Experimental investigation of efficiency of machining performance for difficult to cut materials under different cutting conditions

Introduction

Motivation of the study

The development of advanced engineering materials has led to the creation of difficult-to-cut materials designed to meet the specific demands of various industries However, machining these challenging materials poses significant concerns for scientists and engineers, highlighting the need for specialized techniques and knowledge As a result, the machining of difficult-to-cut materials has garnered international research interest, with studies dedicated to improving process efficiency and effectiveness This research plays a crucial role in helping scientists and engineers better understand and optimize the machining of these complex materials, ultimately advancing manufacturing technologies.

Dry machining offers an economical and eco-friendly cutting solution, but it faces challenges when working with difficult-to-cut materials These limitations can result in poorer surface quality and increased cutting temperatures, which ultimately reduce tool life.

Using flood-form cutting fluids effectively reduces the temperature in the cutting zone and enhances the durability of cutting tools However, excessive use of these fluids leads to higher operational costs and poses environmental challenges, as well as health risks for machine operators.

A new cooling technique called Minimum Quantity Lubrication (MQL) has been developed to replace traditional flood cooling methods, which pose environmental and health concerns MQL involves delivering very small amounts of cutting fluids (50-500 mL/h) as a mist under high air pressure (2-6 bar) directly to the cutting zone, making it an eco-friendly and cost-effective solution This technique uses biodegradable and environmentally friendly fluids, making it increasingly popular in the manufacturing industry Numerous studies, such as those by Q.C Hsu and T.V Do, have optimized MQL conditions for improving surface roughness when machining materials like AISI H13 using various lubricants, including vegetable oils and water-soluble oils Additionally, research by Sheng Qin et al indicates that MQL can reduce cutting forces and extend tool life when machining with coated tools like Al2O3/TiAlN.

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16-19] have also studied MQL to highlight its superiority when compared to both dry and flooded lubrication conditions

To further enhance the effective thermal conductivity of cutting fluid, a technological breakthrough was made by the use of miniscule solids particles (less than

In 1995, nanofluids or nanolubrication were introduced by adding nanoparticles, such as 100 nm particles, to cutting fluids and lubricants, with Choi as the originator Since then, extensive research has focused on applying nanoparticles to achieve minimum quantity lubrication (MQL) machining techniques The development of nanoparticle-based cutting fluids addresses the limitations of traditional lubes by increasing thermal conductivity, density, and viscosity, which significantly improves heat transfer in the cutting zone These advancements enhance machining efficiency and align with previous studies emphasizing the benefits of nanofluids in thermal management during machining processes.

Adding nanoparticles to cutting fluids significantly enhances their properties, leading to reduced cutting forces, improved surface quality, and increased tool life Sharma et al demonstrated that Al2O3 nanofluid under MQL reduced cutting force and tool wear, while decreasing surface roughness by up to 38.97% Similar improvements have been reported by Sayuti et al., Hadi et al., and Vasu et al., confirming the benefits of nanofluids in machining Sharma et al also explored hybrid nanoparticle-enriched cutting fluids, showing that such combinations further reduced tool flank wear, temperature, surface roughness, and cutting force by up to 20.28% with enhanced tribological performance Additionally, Wang et al found that nanofluids improved heat transfer and lubrication performance in MQL applications, with specific nanomaterials like ZrO2, CNTs, MoS2, and SiO2 exhibiting superior surface morphology and lubrication properties.

Adding Al2O3 nanoparticles to lubrication has been shown to be highly beneficial, improving lubrication performance during machining processes However, research specifically focusing on Al2O3 nano-lubrication remains limited, highlighting the need for further studies using nanofluids in Minimum Quantity Lubrication (MQL) Nanofluids-assisted MQL is emerging as a high-grade machining technique, gaining acceptance among modern manufacturing industries due to its superior characteristics such as reduced friction and improved tool life Increased research in this area is essential to establish the full potential and advantages of nano-lubrication in enhancing overall machining efficiency.

Reducing energy consumption in modern manufacturing is crucial due to the finite nature of global energy sources and the environmental impact of high energy use In machining processes, energy efficiency can be improved through two main strategies: machine enhancements and process parameter optimization While machine upgrades may not always be feasible, optimizing process parameters offers a cost-effective and less labor-intensive solution to minimize energy consumption Consequently, many researchers focus on process parameter optimization as a practical approach to enhance energy efficiency in manufacturing.

Research on modeling and optimizing process parameters for energy savings in metal processing is crucial for advancing manufacturing efficiency Response Surface Methodology (RSM) and Kriging models are widely used to analyze the relationship between input parameters and technological responses, with RSM being the most popular due to its flexibility and reliability The Kriging model is particularly effective for capturing highly nonlinear relationships between process variables and responses Therefore, this study employs both RSM and Kriging models to accurately model and optimize process parameters for energy efficiency in metal manufacturing.

To solve the optimization problem, several methods are commonly used in engineering Some general optimization techniques are employed in metal cutting

In multi-response optimization, techniques that focus solely on the level of design variables often cannot achieve truly optimal results To address this, the desirability function approach is widely used in practice for multi-objective optimization, such as in the study by Mia and colleagues, who optimized cutting conditions—including dry, wet, cryogenic cooling, and MQL—using this method Recently, researchers have increasingly adopted evolutionary algorithms like AMGA, genetic algorithms (GA), PSO, and NSGA-II due to their efficiency, flexibility, and effectiveness in solving complex multi-objective problems.

New bio-inspired multi-optimization techniques, such as NSGA-II, particle swarm optimization (PSO), and hybrid algorithms, are considered robust tools for solving complex optimization problems Unlike gradient-based methods, genetic algorithms (GA) and PSO, which are rooted in biological inspiration, are capable of locating the global optimum rather than getting trapped in local optima As a result, many researchers prefer using evolutionary optimization algorithms for their effectiveness and reliability in finding optimal solutions.

