samples, the team proceeded to re-weld two samples using the specified parameters Subsequently, they assessed the appearance of the products to confirm the accuracy of the prediction process and obtain final results.

Figure 6 27: The product compares Taguchi experimental results and GA prediction results

The results from the tensile test specimens, as shown in the images, indicate that both the ANN prediction parameters (right) and the Taguchi experiment parameters (left) produced generally good outcomes without significant defects affecting durability However, the Taguchi sample exhibited slight burning due to overheating during the welding process, which marginally impacted its durability Overall, both parameter sets detailed in Table 6.1 successfully achieved the desired results in the creation of tensile test specimens.

This research project investigates the tensile strength of samples welded using the TIG welding method without filler wire, employing the Taguchi method for analysis The experiments and calculations conducted throughout the study have produced significant outcomes that highlight the effectiveness of this welding technique.

1 Explored the basic characteristics of the TIG welding process without filler wire and identified factors influencing the welding process

2 Applied the Taguchi method to determine the impact of input parameters: current intensity (A), welding speed (V_hàn), distance between weld lines (Denta), and base material thickness (t_phôi) on the tensile strength quality in TIG welding without filler wire

3 Constructed graphs illustrating the influence of input parameters on the tensile strength of test samples using the Taguchi method

4 Utilized ANN (Artificial Neural Network) to develop graphs depicting the influence of input parameters on the tensile strength of test samples

5 Identified two sets of optimized parameters using both the Taguchi experimental method and ANN prediction method

1 Continuing the application of the Taguchi method to optimize other parameters in the metal 3D printing process to enrich scientific research literature

2 Repeating experiments using the two optimized parameter sets to determine the accuracy of the two different methods

3 Applying the results and research methods from the project into practical applications

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Choosing the optimal element size is crucial for achieving accurate Finite Element Analysis (FEA) results while minimizing the complexity of the finite element model This balance enhances the efficiency of simulations and ensures reliable outcomes in engineering applications For more insights, refer to Yucheng Liu's research on this topic, available at ResearchGate.

Metal 3D printing, casting, and CNC machining each have distinct advantages and applications in manufacturing Metal 3D printing offers design flexibility and reduced waste, making it ideal for complex geometries In contrast, casting is a cost-effective solution for mass production, providing excellent surface finishes and mechanical properties CNC machining, a subtractive process, allows for high precision and repeatability, suitable for tight tolerances When choosing the best method, consider factors such as production volume, material properties, and design complexity to ensure optimal results.

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Md Musthak's study focuses on predicting the effects of process parameters on the microstructure of TIG welded mild steel sheets, utilizing the Taguchi method for analysis The research emphasizes the importance of understanding how various welding conditions influence the quality and characteristics of the weld By employing the Taguchi method, the study aims to optimize welding parameters to enhance the overall performance and durability of welded structures For more details, refer to the schematic diagram provided in the article.

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Jianfeng Wang's study on "Finite-Element Analysis of Underwater Wet Welding" explores the implementation of bubble configuration in the welding process The research highlights the significance of bubble dynamics in optimizing the weld pool zone, providing valuable insights into the mechanics of underwater welding For detailed visual representation, refer to the schematic illustrating the bubble-arc-droplet interaction in the weld pool For more information, visit the link: https://www.researchgate.net/figure/Schematic-of-the-bubble-arc-droplet-weld-pool-zone-in-underwater-wet-welding_fig1_369378813.

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The tip angle of tungsten electrodes in the GTAW process significantly influences weld characteristics such as bead width, arc stability, and penetration A sharper electrode with a narrow angle produces a wider weld bead and improved arc stability but results in less penetration and a shorter electrode lifespan Conversely, a blunter electrode with a wider angle allows for better penetration and a longer electrode life, albeit with a narrower bead and more difficulty in arc striking Additionally, proper grinding techniques are crucial; electrodes should be ground longitudinally to maintain arc stability and prolong electrode life, as crosswise grinding disrupts electron flow and can lead to overheating and premature wear.

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The study by Chacún et al (2017) investigates the additive manufacturing of PLA structures through fused deposition modeling, focusing on how various process parameters influence mechanical properties The research aims to identify optimal selections of these parameters to enhance the performance of PLA materials in manufacturing applications.

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