Nghiên cứu xác định chế độ cắt tối ưu theo hàm mục tiêu chất lượng bề mặt, đảm bảo năng suất cắt khi gia công thép SUS304 trên máy tiện CNC TT TIENG ANH

However, austenitic steel is considered a difficult material to machine due to its high tensile strength, low thermal conductivity, high cutting force leading to high work hardening, inc

HANOI UNIVERSITY OF INDUSTRY

-

TRAN VIET HOI

DETERMINATION OF OPTIMAL CUTTING PARAMETERS TO IMPROVE SURFACE INTEGRITY, ENSURING MACHINING PRODUCTIVITY IN TURNING

The desertation can be found at:

- The library of Hanoi University of Industry

- Vietnam National Library

Scientific supervisors:

1 Assoc Prof Dr Pham Van Bong

2 Prof Dr Tran Van Dich

INTRODUCTION

1 The importance of the topic

Austenitic stainless steel has good mechanical, physical properties, high hardness, good corrosion resistance and heat resistance, so it is widely used However, austenitic steel is considered a difficult material

to machine due to its high tensile strength, low thermal conductivity, high cutting force leading to high work hardening, increased tool wear rate, poor surface quality and low machining productivity

After the processing procedure, surface quality is an important criterion to evaluate the quality of the workpiece, the corrosion resistance and the fatigue strength of the workpiece Residual stress and surface roughness are evaluated as two important criteria Residual stresses are generated in the processing procedure due to heat generation, mechanical deformation and changes in material organization The surface after machining with residual compressive stress will be beneficial to limit crack propagation and increase fatigue strength, whereas tensile residual stress will adversely affect the above problem In practice, the measurement, processing of measurement results and modeling of residual stresses are very complex

In manufacturing, the machining process's efficiency is evaluated

by improving quality, reducing costs, and increasing productivity So optimizing the machining process is the goal and the challenge of manufacturing With the development of science and technology, new approaches have been deployed to solve optimization problems for accuracy and fast processing speed in finding optimal results Research on the characteristics and machinability of stainless steel to improve surface integrity is a topic that has received the attention of many researchers before Still, the study and publication mainly evaluate machining quality or accuracy by evaluating surface roughness and microhardness In contrast, for the part after machining, the criterion of residual stress plays a vital role because this even determines the fatigue strength and cracks formed on the part's surface There are few studies and publications on analyzing the influence of the machining process on residual stress Solve the multi-objective optimization problem of important surface integrity such as

surface roughness and residual stress when turning austenitic stainless steel SUS304 based on advanced algorithms The above issues are

guidelines for the author to choose the topic: “Determination of

optimal cutting parameters to improve surface integrity, ensuring machining productivity in turning SUS304 on CNC lathe”

2 The aim, objective and scope of the study

2.1 The aim of the study

This research aims to research, evaluate the influence and determine the relationship between the cutting parameters and some typical output factors of the turning process Moreover, the research was conducted to develop and solve optimization problems when processing stainless steel to improve the efficiency of the machining process

2.2 The objective and scope of the study

- Research object: Research and evaluate the influence of input

cutting parameters (V, f, t) on the machining process in turning

SUS304 on CNC lathe

- Research scope: Study to determine the relationship between

cutting speed (V), feedrate (f), depth of cut (t) to surface roughness,

microhardness, and residual stress

4 Scientific and practical significance

- Scientific significance: Research is the basis for establishing

cutting parameters when turning stainless steel on CNC machines and

is the basis for optimization to improve the surface quality and machining productivity

- Practical significance: Research results can be applied in

production with products made from stainless steel, and at the same time as documents for research at universities

5 Dissertation structure: The thesis is presented in four chapters:

Chapter 1: Overview of stainless steel machining

Chapter 2: Research to evaluate the influence of cutting parameters to surface integrity

Chapter 3: Experiment to determine the effect of cutting parameters

on surface integrity in turning SUS304

Chapter 4: Optimizing cutting parameters to improve surface integrity

in turning SUS304

6 New contributions of the thesis

- Develop experimental model, measure, calculate output criteria and analyze and evaluate the influence of cutting parameters on surface roughness, microhardness, surface residual stress

- Apply the Response surface methodology (RSM) and experimental design Box-Behnken (BBD) to develop mathematical models of the relationship between cutting parameters with surface roughness, microhardness and residual stress

- Applying Pareto optimal solution based on Bat algorithm (BA)

to solve a multi-objective optimization problem to determine optimal cutting parameters to improve surface integrity

CHAPTER 1: OVERVIEW OF STAINLESS STEEL MACHINING 1.1 Overview of stainless steel

chromium content and

adding other elements

such as Nickel and

Stainless steels

Basic families Derived families

Ferritic Martensitic Austentitic Duplex PH

Ferit/Austenit Martensitic

Austentitic Austentitic Figure 1.1 Types of Stainless steels stainless

Semi-1.2 Austenit stainless steel

Austenitic stainless steel has a minimum Nickel and Chromium content of 7% and 16%, respectively, a Carbon content of ≤ 0.08%, and a few other elements Austenitic steels are divided into two groups: Standard group (Type 300), where Nickel is the austenitic stabilizer with a sufficient amount of Chromium and Nickel; Nitrogen can also be used to increase strength, in which SUS304 is the most popular grade stainless steel due to its excellent formability and weldability, is non-magnetic, has a much greater coefficient of thermal expansion and lower thermal conductivity than other grades Manganese group (Type 200), which adds a significant amount of Manganese, usually with higher levels than Nitrogen