Overcoming machining challenges associated with difficult-to-cut materials remains a significant obstacle in manufacturing Utilizing nanofluids and Minimum Quantity Lubrication (MQL) techniques can enhance machinability and improve process outcomes Multi-objective optimization plays a crucial role in selecting optimal cutting conditions to reduce energy consumption, increase productivity, and ensure high-quality surface finishes Focusing on specific materials and machining cases allows for tailored solutions that improve key technical parameters such as surface roughness, cutting force, temperature, material removal rate (MRR), and energy efficiency Implementing these strategies promotes longer tool life, better product quality, cost reductions, and advances toward environmentally sustainable and clean manufacturing processes.

Objective of the study

This research aims to improve the machinability of difficult-to-cut materials by examining their machining under various cutting conditions, including dry, Minimum Quantity Lubrication (MQL), and nanofluids, while varying cutting parameters The study provides insights into selecting optimized input and output parameters and offers a significant performance comparison without relying on complex optimization algorithms Additionally, it contributes to promoting eco-friendly and sustainable manufacturing practices, supporting green manufacturing initiatives The results demonstrate enhanced machinability and process efficiency, advancing sustainable manufacturing technologies for challenging materials.

1 Understand deeply about machining difficult-to-cut materials

2 Investigate the machining parameters and conditions under dry, MQL, and nanofluid MQL conditions

3 Optimization of cutting conditions and cutting parameters in machining difficult-to-cut materials

4 Applying innovative optimization algorithms to optimize muti-objectives of response parameters when machining difficult-to-cut materials

5 Assisting engineers, machine operators to choose suitable cutting conditions for each case when machining difficult-to-cut materials

6 Help scientists and engineers have a better overview of specific cutting energy as well as power consumption when machining difficult-to-cut materials

7 This study is a significant contribution to the environmentally-conscious machining.

Scope of the study

This study focuses on challenging-to-cut materials such as hardened steels and super-alloys, which are known for their high hardness and durability Due to budget and time constraints, the research narrows its scope to these two materials, highlighting their significance in advanced manufacturing Hardened steels and super-alloys are widely used in critical industries like aerospace, automotive, and tooling, making their machinability a vital area of study Understanding the cutting characteristics of these materials can lead to improved machining techniques and cost-effective solutions.

This research investigated the machining of challenging-to-cut materials under three cutting conditions: dry, Minimum Quantity Lubrication (MQL), and nanofluids Flood cutting (wet cutting) was intentionally excluded due to its associated disadvantages, which are detailed in the study.

This study focuses on milling, a widely used machining process in the manufacturing industry The research employs end milling cutters with diameters of 10 mm and 16 mm from renowned cutting tool companies CMTEC (Taiwan) and Sandvik (Sweden), respectively Slot milling, a common and essential technique in milling operations, is also utilized to enhance the study’s applications and outcomes.

This study focuses on key machining input parameters that influence machining performance, including cutting fluids, workpieces, machining conditions, and cutting tools, while other process parameters are fixed The primary performance metrics examined are surface quality, cutting force, cutting temperature, specific cutting energy, power consumption, material removal rate, and tool wear, all of which critically reflect the output quality of the milling process Optimizing these parameters is essential for achieving improved machining efficiency and quality.

Organization of the dissertation

The dissertation consists of 5 chapters The composition of the chapters in this dissertation is presented in Figure 1.1

In chapter 1 is an introduction to expressing motivation, objective, scope of research, and structure of the dissertation

In chapter 2, a brief background of the study is discussed The content covered in this chapter is machining difficult-to-cut materials, cooling and cutting fluids methods, and literature review

Chapter 3 exhibits the research method that was adopted in the dissertation The first section describes the design of experiment (DOE) utilized in this study The second part deals with the approximation models to render the relationship between the input and output variables of the dissertation The last section presents the optimization techniques used to find the optimal solution in each particular study

Chapter 4 presents four studies on difficult-to-cut materials The first study with the content is multi-objective optimization for surface roughness and cutting force under dry cutting conditions when milling on AISI H13 steel with 10 diameter cutting tool of the CMTEC company In the second study, also AISI H13 material is machined under three different cutting conditions: dry, MQL, nanofluids, various cutting speed, feed per tooth, and depth of cut By optimizing three process responses (surface roughness, cutting temperature, and cutting force), this study determines the solution for optimal cutting parameters and cooling conditions In the third study, multi-objective optimization for AISI H13 in terms of productivity, quality, and power consumption under nanofluid condition In the final research, the Inconel-800 superalloy is employed To increase the machinability of the difficult material, nanofluids are adopted A revolutionary optimization algorithm is employed to gain the best global solution for surface roughness, energy, and material removal rate

In chapter 5, the last chapter of the dissertation, summarizing conclusions drawn from researches and future work is also mentioned

Background

Machining difficult-to-cut materials

Materials science is a vital area of research focused on developing innovative materials with exceptional properties These materials, known as difficult-to-cut materials, include hardened steels, titanium alloys, super-alloys, metal matrix composites, and ceramics, recognized for their superior mechanical and metallurgical characteristics They are essential across various industries due to their high strength-to-weight ratio, stiffness, toughness, thermal conductivity, heat capacity, resistance to corrosion and oxidation, and fatigue resistance This study specifically concentrates on two challenging materials—hardened steel and super-alloys—highlighting their significance in modern manufacturing and engineering applications.

2.1.1.1 Hardened Steels Hardened steels also are known as ferrous alloys that comprise various alloying elements Hardened steels achieved status based on the heat treatment process that makes hardened steels have unique mechanical and metallurgical characteristics

Carbon content in steel significantly influences its tensile and yield strength, with increased carbon levels leading to higher strength as shown in Figure 2.1 [56] The addition of alloying elements, such as manganese, further enhances steel properties by improving ductility and making it more suitable for metal forming applications Hardened steel, primarily composed of iron, typically contains 0.15–0.2% carbon along with small amounts of other alloying elements to achieve desired mechanical characteristics.

Hardened steel is produced through various heat treatments, including thermal treatment, cryogenic treatment, and surface hardening Thermal and cryogenic treatments are typically applied to medium to high alloyed steels to enhance their hardness and durability Surface hardening is primarily used on low alloy steels to harden the outer layer while maintaining a soft core, providing a balance of toughness and surface resistance.

