Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation

Empirical models for machining time and surface roughness are described for exploring optimized machining parameters in turning operation. CNC turning machine was employed to conduct experiments on brass, aluminum, copper, and mild steel. Particle swarm optimization (PSO) has been used to find the optimal machining parameters for minimizing machining time subjected to desired surface roughness. Physical constraints for both experiment and theoretical approach are cutting speed, feed, depth of cut, and surface roughness. It is observed that the machining time and surface roughness based on PSO are nearly same as that of the values obtained based on confirmation experiments; hence, it is found that PSO is capable of selecting appropriate machining parameters for turning operation.     

Influence of machining parameters on the machinability of particulate reinforced Al/SiC–MMC

The paper presents the result of an experimental investigation on the machinability of silicon carbide particulate aluminium metal matrix composite during turning using a rhombic uncoated carbide tool. The influence of machining parameters, e.g. cutting speed, feed and depth of cut on the cutting force has been investigated. The influence of the length of machining and cutting time on the tool wear and the influence of various machining parameters, e.g. cutting speed, feed, depth of cut on the surface finish criteria has been analyzed through the various graphical representations. The combined effect of cutting speed and feed on the flank wear has also been investigated. The influence of cutting speed, feed and depth of cut on the tool wears and built-up edge is analyzed graphically. The job surface condition and wear of the cutting tool edge for the different sets of experiments have been examined and compared for searching out the suitable cutting condition for effective machining performance during turning of Al/SiC-MMC. Test results show that no built-up edge is formed during machining of Al/SiC-MMC at high speed and low depth of cut. From the test results and different SEM micrographs, suitable range of cutting speed, feed and depth of cut can be selected for proper machining of Al/SiC-MMC.     

Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool

The main of the present study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (tool life, surface roughness and cutting forces) in finish hard turning of AISI 52100 bearing steel with CBN tool. The cutting forces and surface roughness are measured at the end of useful tool life. The combined effects of the process parameters on performance characteristics are investigated using ANOVA. The composite desirability optimization technique associated with the RSM quadratic models is used as multi-objective optimization approach. The results show that feed rate and cutting speed strongly influence surface roughness and tool life. However, the depth of cut exhibits maximum influence on cutting forces. The proposed experimental and statistical approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes.     

Optimization of turning parameters for surface roughness and tool life based on the Taguchi method

In this study, the effect and optimization of machining parameters on surface roughness and tool life in a turning operation was investigated by using the Taguchi method. The experimental studies were conducted under varying cutting speeds, feed rates, and depths of cut. An orthogonal array, the signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA) were employed to the study the performance characteristics in the turning of commercial Ti-6Al-4V alloy using CNMG 120408-883 insert cutting tools. The conclusions revealed that the feed rate and cutting speed were the most influential factors on the surface roughness and tool life, respectively. The surface roughness was chiefly related to the cutting speed, whereas the axial depth of cut had the greatest effect on tool life.     

Precision machining of an aluminum alloy piston reinforced with a cast iron insert

Bimetallic pistons consisting of aluminum alloy reinforced with a cast iron (CI) insert are used to reduce the weight and improve the wear resistance of pistons. A major problem with machining such bimetallic pistons is producing the desired shape with minimal cutting forces and without damaging the bonding registry. The objective of this paper is to determine the optimal cutting parameters (cutting speed, feed, and depth of cut) for turning bimetallic pistons. When machining, we wish to obtain optimal values of the cutting forces and a better surface integrity while maintaining the required surface finish. Experiments were conducted following Taguchi’s parameter design approach using a cubic boron nitride tool for the machining. The results indicate that the process parameters affected the mean and variance of the cutting force at the Al-CI interface of the piston. The Al-CI interface was examined using an ultrasonic piston bond tester after machining to assure the bond quality. The surface roughness of the components was measured with a surface roughness tester. A mathematical model was developed using the Systat 12.0 software package to establish the relationship between the input quantities (speed, feed, and depth of cut) and the output data (cutting force). The output data of the mathematical model were compared with the experimental results. The results from the Taguchi robust design concept were compared with the results obtained from a nonconventional Genetic Algorithm optimization technique.     

Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718

The heat-resistant super alloy material like Inconel 718 machining is an inevitable and challenging task even in modern manufacturing processes. This paper describes the genetic algorithm coupled with artificial neural network (ANN) as an intelligent optimization technique for machining parameters optimization of Inconel 718. The machining experiments were conducted based on the design of experiments full-factorial type by varying the cutting speed, feed, and depth of cut as machining parameters against the responses of flank wear and surface roughness. The combined effects of cutting speed, feed, and depth of cut on the performance measures of surface roughness and flank wear were investigated by the analysis of variance. Using these experimental data, the mathematical model and ANN model were developed for constraints and fitness function evaluation in the intelligent optimization process. The optimization results were plotted as Pareto optimal front. Optimal machining parameters were obtained from the Pareto front graph. The confirmation experiments were conducted for the optimal machining parameters, and the betterment has been proved.     

Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: An optimization approach

In present work performance of coated carbide tool was investigated considering the effect of work material hardness and cutting parameters during turning of hardened AISI 4340 steel at different levels of hardness. The correlations between the cutting parameters and performance measures like cutting forces, surface roughness and tool life, were established by multiple linear regression models. The correlation coefficients found close to 0.9, showed that the developed models are reliable and could be used effectively for predicting the responses within the domain of the cutting parameters. Highly significant parameters were determined by performing an Analysis of Variance (ANOVA). Experimental observations show that higher cutting forces are required for machining harder work material. These cutting forces get affected mostly by depth of cut followed by feed. Cutting speed, feed and depth of cut having an interaction effect on surface roughness. Cutting speed followed by depth of cut become the most influencing factors on tool life; especially in case of harder workpiece. Optimum cutting conditions are determined using response surface methodology (RSM) and the desirability function approach. It was found that, the use of lower feed value, lower depth of cut and by limiting the cutting speed to 235 and 144 m/min; while turning 35 and 45 HRC work material, respectively, ensures minimum cutting forces, surface roughness and better tool life.     

Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool

Hard turning with multilayer coated carbide tool has several benefits over grinding process such as, reduction of processing costs, increased productivities and improved material properties. The objective was to establish a correlation between cutting parameters such as cutting speed, feed rate and depth of cut with machining force, power, specific cutting force, tool wear and surface roughness on work piece. In the present study, performance of multilayer hard coatings (TiC/TiCN/Al₂O₃) on cemented carbide substrate using chemical vapor deposition (CVD) for machining of hardened AISI 4340 steel was evaluated. An attempt has been made to analyze the effects of process parameters on machinability aspects using Taguchi technique. Response surface plots are generated for the study of interaction effects of cutting conditions on machinability factors. The correlations were established by multiple linear regression models. The linear regression models were validated using confirmation tests. The analysis of the result revealed that, the optimal combination of low feed rate and low depth of cut with high cutting speed is beneficial for reducing machining force. Higher values of feed rates are necessary to minimize the specific cutting force. The machining power and cutting tool wear increases almost linearly with increase in cutting speed and feed rate. The combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness. Abrasion was the principle wear mechanism observed at all the cutting conditions.     

Multi-response optimization of machining parameters in hot turning using grey analysis

This paper envisages the multi-response optimization of machining parameters in hot turning of stainless steel (type 316) based on Taguchi technique. The workpiece heated with liquid petroleum gas flame burned with oxygen was machined under different parameters, i.e., cutting speed, feed rate, depth of cut, and workpiece temperature on a conventional lathe. The effect of cutting speed, feed rate, depth of cut, and workpiece temperature on surface roughness, tool life, and metal removal rate have been optimized by conducting multi-response analysis. From the grey analysis, a grey relational grade is obtained and based on this value an optimum level of cutting parameters has been identified. Furthermore, using analysis of variance method, significant contributions of process parameters have been determined. Experimental results reveal that feed rate and cutting speed are the dominant variables on multiple performance analysis and can be further improved by the hot turning process.     

Surface roughness and cutting forces modeling for optimization of machining condition in finish hard turning of AISI 52100 steel

An experimental investigation was conducted to analyze the effect of cutting parameters (cutting speed, feed rate and depth of cut) and workpiece hardness on surface roughness and cutting force components. The finish hard turning of AISI 52100 steel with coated Al₂O₃ + TiC mixed ceramic cutting tools was studied. The planning of experiment were based on Taguchi’s L₂₇ orthogonal array. The response table and analysis of variance (ANOVA) have allowed to check the validity of linear regression model and to determine the significant parameters affecting the surface roughness and cutting forces. The statistical analysis reveals that the feed rate, workpiece hardness and cutting speed have significant effects in reducing the surface roughness; whereas the depth of cut, workpiece hardness and feed rate are observed to have a statistically significant impact on the cutting force components than the cutting speed. Consequently, empirical models were developed to correlate the cutting parameters and workpiece hardness with surface roughness and cutting forces. The optimum machining conditions to produce the lowest surface roughness with minimal cutting force components under these experimental conditions were searched using desirability function approach for multiple response factors optimization. Finally, confirmation experiments were performed to verify the pertinence of the developed empirical models.     