1.3 Machinability of austenitic stainless steels

The machinability of material is evaluated through some criteria such as size, surface finish quality, energy consumption, chip formation, wear, and tool life Austenitic steel has high tensile strength and low thermal conductivity (Table 1.1), non-transformed steel, so it cannot harden but tends to increase cold hardening It is considered a more difficult material to work with than carbon steel

Table 1.1 Physical properties of the materials

Grade

Tensile strength ( MPa)

Elongation (%)

Thermal conductivity (W/mK)

1.4 Research situation on stainless steel processing

1.4.1 Overseas studies

Technological parameters affecting surface quality are of interest

to many researchers The conducted and published studies show that the feedrate and cutting speed influence the surface roughness, as shown in the publications: M Batista researched based on SOM to evaluate chip shrinkage when turning Dry Titanium The results show that the chip shrinkage coefficient is more significant when turning structural carbon steels due to the low thermal conductivity Xinxin Zhang and et al studying high-speed stainless steel milling, show that

feedrate is the most important factor affecting surface roughness Ra Lakhdar Bouzid studied the optimization of tool wear in turning SUS304 using the desirability function approach (DFA) Franko Puh studied the optimization of cutting parameters when turning with quality combinatorial properties using gray relation analysis (GRA) Residual stress in high-speed milling of aluminum alloy 6061-T651 with finite element analysis by author YB Guo et al The results show that the residual stress in the infeed direction is tensile near the surface and rapidly becomes compressive at a depth of 20-25µm DW

Wu in the study shows that the hardness of the material directly and significantly affects the value of residual stress caused by machining and identifies other hard steel processing methods to process ductile steel, machined surface of ductile steel without any phase transition Selecting the cutting parameters for processing procedure is one of the stages determining product quality and processing productivity In the recent trend, researchers have focused on developing new algorithms to optimise the processing procedure, ensuring many different goals Many publications have shown the effectiveness of applying new algorithms to solve optimization problems such as: Authors Poornima and Sukumar research on optimizing the cutting parameters inturning SUS40 materials using response surface methodology (RSM) and the genetic algorithm (GA) N Ahmad studied and compared optimally the surface roughness when machining SUS1045 steel using GA and particle swarm algorithm (PSO) The results obtained from the study show that the predicted values according

to the RSM method are 99.3% Meanwhile, PSO obtained the lowest surface roughness when compared with Taguchi and GA methods

1.4.2 Previous studies in Vietnam

In Vietnam, studies on the effect of cutting parameters on surface quality have received the attention of researchers However, the studies mainly evaluated the influence of cutting parameters on surface roughness, tool wear, cutting force such as: Research by Nguyen Tien Dung in turning SUS304 steel, evaluated the influence

of (V, f, t) to surface roughness R a The results show that the feed-rate

is the most influential parameter Author Le Thi Hoai Thu, studies the machining accuracy when turning high ductile materials to evaluate

the influence of the cutting parameters on the parameter R a In the doctoral thesis of Nguyen Chi Cong, he assessed the influence of the

cutting parameters on the roughness R a, tool wear and cutting force in turning SUS304, applying analytical methods to solve the problem and find the set of tools The optimal cutting parameters when turning

are V = 42m/min, f=0,08mm/rev, t=0,6mm.

CONCLUSION OF CHAPTER 1

In order to improve the surface quality of the part and the efficiency of the machining process, especially when processing materials with high ductility and strength such as SUS304, it is necessary to consider the problems encountered when processing, through an overview study found that:

- Austenitic stainless steel in which SUS304 is one of the difficult materials to process Machinability (technology in machining) and efficiency of the machining process are assessed through the quality of the part surface after machining, the wear mechanism and the tool life

- Studies in Vietnam and other countries related to the influence of (V, f, t) on surface quality when machining stainless steel, techniques and tools applied to optimize processing procedure are very diverse However, research on the influence cutting parameters on surface layer residual stress has not been paid much attention Studies show that the determination of surface quality criteria includes: surface roughness (criteria for determining product quality), micro hardness (characteristic criteria for corrosion resistance), application Residual stress (main criterion affecting fatigue strength) in turning SUS304 steel on CNC lathe is an important and necessary research direction

CHAPTER 2: RESEARCH TO EVALUATE THE INFLUENCE

OF CUTTING PARAMETERS TO SURFACE INTEGRITY 2.1 Topography of surfaces

determined by equation 2.1 as follows:

1( ) ( )

