CHAPTER 1 INTRODUCTION

Transcription

1 1 CHAPTER 1 INTRODUCTION 1.1 GENERAL In manufacturing, the process of removing unwanted segments of metal workpieces in the form of chips is known as machining, so as to obtain a finished product of the desired size, shape, and surface quality. The machining cutting process can be divided into two major groups which are (i) cutting process with traditional machining (e.g., turning, milling, boring and grinding) and (ii) cutting process with modern machining (e.g., Electrical Discharge Machining (EDM) and Abrasive Water-Jet (AWJ)). (Boothroyd 1989). The basic element of the modern metal removal process consists of a machine tool, a control system and the cutting tool. A tremendous revolution in metal cutting practice takes place as machining is typically carried out using dedicated, specially designed machining systems for mass production. A flexible, agile, or reconfigurable machining system based on Computerized Numeric Control machine tools, the development of open architecture computer based controls and evolution of new tooling materials have greatly impacted metal cutting practice. Milling is the most frequent metal cutting operations in which the material is removed by advancing workpiece against a rotating multiple point tool. End milling, a type of peripheral milling operation, is used for profiling and slotting operation.

2 2 The objectives of this research work is to provide a physical understanding of CNC end milling operation by high speed steel end mill cutter applied to machine aluminum (Al 7075) alloy. Tool geometries, cutting speeds, feed rates and axial depth of cut must be selected to provide reliable and efficient operation. Machinability test and process simulation can be used to choose optimum conditions. The effect of machining parameters such as such as radial rake angle ( ), nose radius (R) of cutting tool,cutting speed (V c ), feed rate (f z ), and axial depth of cut (a p ) on the machining performance are analyzed and investigated. 1.2 END MILLING An end milling process is a multipoint, interrupted cutting, in which the contact between cutting edge and the work piece is not continuous and the uncut chip thickness varies with spindle rotation. Milling is widely used in the industry and milled surfaces are largely used to mate with others in die, aerospace, automobile, biomedical products and machinery design as well as in manufacturing industries. End milling produces profiles, slots, engraves, contours, and pockets in various components. Milling operations have become more productive and efficient over time through the advent of computer aided numerical control milling. Milling is the removal of metal by feeding the work past a rotating multitoothed cutter. During this operation the material removal rate is enhanced as the cutter rotates at a high cutting speed. The surface quality is also improved due to the multicutting edges of the milling cutter. The action of the milling cutter is totally different from that of a drill or a turning tool. In turning and drilling, the tools is kept continuously in contact with the material to be cut, whereas milling is an intermittent process, as each tooth produces a chip of variable thickness.

3 3 The high impact loads at entry as well as fluctuating cutting force make the milling process subject to vibration and chatter. This aspect has great influence on the design of milling cutters. This process is used to generate flat surfaces or curved profile and many other intricate shapes with great accuracy and delivers a very good surface finish. End milling is extensively employed in molds, dies, automotive and aerospace industries. In particular, this process is widely used in the aerospace industry due to the accuracy and complexity involved in the finished dimensions. Competency and productivity of the milling operations have improved due to the introduction of computer aided numerical control milling End Milling Cutter Several types of milling cutters are used for different operations. End mill (profile relief) cutters are cutter with teeth on the circumferential surface on one end. The shank may be straight or tapered. The teeth may be helical or parallel to the axis of the rotation. A spiral end mill is an end mill with moderate helix angle. The End mill cutters generate two workpiece surfaces at the same time; cutting edges are located on both the end face and the periphery of the cutter body. They are usually used in operations such as, facing, profiling, slotting, shoulder, slabbing, plunging and are the most versatile milling tools. They are produced in solid High-Speed Steel (HSS), cobalt enriched HSS-Co, sintered tungsten carbide (WC), ceramic, Polycrystalline Diamond (PCD)/ Polycrystalline Cubic Boron Nitride (PCBN) brazed or vein construction, inserted blade, and indexable insert design. Two major problems often encountered with the end mill cutters which are related to rigidity are springback and chatter. The springback is caused by insufficient stiffness and the results in the deflection or deformation of the cutter due to cutting forces. Excessive springback (or elastic recovery) of the end mill cutter results in a scratch marks during tool retraction. Chatter