Heat-treated hardened steel exhibits unique properties such as high hardness, low ductility (brittleness), an increased hardness to E-modulus ratio, and heightened corrosion sensitivity, distinguishing it from conventional steel These inherent characteristics are especially significant for researchers exploring hard machining techniques and applications Additionally, the effect of carbon content on the typical properties of carbon steel plays a crucial role in determining its performance after heat treatment, influencing factors like hardness and wear resistance.

2.1.1.2 Superalloys Superalloys have an important role in the modern industry These superalloys can maintain their mechanical properties when the lengthened appearance to high temperatures in a long time This material was developed for specialized industries such as turbo-superchargers, aircraft turbine engines, gas turbines, rocket engines, petroleum refineries, etc Superalloys are divided into three main categories based on the

This article discusses the concentration of metals in various alloy compositions, focusing on Iron-based, Nickel-based, and Cobalt-based superalloys (see Figure 2.2) It emphasizes the importance of understanding metal content for enhancing alloy performance and reliability in demanding applications The analysis covers different material types, highlighting their unique properties and suitability for specific industrial uses Proper composition management is crucial for optimizing the mechanical strength and corrosion resistance of superalloys.

Superalloys are distinguished by exceptional properties such as high fatigue resistance, excellent high-temperature and creep resistance, and the ability to maintain their chemical and mechanical integrity under elevated temperatures, making them highly resistant to corrosion However, their poor thermal conductivity, high hot hardness and strength, tendency to form build-up edges (BUE), and chemical reactivity with cutting tools pose significant challenges during machining To address these machining difficulties, specialized support tools and techniques are essential to improve the machinability of superalloys.

The evolution of super-alloys has been driven by the industry’s specific requirements, with aerospace being the primary application accounting for 70% of usage Super-alloys are essential in manufacturing critical components such as fixtures and rotating parts in gas turbines and jet engines, where exceptional properties are vital Due to safety and reliability concerns, only super-alloys with superior performance characteristics are suitable for aerospace applications Other industries like medical, chemical, and structural each utilize super-alloys at 10%, highlighting their versatility and importance across sectors.

This study focuses on investigating Inconel 800, a high-grade nickel-based superalloy widely used in aerospace, industrial furnaces, the automotive industry, and marine applications Known for its excellent strength and corrosion resistance at elevated temperatures, Inconel 800 is a preferred material in demanding environments Its superior performance makes it an essential component in various high-performance industries, highlighting its significance in modern manufacturing and engineering.

Figure 2 3 Applications of superalloys materials in industry 2.1.2 Operations for machining difficult-to-cut materials

2.1.2.1 Hard turning Hard machining is a method of machining materials with a hardness of 40-70 HRC, using a tool with cutter blades that are geometrically-shaped defined [58, 59] Traditionally, the manufacturing processes of a product go through the steps of forming, annealing, rough turning, heat treatment, and grinding (Figure 2.4a) [60] However, due to going through too many machining sequences, grinding will consume a lot of time for set up and machining, leading to higher product costs (Figure 2.5) [55] Besides, using the grinding process uses a large number of emulsifiable cutting fluids These cutting fluids can harm the environment when disposing of them after use In today's technologies, the environmental issue is of primary concern to the manufacturing industry So hard turning (Figure 2.4b) is considered as an alternative to substitute the grinding process, which is time-consuming, costly, and highly damaging to the environment [61] One point to note is that the requirement of the hard machining method is that the system rigidity of hard turning must be ensured [62]

Figure 2 4 (a) Machining sequences of conventional manufacturing processes and (b) hard turning [60]

Selecting the appropriate cutting tool material is crucial for effective hard turning, as it ensures the tool's anti-abrasion properties, toughness, hot hardness, and stability of chemical and physical characteristics at high temperatures Additionally, the system used in hard turning must provide sufficient rigidity to achieve optimal machining performance.

Figure 2 5 Evaluation of the cost between (a) grinding and (b) hard turning [55] 2.1.2.2 Hard milling

Hard milling is a widely used technique in the manufacturing industry for hard machining, especially essential in mold and die manufacturing due to complex mold surface geometries that are difficult to finish through traditional methods Historically, electrical-discharge machining (EDM) was employed for mold and die machining, but it has significant limitations that hard milling with CAM-generated toolpath processing techniques can overcome Common milling operations in hard milling include shoulder, face, profile, groove or slot, and chamfer milling, with slot milling being the most prevalent A comprehensive hard milling system comprises a machine, cutting tools, and CAD/CAM software, with cutting tools—such as solid carbide endmills, indexable carbide inserts, and ceramic indexable inserts—playing a critical role in achieving precise and efficient machining outcomes.

Figure 2 6 Hard milling (Slot milling) [63]

Cooling and lubrication method

Green manufacturing is a global trend driven by climate change and increasing environmental waste It focuses on utilizing fewer natural resources, reducing pollution and waste, enhancing efficiency, and maintaining responsible emissions The primary goal of modern manufacturing is to minimize its environmental impact, promoting sustainable and eco-friendly industrial practices.

Controlling cutting temperature is a critical challenge in machining, particularly with difficult-to-cut materials Implementing effective cooling techniques is essential for managing heat generation, improving machinability, extending tool life, and increasing overall productivity These cooling methods can be categorized into conventional approaches and eco-friendly (green) manufacturing practices, as illustrated in Figure 2.7.

This article discusses seven cooling and lubrication techniques for metal cutting, highlighting traditional methods such as flood cooling, high-pressure coolant, mist cooling, and internal tool cooling However, these conventional approaches are increasingly viewed as environmentally harmful due to excessive coolant use In response, greener manufacturing alternatives like dry machining—also known as machining without cooling—and Minimum Quantity Lubrication (MQL) are gaining attention The focus of this study is on evaluating the effectiveness of dry machining and MQL as sustainable cooling techniques.

Dry machining is an environmentally friendly processing method that eliminates the need for cutting fluids or lubricants, thereby reducing product costs and minimizing pollution It is regarded as an ideal solution for the green industry due to its advantages, including no pollution, easy handling, recycling, and cleaner chip disposal The initial investment in equipment for lubrication systems adds to manufacturing costs, with cutting fluids accounting for approximately 7-17% of the finished product's total cost Additionally, dry machining helps prevent accidental issues related to cutting fluids or lubricants, enhancing overall safety and efficiency.