Multi-objective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches

Optimization techniques using evolutionary algorithm (EA) are becoming more popular in engineering design and manufacturing activities because of the availability and affordability of high-speed computers. In this work, an attempt was made to solve multi-objective optimization problem in turning by using multi-objective differential evolution (MODE) algorithm and non-dominated sorting genetic algorithm(NSGA-II). Optimization in turning means determination of the optimal set of machining parameters to satisfy the objectives within the operational constraints. These objectives may be minimum tool wear, maximum metal removal rate or any weighted combination of both. The main machining parameters which are considered as variables of the optimization are cutting speed, feed rate, and depth of cut. The optimum set of these three input parameters is determined for a particular job-tool combination of EN24 steel and tungsten carbide during a single-pass turning which minimizes the tool wear and maximizes the metal removal rate after satisfying the constraints of temperature and surface roughness. The regression models, developed for tool wear, temperature, and surface roughness were used for the problem formulation. The non-dominated solution set obtained from MODE was compared with NSGA-II using the performance metrics and reported     

Improving surface integrity in finish machining of Inconel 718 alloy using intelligent systems

In machining of hard materials, surface integrity is one of the major customer requirements which comprise the study of the changes induced to the workpiece. Surface roughness and residual stress are often considered as the most significant indications of surface integrity. Inducing tensile residual stress during the machining processes is a critical problem which should be avoided or minimized to obtain better service quality and component life. This problem becomes more evident in the presence of rough machined surface because fatigue life of manufactured components might be decreased significantly. Inconel 718 superalloy is one of the hard materials used extensively in the aerospace industries. It is prone to tensile residual stress in machined surface. Thus, controlling and optimizing residual stress and surface roughness in machining of Inconel 718 are so needed. Intelligent techniques based on the predictive and optimization models can be used efficiently for this purpose. In this study, the optimal machining parameters including cutting speed, depth of cut, and feed rate were accessed by intelligent systems to evaluate the state of residual stress and surface roughness in finish turning of Inconel 718. The results of experiments and analyses indicated that implemented techniques in this work provided a robust framework for improving surface integrity in machining of Inconel 718 alloy. It was shown that cutting speed has more effect on surface integrity than other investigated parameters. Also, depth of cut and feed rate were found in the moderate range to obtain satisfactory state of tensile residual stress and surface roughness.     

Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis

This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicted values and the experimental values. This reveals that the prediction system established in this study produces satisfactory results, which are improved performance over other models in the literature. The enhanced method can be readily applied to different metal cutting processes with greater confidence.     

Modelling of dimensional and geometric error prediction in turning of thin-walled components

In die-mold manufacturing and aircraft industry, many components that have thin-walled features are produced by turning operation. The major problem encountered during internal or external turning is cutting force induced deflection of workpiece along the periphery as well as axial length of a component. The present research work aims to develop a mathematical model for estimating dimensional and geometric errors during turning of thin-walled hollow cylinder qualitatively and quantitatively. In the proposed model, a mechanistic approach which is semi-analytical in nature is followed to achieve accuracy of the predicting results. First of all, process geometry model for thin-wall turning is developed based on process geometry variables such as uncut chip thickness, actual feed per revolution, actual depth of cut, peripheral cutting speed, effective cutting area etc. Using these process geometry variables and mechanistic cutting constants, a force model of turning is developed to estimate the tangential and radial force components. Later on, based on the predicted forces, tool-workpiece combined deflection model is developed to estimate radial, diametric and various geometric errors of the turned surface. The developed models are able to predict radial, diametric and various geometric errors such as straightness, circularly and cylindricity errors without conducting expensive actual machining operation. Hence, the present study will be helpful to take care of precautionary measures for controlling of dimensional and geometric errors more efficiently and reliably. Therefore, an attempt has been made to provide a basic platform to machining practitioners and process planners for in-depth comprehension and characterization of dimensional and geometric errors of the entire turned surface for varying machining conditions.     