4 4 can occur either during the feeding or retracting motions. The resonant frequencies and chatter resistance of a cutter strongly depends on the cutter length to diameter (or overhang) ratio; overhang ratios greater than 4 or 5 are especially susceptible to chatter. In this present work the end mill cutters made of HSS with five different radial rake angles (4 0, 8 0, 12 0, 16 0 and 20 0 ) and nose radius (0.4mm, 0.6mm, 0.8mm, 1mm, 1.2mm) have been used to conduct the experiments. The effect of radial rake angles and nose radius on the machining performance has been analyzed and investigated. The workpiece material selected is aluminum alloy (Al 7075) High Speed Steel The HSS is a self hardening steels have a high degree of red hardness and high abrasion resistance along with a comparable degree of shock resistance. Their primary application is used as a material for cutting tools. The other applications are used as a material for extrusion dies and blanking punches and dies. Their major alloying elements are tungsten, molybdenum, chromium and vanadium, and in superior grade cobalt is added. High speed steels are more difficult to machine and grind because of high carbon and alloy content. The high speed steels are grouped into two divisions as tungsten high speed steels (T-type steels) and molybdenum high speed steels (M-type steels). The T-types are less tough than the M - type, but are heat treated more easily. 1.3 AL7075 ALUMINUM ALLOY Al7075 is a high strength material commonly used for highly stressed structural components. It has been widely used in the missile parts, bicycle frames,all terrain vehicle sprockets, rock climbing equipment, bicycle components, and hang glider airframes. It also used in transport applications,

5 5 including marine, automotive and aviation, due to their high strength-todensity ratio. More recently, Al7075 has been gaining popularity in the mold making and rapid prototyping industry due to its favorable material properties (Jamal Sheikh Ahmad &Twomey 2007). Al7075 was selected for this study as it we can be used in a wide range of applications as well as its increased usage in the mold making and rapid prototyping industry The shown in Table 1.1 chemical composition of AL7075-T6 aluminum alloy is Table 1.1Chemical composition of Al T6 Element Al Zn Mg Cu Fe Cr Mn Ti Si Composition % Max Max 0.3 Max 0.2 Max ECONOMIC CONSIDERATIONS Economic considerations are obviously important in designing an end milling operation. There is generally more than one approach for machining a particular part; each approach will have an associated cost and level of part quality. The initial method of producing a new part, including machine tools, cutting tool materials and geometries, speeds and feeds, and coolant, is generally determined from previous experience with similar parts, hand book recommendations, catalog data, or rules of thumb. These sources provide plausible starting starts, but rarely yield the most efficient approach. In high-volume operations, changes are continually made and experience with the specific part is accumulated. This can be a tedious process and often results in comparatively inefficient practices being used much in the production run. A more efficient methodology to predict and optimize machining practices would be desirable to reduce the time required to identify the best process more systematically (Hernandez et al 2006).

6 6 1.5 PLAN OF RESEARCH The research work presented in the subsequent chapters of this thesis, was undertaken to investigate the effect of process characteristics of CNC end milling process on surface finish, cutting force, vibration amplitude, temperature rise, tool wear, surface topography of machined AL7075-T6 and finite element analysis of machining for AL7075-T6 aluminum alloy. Literature Survey Machining characteristics for Al7075-T6 Problem Identification &Material Selection Material characteristics for CNC end milling process Al7075 T6 Aluminium Alloy Experiment No: 1-5 RSM-CCD (5factors, 5levels, and 32 runs) CNC end milling Process Parameters 1.Radial angle of cutting tool ( ) 2.Nose radius (R) 3.Cutting speed (V c) 4.CuttingFeed rate (f z) 5.Axial depth of cut (a p) Trial Runs for Finding Limits of Process Variables Design of Experiments Experiment no: 6 Surface topography Material best suited for product like Arerospace, defence, tool & die and RPT components Measuring Response for Expt No 1-5 [surface roughness (Ra), cutting force (F), vibration amplitude (A m) temperature rise (T r) and tool wear (T w) for HSS end milling] Experiment No: 7 RSM- CCD (3factors, 5levels, and 20 runs) Finite element analysis Development of Regression Models for Ra, F, A m, T r, T w Measuring Response for Expt No 7 (force, temperature, stress, strain etc) Validations of Models-Comparison of Regression Models & ANN Models Development of Regression Models for response Main effect Analysis Optimization of Process Parameters Using PSO,SA& GA Conclusions Figure 1.1 Sequence of research work