L a o

L

where: R is the average order compared to with the mean line, L is a

the standard length for evaluation, y x( ) which, is the rough profile

2.1.2 Influence of cutting parameters on surface roughness

Surface roughness is influenced by many factors such as: cutting parameters, phenomena occurring in the processing procedure, tool geometry parameters, workpiece characteristics (Figure 2.1) In

which the influence of cutting speed (V ), feedrate ( f ), depth of cut ( t ) has been received the most attention

2.2 Microhardness

Microhardness is one of the important parameters of surface quality and is used to evaluate the effect on the workability and service life of the workpiece Some studies have shown that surface hardening will increase the fatigue strength of the part by about 20%, increase the wear resistance by 2 to 3 times However, if the surface

is too hard, it will reduce the fatigue strength of the part

2.3 Residual stress

The compressive residual stress on the surface can increase the fatigue strength of the part by 50% and reduce it by 30% when the surface has tensile residual stress Three sources generate residual stresses during

Figure 2.1 Cutting parameters affectingR a

Cutting tools

properties

Tool shape

Tool material

Depth of cut Cooling fluid

Tool angle Stepover

Friction

Accelerations Chip formation

Cutting force variation

SURFACE ROUGHNESS

Feedrate

Cutting speed

Process kinematics

Workpiece

properties

Cutting phenomena

machining: heat generated during cutting, mechanical deformation, and organizational change.The main techniques for measuring residual stress include: non-destructive, semi-destructive, and destructive depending on the test conditions and the sample to be measured Among them, X-ray diffraction is one of the best methods for determining residual stress XRD data analysis methods to determine strain in materials such as Scherrer, Williamson-Hall, strain size histogram (SSP), Warren-Averbach method In which Williamson-Hall is evaluated as a straightforward analytical method based on the half-peak width of the FWHM diffraction

From the X-ray diffraction pattern, the width of diffraction peaks βhkl

is determined by the width due to the change in crystal size β L and the

width due to microscopic deformation β ε according to the formula:

in which β hkl is the total diffraction width, β L is the width due to crystal

size and β ε is the width due to strain Peak width due to crystal size change is calculated from formula:

cos

L

k L







where: - Wavelength (0.15405 nm); L - Crystal size (nm);

: diffraction angle (°/ rad); k : 0.94

Similarly, the XRD peak width due to deformation is determined by

the formula: β ε = 4εtanθ, with  is deformation

Substituting into formula (2.7) we get: 4 sin

hkl

k L



Multiplying both sides by cos,

hkl

k L

L



From there, we can calculate the crystal size L

The displacement of the vertices and angles ( ) , allowing the average lattice strain to be determined along a given direction [hkl] is determined by the formula: o

- Measure surface roughness (R a)by the electronic surface scanning device

- Measure the microhardness (HV)with the Vickers hardness tester

- Measurement of residual stress ( ) by X-ray diffraction method

is a suitable option to measure and calculate residual stress value CHAPTER 3: EXPERIMENT TO DETERMINE THE EFFECT

OF CUTTING PARAMETERS ON SURFACE INTEGRITY IN

TURNING SUS304 3.1 Determine the mathematical model representing the relationship between the cutting parameters and outputs

3.1.1 Response Surface Methodology (RSM)

Response surface methodology is used to model the relationship between input variables and output criteria in the study The Box-Behnken experimental design method is evaluated to be suitable and effective with polynomial equations of second-order

(R a ,,HV)= C + a.V+b.f+c.t+d.V 2 +e.f 2 +g.t 2 +h.V.f+ i.V.t+ j.f.t (3.3) 3.1.2 Analysis of Variance (ANOVA)

ANOVA was applied to evaluate the effect of input parameters from a series of experimental results employing experimental design

during machining and interpretation of the output data

3.2 Develop experimental models

3.2.1 Experimental diagram

The research is carried out in the following steps: Design input parameters, experiment and measure output factors, analyze variance to evaluate the influence of cutting parameters on outputs, develop

regression function, optimize influence parameters, verify optimal results

3.2.2 Determination of experimental cutting parameters region in turning SUS304

Table 3.1 Cutting paremeters

Cutting speed ( )V (m/min) 230 260 290 Feedrate ( )f (mm/rev) 0,08 0,14 0,2 Depth of cut ( )t (mm) 0,1 0,25 0,5 Based on the analysis in Chapter 2 to choose cutting parameters for turning SUS304 Along with the rigidity of the technological system, the characteristics of the processing materials, choose the region of the cutting parameter according to the recommendations of the tool manufacturer as in Table 3.1

3.2.3 Output parameters in the research

1/ Surface roughness; 2/ Microhardness; 3/ Residual stress

3.3 Experimental conditions influence of cutting parameters on outputs in turning SUS304

grooved along the length

to form 15 samples Figure 3.3 Mori Seiki SL-253

3.4 Experimental determination of some characteristics of

surface integrity in turning SUS304

3.4.1 Experimental sequence

Step 1: Turn a thin layer with t= 0.1mm throughout the machining

length to eliminate residual errors, deviations in non-parallelism between

Figure 3.4 Experimental workpiece drawing

Figure 3.7 Roughness meter Mitutoyo

Figure 3.8 Microhardness meter

Figure 3.9 X-ray machine