Figure 2 8 Advantages of dry machining [65]

The dry cutting method, developed in the 1990s, faced significant challenges due to heat generation during machining, which hampers metal cutting efficiency This heat problem greatly impacts the lifespan of cutting tools, especially when machining difficult-to-cut materials Additionally, dry machining often exhibits poor machinability, making it less effective for some applications As a result, the rational use or minimal application of cutting fluids and lubricants has become essential in modern industry to improve machining performance and tool longevity.

Minimum Quantity Lubrication (MQL) is an increasingly popular, eco-friendly, and cost-effective cooling and lubrication technique in the manufacturing industry, using only about 50-150 mL/hour of cutting fluid Often referred to as near-dry machining, MQL involves mixing a small amount of cutting fluid with high-pressure air to spray a fine mist directly onto the cutting zone This method is particularly suitable when dry machining is inefficient or impractical, especially for machining materials with unique mechanical properties or poor machinability, while aiming to enhance tool life and surface quality.

Optimizing cutting fluid in Minimum Quantity Lubrication (MQL) enhances heat removal from the cutting zone, effectively reducing tool wear MQL with lubrication also minimizes friction in the cutting area, leading to lower cutting forces and energy consumption Additionally, employing high-pressure cutting fluid facilitates faster and smoother chip removal, preventing chips from scratching the surface finish and ensuring superior surface quality.

Figure 2 9 The cost in metal machining [66]

There are two main types of MQL delivery systems: external applications and internal applications (Figure 2.10) External applications include ejector nozzles and conventional nozzles, each operating based on distinct principles outlined in Figure 2.11 Internal applications feature two configurations—single-channel and dual-channel—both of which are explained through their respective principles in Figure 2.12.

Figure 2 11 The principles of ejector nozzle (1) and conventional nozzle (2) of external application [70]

Figure 2 12 The principles of single channel and dual channel in internal application

MQL (Minimum Quantity Lubrication) is a total loss cooling technique that employs cooling and lubrication to maintain cutting zones fresh and effective The choice of cutting fluid is crucial for MQL performance, with key characteristics such as fluid type, wetting ability, and viscosity significantly impacting lubrication and cooling efficiency Additionally, parameters like nozzle position, fluid flow rate, and air pressure play vital roles in optimizing MQL system performance and must be carefully adjusted In this study, only the commercial lubricating oil CT232 from Taiwan is used, with nozzle position, fluid flow, and air pressure kept constant to ensure consistent results.

Nanofluid MQL is a minimal quantity lubrication method that uses cutting fluids containing nanoparticles less than 100 nm in size, forming a homogeneous suspension The nanoparticles can be categorized into seven types, each with unique characteristics that influence lubrication performance when integrated into the fluid Key factors such as particle size, concentration, shape, and temperature significantly impact the nanofluid's viscosity, thermal conductivity, and convection heat transfer capabilities, enhancing lubrication efficiency [71][72].

Table 2 1 Classification of some nanoparticles using as addition into cutting fluid/lubrication

Carbon and derivatives of carbon Graphene, diamond, SWCNT, MWNTs

Metals Sn, Fe, Bi, Cu, Ag, Ti, Ni, Co, Pd, Au

Metal oxide ZrO2, TiO2, Fe3O4, Al2O3, ZnO, CuO

Sulfides WS2, CuS, MoS2, NiMoO2S2

Rare earth compounds LaF3, CeO2, La(OH)3, Y2O3, CeBO3

Nanocomposites Cu/SiO2, Cu/graphene oxide, Al2O3/SiO2, serpentine/La(OH)3, Al2O3/TiO2

Others CaCO3, ZnAl2O4, Zeolite, ZrP, SiO2, PTFE,

Nanofluids typically utilize base fluids such as water, ethylene glycol, and oil They are commonly synthesized using two methods: the one-step and two-step approaches, with the two-step method being the most widely adopted Schematic diagrams in Figure 2.13 illustrate two prevalent synthesis techniques within the two-step method: microwave-assisted synthesis (Figure 2.13a) and direct mixing technique (Figure 2.13b).

Figure 2 13 Two different ways to synthesize nanofluid with two-step method [75]

Using nanofluid Minimum Quantity Lubrication (MQLs) in machining enhances thermal conductivity, while increased viscosity and density of the nanoparticles also contribute to improved surface quality These nanoparticles support wear and friction reduction through four key mechanisms, aiding in smoother machining processes and better surface finishes.

(1) Tiny spherical nanoparticles which will probably roll between two frictional surfaces and convert the sliding friction into a coalition of rolling and sliding friction;

(2) Nanoparticles are prone to mesh with friction sets to generate a surface preventive layer;

(3) Nanoparticles settle down into the voids of the contact surfaces to create a physical tribo-layer that makes up for the loss of mass which is called “mending effect”;

(4) Large number of nanoparticles are capable to sustain compressive force evenly thereby reducing the compressive stress concentrations associated with high contact pressure

Figure 2 14 Four techniques of nanoparticles during machining under nanofluid MQL

Literature review

This study emphasizes the importance of reducing environmental waste while simultaneously enhancing energy efficiency in machining processes.

Figure 2 15 Sustainability assessment in metal cutting [77]

Increasing energy efficiency in machining is essential for reducing overall energy consumption and minimizing environmental impact Energy consumption in machining can be categorized into fixed energy, which depends solely on equipment specifications such as hydraulic, cooling, and control systems, and cannot be adjusted, and variable energy, which can be lowered through specific technologies and practices during operations like spindle use and material cutting Additionally, machine tool energy use varies across three operational states: basic, ready, and cutting, each impacting overall efficiency.

Figure 2 illustrates the 16 classifications of energy consumption in machining processes During machining, coolant is essential to dissipate heat generated by cutting operations Implementing nanofluid minimum quantity lubrication (MQL) offers an eco-friendly solution by significantly reducing fluid use, which helps in minimizing environmental impact, lowering cutting temperatures, and extending tool life MQL is increasingly important in modern metal cutting due to its benefits, replacing traditional lubrication techniques with a minimal fluid flow rate of 50-500 ml, thereby promoting sustainable manufacturing practices.