7 7 The various aspects of the research work are presented in Figure 1.1. The process variables selected for investigation of surface finish, cutting force, vibration amplitude, temperature rise, tool wear and surface topography, are the radial rake angle( ), nose radius(r), cutting feed rate(f z ) and axial depth of cut(a p ) for HSS end mill process. A final investigation of finite element analysis of machining using Deform software for Al7075-T6 aluminum alloy is done. Advanced statistical technique - Design of Experiments (DOE) was used for deciding the experimental runs for this investigation. The central composite rotatable design was used for conducting the experiments to develop mathematical models for predicting the responses. The optimization of process parameters to minimize surface finish, cutting force, vibration amplitude, temperature rise and tool wear was done by using intelligent optimization techniques like Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO). A source code was developed in MATLAB for optimization using GA, SA and PSO. Before going into the details of investigations carried out in the research work, an introduction to all aspects is described below under the appropriate headings PredictionofSurface Roughness The quality of the surface is significantly important for evaluating the productivity of machine tools, and mechanical parts. A proper cutting condition is extremely important because this factor determines the surface quality of manufactured parts. The surface roughness value is a result of the tool wear. When tool wear increase, the surface roughness also increases. The determination of the sufficient cutting parameters is a very important process

8 8 obtained by means of both minimum surface roughness values and long tool life. The poor surface finish due to the chatter marks, excessive tool wear, reduces dimensional accuracy, and tool damage. Machine-tool operators often select conservative cutting conditions to avoid chatter, thus, decreasing productivity. Surface roughness is an important parameter in milling which decides how the work piece components interact with its assembled parts. Obviously rough surface will wear more and have a higher coefficient of friction than smooth surface; hence surface roughness is a good predictor of quality product (Benardos et al 2003). The demands for high quality of product relay on surface roughness urge the industrial automation to focus its attention on the surface finish of the product. Though surface roughness is a prominent parameter, it is expensive to control it since the manufacturing cost will increase exponentially with a decrease in surface roughness. An effective model to predict the surface roughness becomes essential to ensure the desired quality in end milling Prediction of Cutting Force The prediction of cutting forces in milling processes is really extremely important to effectively design the machining process, including the choice of the optimal process parameters, the tooling and the fixture. A correct estimation of such force could avoid quality problem related to the tool deflection, chatter or fixture thereby improving also the productivity. The excessive cutting forces are undesirable in milling, which results in a poor surface finish, inaccurate dimensions and increases tool wear. Measured cutting forces are used to compare the machinability of materials and for real time control in monitoring a cutting process, tool wear and failure. Estimation of cutting forces has been used to determine machine power requirements, bearing loads and to design fixtures. The most actual techniques to improve