An experimental analysis and measurement of process performances in machining of nimonic C-263 super alloy

Nimonic C-263 alloy is extensively used in the fields of aerospace, gas turbine blades, power generators and heat exchangers because of its unique properties. However, the machining of this alloy is difficult due to low thermal conductivity and work hardening characteristics. This paper presents the experimental investigation and analysis of the machining parameters while turning the nimonic C-263 alloy, using whisker reinforced ceramic inserts. The experiments were designed using Taguchi’s experimental design. The parameters considered for the experiments are cutting speed, feed rate and depth of cut. Process performance indicators, viz., the cutting force, tool wear and surface finish were measured. An empirical model has been created for predicting the cutting force, flank wear and surface roughness through response surface methodology (RSM). The desirability function approach has been used for multi response optimization. The influence of the different parameters and their interactions on the cutting force, flank wear and surface roughness are also studied in detail and presented in this study. Based on the cutting force, flank wear and surface roughness, optimized machining conditions were observed in the region of 210 m/min cutting speed and 0.05 mm/rev feed rate and 0.50 mm depth of cut. The results were confirmed by conducting further confirmation tests.     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

Optimization of cutting conditions during continuous finished profile machining using non-traditional techniques

Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Many researchers have dealt with the optimization of machining parameters for turning operations with constant diameters only. All Computer Numerical Control (CNC) machines produce the finished components from the bar stock. Finished profiles consist of straight turning, facing, taper and circular machining.This research work concentrates on optimizing the machining parameters for turning cylindrical stocks into continuous finished profiles. The machining parameters in multi-pass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost.In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost subject to a set of practical constraints. The constraints considered in this problem are cutting force, power constraint, tool tip temperature, etc. Due to high complexity of this machining optimization problem, six non-traditional algorithms, the genetic algorithm (GA), simulated annealing algorithm (SA), Tabu search algorithm (TS), memetic algorithm (MA), ants colony algorithm (ACO) and the particle swarm optimization (PSO) have been employed to resolve this problem. The results obtained from GA, SA,TS, ACO, MA and PSO are compared for various profiles. Also, a comprehensive user-friendly software package has been developed to input the profile interactively and to obtain the optimal parameters using all six algorithms. New evolutionary PSO is explained with an illustration .     

Multi-response optimization of machining parameters on red mud-based aluminum metal matrix composites in turning process

This paper presents the findings of an experimental investigation into the effects of cutting speed, feed rate, depth of cut, and nose radius in computer numerical control (CNC) turning operation performed on red mud-based aluminum metal matrix composites. This paper investigates optimization design of a turning process performed on red mud-based aluminum metal matrix composites. The major performance characteristics selected to evaluate the process are surface roughness, power consumption, and vibration, and the corresponding turning parameters are cutting speed, feed, depth of cut, and nose radius. Taguchi-based grey analysis, which uses grey relational grade as performance index, is specifically adopted to determine the optimal combination of turning parameters. The principal component analysis (PCA) is applied to evaluate the weighting values corresponding to various performance characteristics. L9 orthogonal array design has been used for conducting the experiments. The outcome of confirmation experiments reveals that grey relational analysis coupled with PCA can effectively be used to obtain the optimal combination of turning parameters. Hence, this confirms that the proposed approach in this study can be a useful tool to improve the turning performance of red mud-based aluminum metal matrix composites in CNC turning process.     

Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method

This study focuses on optimizing turning parameters based on the Taguchi method to minimize surface roughness (Ra and Rz). Experiments have been conducted using the L9 orthogonal array in a CNC turning machine. Dry turning tests are carried out on hardened AISI 4140 (51 HRC) with coated carbide cutting tools. Each experiment is repeated three times and each test uses a new cutting insert to ensure accurate readings of the surface roughness. The statistical methods of signal to noise ratio (SNR) and the analysis of variance (ANOVA) are applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. Results of this study indicate that the feed rate has the most significant effect on Ra and Rz. In addition, the effects of two factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appear to be important. The developed model can be used in the metal machining industries in order to determine the optimum cutting parameters for minimum surface roughness.     

Robust weighting applied to optimization of AISI H13 hardened-steel turning process with ceramic wiper tool: A diversity-based approach

A mixed ceramic is a type of cutting-tool material widely employed for machining hardened steels. The usage of a mixed ceramic along with a wiper geometry can help double the feed rate, thereby increasing productivity while keeping the surface roughness (Ra) as low as possible. Analyses of manufacturing processes, such as a machining process, show that the various possible controlled parameters can be modeled by multiobjective mathematical models to ensure their optimization. Hence, the aim of this study was optimize a hard turning process using a robust weighting based on diversity to choose the final Pareto optimal solution of the multiobjective problem. The responses of the material removal rate (MRR), Ra parameter, and cutting force (Fc) were modeled by using the response surface methodology; in this methodology, decision variables, such as the cutting speed (Vc), feed rate (f), and depth of cut (d), are employed. The diversity index, as a decision-making criterion, proved to be useful in mapping the regions of minimum variance within the Pareto optimal responses obtained in the optimization process. Hence, the study demonstrates that the weights used in the multiobjective optimization process influence the prediction variance of the obtained response.