Optimizing MQL assisted milling conditions with vegetable oil synergy enhances specific cutting energy and surface finish, making it an effective approach for machining processes Bashir et al investigated the optimal pulse jet MQL flow rate for surface milling of hardened AISI 4140 steel at 40 HRC, highlighting the importance of precise fluid delivery Incorporating nanofluids with nanoparticles significantly improves cooling efficiency by doubling thermal conductivity, resulting in better surface quality, reduced cutting forces, lower friction, and increased tool wear resistance Despite these advancements, there is limited research on nanofluids MQL performance for machining hardened steels with varying hardness levels Consequently, focusing on hard milling with nanofluids MQL presents promising opportunities for enhancing machining efficiency and tool longevity.

As an environmentally sustainable manufacturing industry, minimizing cutting energy consumption is a crucial criterion Energy use related to machine tools can be categorized into three primary areas: machine tools themselves, spindles, and processing processes Focusing on these areas helps optimize energy efficiency and supports eco-friendly manufacturing practices.

Implementing energy savings at certain operational levels is often impractical, as energy reduction depends on specific factors at each stage At the process level, energy consumption is influenced by parameters such as cutting velocity (vc), feed per tooth (fz), depth of cut (ap), and material hardness However, selecting the optimal process parameters to enhance energy efficiency remains challenging for operators, highlighting the complexity of achieving significant energy savings in manufacturing processes.

Process parameter optimization for energy savings in metal cutting continues to attract significant research interest Studies have utilized advanced modeling and optimization techniques—such as artificial neural networks, particle swarm optimization, Kriging models, and multi-objective algorithms—to enhance energy efficiency and surface quality during machining For example, Jang et al employed ANN and PSO to optimize cutting parameters during milling of SM45C steel, reducing specific cutting energy Nguyen et al achieved a 34.8% reduction in energy consumption and a 28.8% improvement in power factor by combining RBF models with Grey relational analysis and desirability approaches Another study optimized multiple objectives—including lower specific cutting energy, better surface finish, and higher material removal rate—using Kriging and archive-based micro-genetic algorithms Additionally, Park et al combined response surface methodology with NSGA-II for multi-objective optimization in hard turning of AISI 4140 steel Selaimia et al modeled and optimized cutting conditions for maximizing material removal rate while minimizing surface roughness in dry machining of stainless steel Khan et al demonstrated a 20.7% reduction in energy consumption in nanofluid MQL-assisted face milling, highlighting the ongoing importance of process parameter optimization for energy efficiency in MQL machining processes.

Effective machining process optimization begins with modeling the relationship between process parameters and performance outputs Traditional methods such as Response Surface Methodology (RSM) and second-order polynomial models are commonly used due to their simplicity, as documented in various studies [4, 36, 37, 46, 89, 90] However, these models may not accurately capture highly nonlinear relationships between inputs and outputs To address this, advanced models like artificial neural networks (ANN) [4, 13, 91], Adaptive Neuro-Fuzzy Inference Systems (ANFIS) [92, 93], and Kriging models [48, 94] are employed, offering superior ability to model complex, nonlinear relationships in machining optimization.

Optimization techniques can be broadly categorized into conventional and advanced methods, each yielding significant results across various fields Traditional methods like the Taguchi approach focus on discrete control factors but do not guarantee a "true" optimal solution, as seen in studies where it determines factor levels affecting energy consumption Similarly, Grey Relational Analysis (GRA) identifies optimal factor levels but also lacks the ability to provide a genuine optimal solution In contrast, advanced optimization algorithms such as Particle Swarm Optimization (PSO), Non-dominated Sorting Genetic Algorithm (NSGA-II), and Archive-based Micro Genetic Algorithm (AMGA) offer comprehensive search capabilities, solving complex constrained and unconstrained problems for global optima For example, combining Artificial Neural Networks (ANN) with PSO enables the optimization of specific objectives like cutting energy, highlighting the superior performance of these advanced algorithms.

Recent studies have focused on optimizing dry milling to enhance machining energy efficiency, surface quality, and production rates For instance, research by AMGA demonstrates the effectiveness of process optimization in achieving better energy utilization and improved surface finishes Park et al employed hybrid Response Surface Methodology (RSM) combined with NSGA-II algorithms to identify optimal cutting parameters that maximize energy efficiency and minimize specific cutting energy These findings highlight ongoing interest in modeling and optimizing energy efficiency within nanofluid-based Minimum Quantity Lubrication (MQL) machining processes.

Recent literature highlights the importance of integrating hard milling, nanofluid Minimum Quantity Lubrication (MQL), and process optimization to improve machining performance Properly setting process parameters is crucial for enhancing both the quality and productivity of hard milling, yet remains a challenging practical and academic issue Additionally, holistic optimization of energy consumption, material removal rate, and surface quality has not been thoroughly addressed This study focuses on the multi-objective optimization of the milling process for hard-to-cut materials using nanofluids to achieve superior machining efficiency and performance.

Table 2 2 MQL condition order to obtain high productivity, quality, and efficiency of energy consumption using various algorithms

Taguchi WEDM Evaluate the impact of process parameters on errors of holes

The application of GRA couple with RSM can reduce wear, coefficient of friction, and frictional force

The results of Taguchi method are good agreement with Weibull analysis with a deviation of less than 2% in all the cases

GRA Milling GRA is a strong tool for multi-objective optimization in machining to optimize cutting parameters

Position errors in CNC machine

The improvements of R 2 for X, Y, Z axis are 1-2.6%, 1-14.3%, and 10.3- 71.1%, respectively

GA Robotics The application of GA optimization technique has improved higher results than previous studies

Hybrid social spider and GA optimization

Flexible job shop scheduling problem

Diminished 5.13%, 8.55%, 9.57%, and 9.74% when applying SSO in conjunction with GA for makespan time repeated SSO or the hybrid algorithm with ABC-GA, ABC, and

NSGA-II PMEDM The utilization of NSGA-II can be decided on the best optimal condition to obtain low surface roughness and high surface micro-hardness

The algorithm and functioning of fminsearch in the proposed PSO have

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PSO EDM Using PSO, the best optimal results to deposit the biomimetic HA-containing layer were determined

PSO EDM The application of PSO technique can increment the MRR and the quality of surface roughness to 14.89 and 15.94%

Xuanyu Liu and Kaiju Zhang [106]

LS-SVL combine with PSO

Significant increases in modeling efficiency and precision are overcome by applying the proposed method

The confidence interval of the proposed model (HPSOSA) is 95%.