9 9 the quality and efficiency of milling is arising the possibility to change continuously the cutting parameters in order to avoid chatter and optimize the production. Cutting forces generated in metal cutting operations cause deflections of the part, tool or machine structure and supply energy to the machining system which results in excessive temperatures or unstable vibrations. The excessive cutting forces are undesirable in milling, which results in a poor surface finish, inaccurate dimensions and increases tool wear (Kuljanic&Sortino 2005). Measured cutting forces are used to compare the machinability of materials and for real time control in monitoring a cutting process and tool wear and failure. Estimation of cutting forces has been used to determine machine power requirements and bearing loads and to design fixtures.an effective model to predict the cutting force becomes essential to ensure the stability in end milling process Prediction of Vibration Amplitude The action of the milling cutter is totally different from that of a drill or a turning tool. In turning and drilling, the tool is kept continuously in contact with the material to be cut, whereas milling is an intermittent process, as each tooth produces a chip of variable thickness. It is possible for periodic force variations in the cutting process to interact with the dynamic stiffness characteristics of the machine tool to create vibrations during processing that are known as chatter. The demand on high productivity leads to increased material removal per unit time and higher spindle speeds, increased feed rate, and greater depth of cut. However, at certain combinations of machining parameters; process instabilities and vibrations can occur which result in decreased accuracy, poor surface finish, reduced tool life time, a decrease of the metal removal and in the worst case spindle failure and even a reduction of life of a machine tool.

10 10 Monitoring of cutting tools and cutting process is of considerable economic importance in the manufacturing industry. Continuity of production with improved quality and reliability, preservation of investing capital, optimization of manufacturing process efficiency and economic operation can only be assured by an efficient monitoring of cutting tools, to predict damage and to avoid any disturbances which affect the quality of machined components. Prediction of the vibration of the machine tool is of great concern as it helps to increase quality of machining Prediction of Temperature Rise The power consumed in metal cutting is largely converted into heat near the cutting edge of the tool, and many of the economic and technical problems of machining are caused directly or indirectly by this heating action. The cost of machining is very strongly dependent on the rate of metal removal, and costs may be reduced by increasing the cutting speed and/or the feed rate, but there are limits to the speed and feed above which the life of the tool is shortened excessively. With these higher melting point metals and alloys, the tools are heated to high temperatures as metal removal rate increases and, above certain critical speeds, the tools tend to collapse after a very short cutting time under the influence of stress and temperature (Edward Trent & Paul 2000). It is, therefore, important to understand the factors which influence the generation of heat, the flow of heat, and the temperature distribution in the tool and the work material near the tool edge. The heat energy produces high temperature in the deformation zones and surrounding regions of the chip, tool and work piece. This temperature rise propagates tool wear, devastates the work piece quality and increases tooling cost. The temperature rise affects the work material properties, as the moderate temperature rise induces residual stress in the machined surface, while the high temperature rise may leave a hardened layer

11 11 on the machined surface. The cutting tool which possesses high hardness at room temperature is unable to retain the hardness at high temperature during milling. Temperature rise on the rake face of the tool has a strong influence on tool life. As temperature in this area increases, the tool softens and wears more rapidly, the tool material diffuses into chips and leads to tool failure and the workpiece material adheres to the tools which causes rapid wear. The softening of the tool at high temperature rise propagates wear rapidly, therefore determining the critical value of the temperature becomes important for the reduction of tool wear. Temperature rise on the relief face of the tool affects the surface finish and metallurgical state of the machined surface Prediction of Tool Wear The cutting tool wear reduces the surface integrity of the product in the end milling process, hence it is essential to know tool wear level to inhibit any deterioration in machining quality. During machining cutting tools are subjected to rubbing process, where the friction between cutting tool and workpiece materials results in progressive loss of materials in cutting tool. Tool wear is a change of shape of the tool from its original shape resulting from the gradual loss of tool material. This tool wear becomes an important parameter in end milling operation. The worn tool may cause significant degradation in the work piece quality.the consequence of the tool wear is poor surface finish, increase in cutting force, increase in vibration of the machine tool, increase in tool-workpiece temperature during machining, decreases in dimensional accuracy, increases in the cost and lowering of the production efficiency and component quality. Prediction of tool wear becomes important to increase the maximum utilization of the tool and to minimize the machining cost. An effective model to predict the tool wear becomes imperative.

12 Study on Surface Topography of Machined Specimens Surface topography effects are assessed with regard to different tool geometry orientations on its surface. Machined surface characteristics such as surface roughness and form as well as the sub-surface characteristics such as residual stress, granular plastic flow orientation and surface defects (porosity, micro-cracks, etc.) are important in determining the functional performance of machined components. The quality of surfaces of machined components is determined by the surface finish and integrity obtained after machining. Surface integrity is defined as the inherent or enhanced condition of a surface produced during machining or other surface operations.metal removal operations lead to the generation of surfaces that contain geometric deviation (deviation from ideal geometry) and metallurgical damage different from the bulk material. The geometrical deviation refers to the various forms of deviations such as roundness, straightness etc. Typical metallurgical surface damage produced during machining include micro-cracks, micro-pits, tearing (pickup), plastic deformation of feed marks, re-deposited materials, etc. High surface roughness values, hence poor surface finish, decrease the fatigue life of machined components (Helmi&Youssef Hassan 2008). Therefore, it is essential to study the surface topography of the machined Specimens Finite Element Models Computer simulations based on FEA have seen increased attention in the last two decades because they also offer the possibility to reduce the cost of experimental research. Advancements in remeshing procedures and damage models has brought the accuracy of FEA metal cutting simulations to a higher level. Amongst the most popular commercial software used at present are Abaqus TM, Deform (2D, 3D) and LS-DYNA. An accurate simulation makes a detailed examination of physical phenomena possible. Simulation of metal cutting allows for example to evaluate the chip forming

13 13 process and to predict chip shapes which are dependent on several material and process parameters. FEA based modelling and simulation of machining processes furthermore allows to predict physical parameters such as strain, stress, velocity, temperature and cutting force, but it can also provide information regarding the integrity of the machined surface and cutting tools. In order to reduce the experimental costs, FEM of machining can be employed to qualitatively predict tool forces, stress, temperature, strain, strain rate and velocity fields Design of Experiments Design of experiments is a scientific approach of planning and conducting experiments to generate, analyze and interpret the data so that valid conclusions can be drawn efficiently and economically (Adler et al 1975). It has been proved to be very effective for improving the process yield, process performance and process variability. The DOE procedure has been applied in this work to develop mathematical models to surface roughness, vibration amplitude, cutting force, temperature rise and tool wear. Direct and interactive effects of the process parameters have been analyzed and presented in graphical form. This enables the CNC end milling technologists to choose optimum process parameters to achieve minimum surface roughness, vibration amplitude, cutting force, temperature rise and tool wear of Al7075-T6 aluminum. The following methodology was adopted in this work to achieve the above said objectives.

14 14 1. The ranges of process parameters were identified by means of the following methodology. The upper and lower limit of each process variable was estimated initially through trial runs. For instance, trial runs for varying values of cutting speed between 50 and 160m/min were conducted in order to identify the lower limit and upper limit of cutting speed. During the trial runs, the other variables were fixed at a constant value, i.e. at 12 º, R at 0.8 mm, fz at 0.04 mm/tooth, and ap at 2.5 mm. Later the specimen was scrutinized on the basis of surface roughness and the same factors form the basis for fixing the levels. 2. Five factor five level central composite rotatable designs with 32 experimental runs for developing mathematical models to predict, surface roughness, vibration amplitude, cutting force, temperature rise and tool wear for HSS end mill cutter. 3. Three factor (R,V c,f z ) five level central composite rotatable designs with 20 experimental runs for developing mathematical models to analysis the main effect parameter for 2D FEA simulation process. 4. Three factor (V c,f z,a p ) five level central composite rotatable designs with 20 experimental runs for developing mathematical models to analysis the main effect parameter for 3D FEA simulation process. 5. The value of the regression coefficients gives an idea as to what extent the control parameters affect the response quantitatively. Less significant coefficients were eliminated by finding p-values of the coefficients. If the p-value of the coefficient is less than 0.05, the coefficient becomes