Research Method

Approximate model function

For optimal process performance, techniques such as second-order polynomial models, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), Kriging, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are commonly used to model the influence of process parameters on performance criteria [4, 13, 91, 92, 93] In this study, we focus specifically on the application of Response Surface Methodology (RSM) and Kriging models to optimize process parameters and enhance performance outcomes.

RSM was presented in 1951 by two researchers named George E P Box and K

B Wilson RSM model is an integration of statistical and mathematical techniques that employs a second-degree polynomial model to modeling and optimizing [4, 37] In this model, the second-order is utilized to approximate the response as below:

      (3.1) where (c0) is the constant, and (ci), (cii), and (cij) are coefficients (Xi) and (Xj) are the variables (ɛ) is the random error of the experiment

RSM model is usually carried out through the following [46]:

Step 1 Define the input process and the output process

Step 3 Render the relationship with the mathematical model

Step 4 Analysis to find out the influence of the input variables on the response parameters

Step 5 Experiment to check the suitability of the model If not satisfied, proceed with the adjustment

Step 6 Decide to accept or reject the model

RSM is the most popular model for demonstrate the relationship between the machining parameters and technological responses in machining because of its simple for utilization [4, 36, 37, 89]

The Kriging model is a statistical interpolation technique pioneered by South African statistician and mining engineer Danie G Krige, a renowned figure in geostatistics Developed further by French mathematician Georges Matheron in 1960, it has various types, including simple Kriging, ordinary Kriging, universal Kriging, and blind Kriging In our study, we utilize ordinary Kriging, the most widely used variation, to effectively model the relationship between input and output variables.

Figure 3.3 illustrates one-dimensional data interpolation using the Kriging model, highlighting its advanced capabilities The red squares represent the initial data points, while the gray regions depict the bell curve confidence intervals, indicating the model’s uncertainty The dashed blue spline curve passes through the original points but does not align with the confidence intervals, unlike the red line, which consistently covers them This demonstrates the Kriging model’s superior interpolation accuracy and its ability to produce an interpolated spatial model that quantifies uncertainty at each point Overall, Kriging provides a reliable method for spatial data interpolation and uncertainty assessment, making it a valuable tool in geostatistics.

Figure 3 3 The sample of one-dimensional data interpolation generated by the Kriging model [109]

When observations Z(s1), Z(s2), , Z(sn) are collected at known design points in Euclidean space Rd, the Kriging estimator provides an optimal linear prediction of the value at a target location s0 This estimated value, denoted as ̂(s0), is derived through a linear combination of the observed data points, leveraging spatial correlation structures Kriging is widely used in geostatistics and spatial analysis to produce unbiased and minimum variance predictions, ensuring accurate interpolation of unknown values based on existing observations.

The weight wi(s0) is find out to minimized the mean-square predictor error

Where γ indicates the (co) variances

is the matrix whose (i,j)th element is

It's necessary that the predictor is unbiased and the weights of the predictor requirement meet the constraint

By submitting a Lagrange multiplier -2λ and minimize (w, )= T w 2 T 2 ( T 1)

Two equations in the matrix form are derived by implementing stationary condition F=0

By answering Equation 3.7, the coefficient wi of the linear predictor of Equation 3.2 can be recognized as bellow:

The optimal weights of the Kriging model, as described in Equation 3.8, depend on the covariance or related relationships between process parameter values The general form of the relationship function within the Kriging model can be determined based on these covariance structures, ensuring accurate prediction and interpolation of the process behavior.

H can be quantified through the interval input points, while hj represents the correlation parameter that indicates the influence of process parameter j To accurately estimate these correlation function parameters, Kriging software and related literature employ maximum likelihood estimators, ensuring reliable and precise modeling of the process.

For each s0 point, an assessed value is computed utilizing Equations 3.2 and 3.8 The mean-squared forecast error is determined as

The Kriging model is considered a better approximation model than other approximations as RSM or ANN This model can perform better at nonlinear characteristics [112, 113] and power in experiment expenses [114].

Optimization

Particle Swarm Optimization (PSO) is a powerful, nature-inspired optimization algorithm developed by Kennedy and Eberhart in 1995, based on the principles of swarm behavior As a global optimization technique, PSO effectively identifies the optimal solution rather than becoming trapped in local optima, unlike traditional gradient-based methods Leveraging evolutionary computation and low-cost calculations, PSO mimics the collective intelligence of swarms, making it a highly efficient tool for complex problem-solving across various applications.

The Particle Swarm Optimization (PSO) algorithm is well-established in the literature [13, 94, 115, 116], making a detailed explanation unnecessary The PSO framework utilized in this study is illustrated in Figure 3.4 To enhance the optimization process, both Isight 5.9 and MATLAB R2015a software were employed to perform multi-objective optimization efficiently.

Figure 3 4 Algorithm for PSO 3.3.2 Non-dominated sorted genetic algorithm (NSGA-II)

The NSGA algorithm was initially proposed by Srinivas and Deb; however, its early version was complex to compute To address this, Deb and colleagues introduced an improved version known as NSGA-II in 1998, featuring significant enhancements to improve the algorithm's convergence and overall efficiency.

NSGA-II is a innovative multi-objective optimization algorithm extensively used in machining optimization It uniquely combines three key features: a fast non-dominated sorting approach, an efficient crowded distance estimation procedure, and a simple crowded comparison operator, enhancing its performance Typically, the workflow of NSGA-II involves iteratively sorting solutions, maintaining diversity through crowding distance, and selecting optimal candidates, making it a powerful tool for solving complex multi-objective problems in manufacturing processes.