15 15 significant otherwise it becomes insignificant. The final mathematical models were developed using only the significant coefficients. 6. The adequacies of the models were tested using analysis of variance technique (ANOVA). As per this technique the calculated value of F-ratio of the model developed should not exceed the standard value of F-ratio for a desired level of confidence (selected as 95%) and the calculated value of R- ratio of the model developed should exceed the standard tabulated value for the same confidence level. If these conditions are fulfilled then the models are considered to be adequate. The validity of the final mathematical models was further tested by drawing scatter diagrams which compare observed and predicted values and shows the agreement between them in graphical form. 7. Contour plots and response surfaces were drawn using Design expert software to study the two way interaction effects of the process variables on responses Artificial Neural Networks Due to the complexity of cutting-process phenomena, there is a heavy nonlinearity in the relationships between the involved variables. For this reason, several researchers have pointed out the shortcomings of the statistical approaches in modeling these relationships.on the contrary, some artificial-intelligence-based tools have proved their ability to match complex nonlinear relationships. The most popular and deeply studied techniques in soft computing are the artificial neural networks.

16 16 The artificial neural network is designed to simulate the information processing of the human brain or neural system. Neural network consist of a basic elementary element called neuron which process the input signal and feed it to a differentiable transfer function to generate output in a similar way the brain neuron works. The advantage of neural network is that it can accommodate larger input data and filter the noisy and incomplete data. The most widely used technique is feed forward back propagation neural network. This neural network uses network which training functions that updates weights and bias values according to gradient decent to reduce errors (Hazim et al 2010). The network is a multi layer network consists of an input layer (input parameter fed), output layer (generates output response) and at least one hidden layer (uses training function to process input to yield output). The backward propagation network is a supervised learning algorithm where the set of inputs and response obtained from the experiment are provided at the training stage. The input is feed forward from the input layer, propagates through the hidden layer where by means of training function the output is obtained. Training plays an important role in the accuracy of the prediction of response. The accuracy of the network was measured by the Mean sum of Squared Error (MSE) between the measured and predicted values. During the training the output obtained from the network is compared with the experimental value and the error is minimized by adjusting the weights used in the network. The newly adjusted weight and bias value is again propagated backwards for further training. The training is an iterative process and will stop once an acceptable error is reached. The experimentally measured values are used to train back propagation neural network model to predict the average surface roughness (R a ), tool wear (T w ), cutting force (F x, F y &F z ), acceleration amplitude (A m ) and temperature rise (T r ) by using MATLAB R2011a software.

17 Need for Modeling and Optimization of Machining Processes Manufacturing includes various types of processes and today s machining processes are caught between the growing needs for quality, high process safety, minimal machining costs, and short manufacturing times. In order to meet the demands, manufacturing process setting parameters has to be chosen in the best possible way. In today s manufacturing environment many large industries use highly automated and computer-controlled machines as their strategy to adapt to the ever-changing competitive market requirement. Due to high capital and manufacturing costs, there is an economic need to operate these machines as efficiently as possible in order to obtain the required pay back. The success of the machining process depends upon the selection of appropriate process parameters. The selection of optimum process parameters plays a significant role to ensure the quality of product, to reduce the manufacturing cost and to increase productivity in computer controlled manufacturing process. In the case of milling operation the significant parameters that need to be optimized are cutting speed, radial and axial depths of cut, feed, and number of passes. Modeling and optimization of process parameters of any manufacturing process are usually a difficult task where the following aspects are required: knowledge of the manufacturing process, empirical equations to develop realistic constraints, specification of machine capabilities, development of an effective optimization criterion, and knowledge of mathematical and numerical optimization techniques. A human process planner selects proper parameters using his own experience or from the handbooks. The performance of these processes, however, is affected by many factors and a single parameter change will influence the process in a complex way. Because of the many variables and the complex and stochastic nature of the process, achieving the optimal performance, even for a highly

18 18 skilled operator is rarely possible. An effective way to solve this problem is to discover the relationship between the performance of the process and its controllable input parameters by modeling the process through suitable mathematical techniques and optimization using a suitable optimization algorithm. The first necessary step for process parameter optimization is to understand the principles governing the manufacturing process by developing an explicit mathematical model which may be mechanistic and empirical.the model in which the functional relationship between input output and inprocess parameters is determined analytically is called mechanistic modelling. However, as there is a lack of adequate and acceptable mechanistic models for manufacturing processes, the empirical models are generally used in manufacturing processes. The modeling techniques of input output and inprocess parameter relationships are mainly based on statistical regression, fuzzy set theory, and artificial neural networks. types: The optimization algorithms can be classified into two distinct 1. Traditional optimization algorithms: These are deterministic algorithms with specific rules for moving from one solution to the other. These algorithms have been in use for quite some time and have been successfully applied to many engineering design problems. The examples of these algorithms include non-linear programming, geometric programming, quadratic programming, dynamic programming, etc. However, the optimization problems related to manufacturing are usually complex in nature and characterized by mixed continuous discrete variables and discontinuous and non-convex design spaces. Hence, the traditional optimization methods fail to

19 19 give the global optimum solution, as they are usually trapped at the local optimum. Also these techniques are usually slow in convergence. To overcome these problems, researchers have proposed non-traditional methods for optimization of process parameters of various manufacturing processes. 2. Nontraditional optimization algorithms: These algorithms are stochastic in nature, with probabilistic transition rules. These algorithms are comparatively new and gaining popularity due to certain properties, which the deterministic algorithms do not have. These methods are mainly based on biological, molecular, or neurological phenomenon that mimics the metaphor of natural biological evolution and/or the social behavior of the species. To mimic the efficient behavior of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. Examples of these algorithms include Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Shuffled Frog Leaping (SFL), Harmony Search (HS), etc. Figure1.2.(a) and (b) provides a general classification of different input-output and in-process parameter relationship modelling and optimization techniques in metal cutting processes, respectively (Mukherjee & P.K. Ray,2006, NorfadzlanYusup et al. 2012). Whereas conventional techniques attempt to provide a local optimal solution, non-conventional techniques based on extrinsic model or objective function developed, which is only an approximation, and attempt to provide near-optimal cutting conditions.

20 20 Figure 1.2 Classification of modelling (a) and optimization (b) Techniques in metal cutting process problems The traditional methods of optimization and search do not fare well over a broad spectrum of problem domains. Recently the focus is on to bring out the utility and advantages of non-traditional optimization techniques, such as genetic algorithm, simulated annealing and particle swarm optimization. In this work the new evolutionary techniques like GA, SA and PSO were applied for the CNC end milling optimization of process parameters to minimize surface finish, cutting force, vibration amplitude, temperature rise and tool wear. The results obtained from these techniques were compared and the appropriate method that could be employed to get accurate results and presented.

21 SUMMARY End milling operation is the most common metal removing process in automotive and valve industries in order to produce components with complex profile. Metal cutting at high speed results in distortion of workpiece surface finish, rapid tool wear, increase in cutting forces and acceleration amplitude and high temperature rise at the tool-workpiece interface. Prediction of these responses which affect the quality of machining in end milling becomes important. This report depicts the description of the investigations on the effect of machining parameters of the measured response in end milling; and of the prediction and optimization of these responses in terms of machining parameters. In the light of above mentioned chronology of investigations, different chapters in this report are incorporated in the following sequence: Literature survey, Experimental designs, Study on surface finish, cutting force, vibration amplitude, temperature rise and tool wear, which includes development of mathematical and artificial neural network model to predict surface finish, cutting force, vibration amplitude, temperature rise and tool wear, optimization of process parameters to minimize surface finish, cutting force, vibration amplitude, temperature rise and tool wear using PSO, SA and GA. A study on the surface topography of the machined surface was also carried out to determine the effect of process parameters on the specimens. Finally, a focused finite element simulation of cutting processes was also carried out to determine the effect of machining parameters on machining behavior.the conclusions of the investigations are summed up in the last chapter, which is followed by a list of references for different chapters.