Step 1: Initialization of Non-dominated sorted genetic algorithm based on the problem range and constraint

Step 3: Combine population and evaluate with non-dominated sorting method Step 4: Generate population from the results from the last step

Step 5: Utilize selection, crossover, and polynomial mutation to produce the new population

Step 6: Recombination and selection to produce the new population in reliance on their rank

The steps of NSGA-II multi-objective algorithm can be seen more clearly through the flowchart shown in Figure 3.5

Figure 3 5 Flowchart of NSGA-II algorithm

Results and Discussion

Multi-objective optimization of surface roughness and cutting forces in hard

In this research, a stability lobe diagram of the machine tool is developed by means of CUTPRO software as is shown in Figure 4.1

The development of the stability lobe diagram involves a systematic procedure, as illustrated in Figure 4.1 Hard milling test parameters were selected based on this diagram, which is shown in Figure 4.2 The stability lobes define a spindle speed-dependent boundary between stable and unstable cutting conditions This diagram was used to identify the optimal cutting parameters for subsequent machining processes Consequently, the selected input process parameters for the further hard milling process, derived from Figure 4.2, are detailed in Table 4.1.

Figure 4 2 Analytical stability lobe diagram for hard milling test

Table 4 1 Values of input parameters

Axial depth of cut, a [mm] 0.3 0.4 0.5 0.6

This study applies the Taguchi method to design experiments for the hard milling of AISI H13 steel, optimizing process parameters for improved performance The input variables and their specific levels are detailed in Tables 4.1 and 4.2, ensuring a systematic experimental setup Additionally, Response Surface Methodology (RSM) is employed to develop second-order mathematical models, enabling a comprehensive understanding of the relationships between variables The integration of RSM facilitates multi-objective optimization, aiming to enhance machining efficiency and surface quality in the hard milling process of AISI H13 steel.

Hard milling trials were performed on AISI H13 steel workpieces measuring 200 mm × 100 mm × 40 mm with a hardness of 50 HRC The experiments utilized CMTec M520 ultra carbide end mills with a 10 mm diameter, a 35-degree helix angle, square type design, and four flutes.

The milled surfaces were measured at three different positions by a Surftest SJ-

Using a piezoelectric three-component dynamometer (Model 6423, Lebow®), the cutting forces in three different directions were accurately measured In this study, the resultant cutting force was used to analyze hard milling processes, providing comprehensive insight into cutting dynamics The resultant force was calculated based on a specific equation, ensuring precise assessment of the cutting forces involved in the machining operation This approach enhances understanding of cutting force components, contributing to optimized machining parameters and improved tool performance.

4.1.3 Results and Discussions After experimentation, the values of the Ra and the Ft were given in Table 4 2

Code of factors Value of factors Results

ANOVA for the response characteristics

Table 4 3 ANOVA results for Ra and Ft

Source DF SS MS F P % PC Remarks

The ANOVA results for Ra indicate that the factors (v), (f), and (a) are statistically significant contributors to surface roughness Cutting speed (v) has the most substantial impact on Ra, accounting for 35% of the total variation, followed by feed rate (f) at 33.6% and depth of cut (a) at 22.2% Both feed rate and depth of cut also show significant effects on the cutting force (Ft), with contributions of approximately 38.57% and 39.21%, respectively Additionally, the high R² values of 0.996 for Ra and 0.935 for Ft demonstrate that the empirical data closely align with the statistical model, confirming its reliability.

Using the RSM method combined with Taguchi experimental design, the study developed second-order equations to accurately predict surface roughness (Ra) and cutting force (Ft) during hard milling of AISI H13 steel These predictive models enable optimized process parameters for improved machining performance and surface quality in hard milling operations.

Effect of the input variables on the Ra and Ft models

Figure 4 3 The plots of response surface for Ra

Figure 4.3a illustrates how decreasing feed rate (f) and increasing cutting speed (V) significantly reduce the surface roughness (Ra), aligning with findings from previous research [3] Low cutting speeds tend to promote built-up edge (BUE) formation, leading to poorer surface quality [89] Conversely, higher cutting speeds eliminate BUE, resulting in a smoother and better-finished surface [119].

Figure 4.3b shows that an increase in the feed rate will lead to a rapid increases in

Ra This could be explained since the helicoidal movement of end mill cutter makes furrows on milled workpiece, as reported by many earlier researchers [120]

Figure 4.3c reveals that when the cutting speed is decreased and the depth of cut is increased, this will cause a considerable increase in the Ra This can be accepted

Recent studies indicate that increasing the depth of cut enhances chip load, which subsequently elevates cutting forces during machining processes This relationship is supported by numerous researchers, highlighting how higher cutting depths can lead to increased tool stress and force requirements, ultimately impacting machining efficiency and tool wear Optimizing cutting parameters to balance depth of cut and cutting forces is essential for improving productivity and tool longevity in manufacturing operations.

Figure 4 4 The plots of response surface for Ft

Analysis of the interaction plots in Figures 4.4a-c reveals that the resultant cutting force significantly increases with higher values of feed rate (f) and cut depth (a), while it decreases as cutting speed (V) increases Optimizing these parameters is essential for controlling cutting forces to improve machining performance Understanding these relationships helps in selecting optimal cutting conditions to reduce tool wear and enhance productivity.

This study focuses on simultaneous optimization of surface roughness (Ra) and flank wear (Ft) The optimal multi-objective results, aiming to minimize both Ft and Ra, are summarized in Table 4.4 The optimal input parameters identified include a cutting speed of 100 m/min, a feed rate of 0.015 mm/tooth, and a depth of cut of 0.44 mm These settings achieve the minimum values for Ra and Ft, indicating improved machining performance and surface quality.

The optimized parameters resulted in a force of 66.58 N and a value of 0.206 am for ft, demonstrating precise control over the machining process The composite desirability value, as shown in Table 4.4, is 0.9473, which is very close to the ideal value of 1 This high desirability indicates that the multi-objective optimization effectively enhances machining performance Overall, the results confirm that the optimization process is highly suitable for improving machining efficiency and quality.

Table 4 4 Results of multi-objective optimization for the Ra and Ft

This study utilized the RSM method combined with Taguchi experimental design to optimize the multi-objective parameters of Ra (surface roughness) and Ft (cutting force) during hard milling of AISI H13 alloy steel at 50 HRC The research findings indicate that specific cutting parameters significantly influence surface quality and machining forces, enabling more efficient process optimization The results demonstrate that optimal cutting conditions can be identified to enhance surface finish while minimizing cutting forces These insights provide valuable guidance for improving hard milling performance and achieving higher precision in machining high-hardness tool steels.

ANOVA results indicate that cutting speed, feed rate, and depth of cut all significantly affect surface roughness (Ra) at a 95% confidence level Among these parameters, cutting speed has the most significant impact on Ra, followed by feed rate and then depth of cut.

(2) Based on the results of ANOVA for Ft, it revealed that the given parameters (f and a) have a powerful effect on Ft at the reliability level 95%

Optimized machining conditions for minimizing surface roughness (Ra) and cutting force (Ft) were achieved with a cutting speed of 100 m/min, a feed rate of 0.0150 mm/tooth, and a depth of cut of 0.44 mm Under these parameters, Ra approached 0.206 μm and Ft was around 66.58 N, ensuring improved surface quality and cutting efficiency.

Comparative machinability performance of AISI H13 steel under dry, MQL, and

A DOE approach, utilizing the Taguchi method and ANOVA, was implemented to identify critical variables affecting surface roughness, cutting force, and cutting temperature The study optimized cooling conditions and cutting parameters to minimize these responses, using an L27 orthogonal array to systematically organize experiments Four key input factors—cutting speed, feed rate, depth of cut, and cooling condition—were examined at three different levels, as detailed in Table 4.5, to determine their significant effects and optimize machining performance.

Responses Goal Optimum conditions Predicted values v f a

Table 4 5 The input factors and levels Level

The experimental setup involved machining a 50 mm wide, 200 mm long, and 100 mm high AISI H13 steel workpiece with a hardness of 45 HRC, as depicted in Figure 4.5 A Φ10 TiAlN coated end mill tool was used for all operations Cutting was performed under Minimum Quantity Lubrication (MQL) conditions using cutting oil CT232 at a flow rate of 90 ml/h and an air pressure of 3 kg/cm² When applying nanofluid-based MQL, aluminum oxide (Al₂O₃) nanoparticles with diameters of 10–17 nm and a 1 wt% concentration were dispersed into the cutting oil, prepared via 12 hours of magnetic stirring to ensure uniformity and stability The MQL nozzle was positioned at a 60-degree angle and 30 mm distance from the relief face of the tool to optimize application, as recommended in previous studies All machining cooling conditions are summarized in Table 4.6, with each experiment replicated three times to minimize error and ensure reliable results.

Figure 4 5 Setup for experiments Table 4 6 Machining cooling conditions Cutting tool End milling Φ10 TiAlN coated carbide

 Al2O3 nanoparticle enhanced MQL MQL spray parameters  Distance: 30 mm

 Flow-rate of MQL oil: 90 ml/h 4.2.3 Results and Discussions

The purpose of this study is to find the optimal value of input factors for

The study utilizes the Taguchi method, specifically selecting the signal-to-noise (S/N) ratio as a key performance indicator for optimizing process quality The S/N ratio was estimated using a standardized equation, enabling robust analysis of variability and consistency in experimental results This approach ensures enhanced reliability and efficiency in designing experiments, leading to improved product quality and process performance.

Where: yi is the observed data, n is the number of experiments repeated

The experiments were organized using an L27 orthogonal array to evaluate the impact of four factors at three levels on machining performance The key factors examined include cooling condition (C), cutting speed (v), depth of cut (d), and feed rate (f), each tested at three levels labeled “1”, “2”, and “3” The experimental results, along with the S/N ratio calculated using formula (4.4), are summarized in Table 4.7, providing valuable insights into optimizing machining parameters for improved process quality.

Table 4 7 The result of the experiment and S/N ratios

Surface roughness is a key indicator for assessing the quality of metal machining output In this study, the surface roughness of the workpiece was measured using the Mitutoyo SJ-401 surf-test instrument Analysis of the S/N response means revealed that the optimal parameters are the second level of cooling condition, the third level of cutting speed, the first level of depth of cut, and the first level of feed rate, corresponding to Experiment No 16 (2-3-1-1) The results indicate that cooling condition has the most significant impact on surface roughness, followed by feed rate.

Table 4 8 The mean of S/N response for surface roughness

The Signal-to-Noise (S/N) response graph, depicted in Figure 4.6, illustrates the influence of cutting parameters and cooling conditions on surface roughness Analysis indicates that utilizing nanofluid-based Minimum Quantity Lubrication (MQL) as the cooling method, combined with optimized cutting parameters, is most effective in achieving lower surface roughness These findings highlight the importance of selecting appropriate cooling techniques—specifically nanofluid MQL—to enhance surface quality in machining processes.

This article discusses the analysis of machining parameters, focusing on cutting velocity, depth of cut, and feed rate Specifically, a cutting velocity of m/min, a depth of cut set at 0.2 mm, and a feed rate of 0.01 mm/tooth are applied to optimize machining performance These parameters are essential for ensuring efficient material removal and surface quality in the manufacturing process Proper selection and control of these variables contribute to improved productivity and precision in machining operations.

The S/N ratio plot for surface roughness highlights key factors influencing surface quality, with ANOVA analysis revealing that cooling condition is the most significant parameter, contributing 60.11% to the total effect, followed by feed rate at 19.29% The statistically significant P values (

Tiêu đề	Experimental Investigation of Efficiency of Machining Performance for Difficult-to-cut Materials Under Different Cutting Conditions
Tác giả	Ngoc-Chien Vu, Shyh-Chour Huang
Người hướng dẫn	黃世疇教授
Trường học	National Kaohsiung University of Science and Technology
Chuyên ngành	Mechanical Engineering
Thể loại	Doctoral dissertation
Năm xuất bản	2020
Thành phố	Kaohsiung

Định dạng
Số trang	129
Dung lượng	9,63 MB