MACHINING STUDIES ON BIMETALLIC PISTONS WITH CBN TOOL USING THE TAGUCHI METHOD – TECHNICAL COMMUNICATION

In order to combine the advantages of weight reduction and wear resistance, bimetallic pistons are used. Aluminum alloy is reinforced with cast iron insert to realize the bimetallic pistons. Nevertheless, as far as machinability is concerned, achieving the near net shape of the bimetallic pistons without damaging the bonding between the aluminum and cast iron is the major crisis. The bond integrity after machining is primarily related to the magnitude of the cutting forces during machining and thus the objective of the paper is to obtain optimal cutting parameters in turning of such pistons. In addition, any machining process should also satisfy surface finish requirements. A Taguchi analysis of the influence of cutting speed, feed, and depth of cut on cutting force were conducted and the extent of debonding and the surface finish was measured independently after machining to ensure that this minimum cutting force condition fully satisfied all the product requirements.     

Machining and simulation studies of bimetallic pistons

Light aluminium alloy piston is suitably reinforced at high load-bearing region with cast iron insert, and machining of such bimetallic material is more difficult with a single cutting tool material. Present study focuses on the orthogonal cutting of bimetallic material machining using cubic boron nitride as a cutting tool through finite element analysis. The effects of cutting parameters such as cutting velocity, feed rate and depth of cut on resultant cutting forces and the surface roughness were analysed. Those parameters yielding minimum cutting forces were identified as minimal cutting force parameters, so numerical simulation and experiments were carried out on these parameters. After machining, the intermediate bonding between metallic regions was studied using ultrasonic testing. Bimetallic machining is successfully simulated, and its potential is readily applied to an industrially important component.     

Investigations of flank wear, cutting force, and surface roughness in the machining of Al-6061�CTiB2 in situ metal matrix composites produced by flux-assisted synthesis

This paper presents the results of an experimental investigation on the machinability of in situ Al-6061?CTiB₂ metal matrix composite (MMC) prepared by flux-assisted synthesis. These composites were characterized by scanning electron microscopy, X-ray diffraction, and micro-hardness analysis. The influence of reinforcement ratio of 0, 3, 6, and 9?wt.% of TiB₂ on machinability was examined. The effect of machinability parameters such as cutting speed, feed rate, and depth of cut on flank wear, cutting force and surface roughness were analyzed during turning operations. From the test results, we observe that higher TiB₂ reinforcement ratio produces higher tool wear, surface roughness and minimizes the cutting forces. When machining the in situ MMC with high speed causes rapid tool wear due to generation of high temperature in the machining interface. The rate of flank wear, cutting force, and surface roughness are high when machining with a higher depth of cut. An increase in feed rate increases the flank wear, cutting force and surface roughness.     

Optimization techniques for machining operations: a retrospective research based on various mathematical models

Simulated annealing, genetic algorithm, and particle swarm optimization techniques have been used for exploring optimal machining parameters for single pass turning operation, multi-pass turning operation, and surface grinding operation. The behavior of optimization techniques are studied based on various mathematical models. The objective functions of the various mathematical models are distinctly different from each other. The most affecting machining parameters are considered as cutting speed, feed, and depth of cut. Physical constraints are speed, feed, depth of cut, power limitation, surface roughness, temperature, and cutting force.     

An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish

High-speed machining (HSM) has emerged as a key technology in rapid tooling and manufacturing applications. Compared with traditional machining, the cutting speed, feed rate has been great progress, and the cutting mechanism is not the same. HSM with coated carbide cutting tools used in high-speed, high temperature situations and cutting more efficient and provided a lower surface roughness. However, the demand for high quality focuses extensive attention to the analysis and prediction of surface roughness and cutting force as the level of surface roughness and the cutting force partially determine the quality of the cutting process. This paper presents an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness. Taguchi optimization method is the most effective method to optimize the machining parameters, in which a response variable can be identified. The standard orthogonal array of L₉ (3⁴) was employed in this research work and the results were analyzed for the optimization process using signal to noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA) to identify the most significant parameters affecting the cutting forces and surface roughness. For such application, several machining parameters are considered to be significantly affecting cutting forces and surface roughness. These parameters include the lubrication modes, feed rate, cutting speed, and depth of cut. Finally, conformation tests were carried out to investigate the improvement of the optimization. The result showed a reduction of 25.5% in the cutting forces and 41.3% improvement on the surface roughness performance.     

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.     

Effect of machining parameters on the milling process of 2.5D C/SiC ceramic matrix composites

Abstract

The C/SiC ceramic matrix composites are widely used for high-value components in the nuclear, aerospace and aircraft industries. The cutting mechanism of machining C/SiC ceramic matrix composites is one of the most challenging problems in composites application. Therefore, the effects of machining parameters on the machinability of milling 2.5D C/SiC ceramic matrix composites is are investigated in this article. The related milling experiments has been carried out based on the C/SiC ceramic matrix composites fixed in two different machining directions. For two different machining directions, the influences of spindle speed, feed rate and depth of cut on cutting forces and surface roughness are studied, and the chip formation mechanism is discussed further. It can be seen from the experiment results that the measured cutting forces of the machining direction B are greater than those of the in machining direction A under the same machining conditions. The machining parameters, which include spindle speed, feed rate, depth of cut and machining direction, have an important influence on the cutting force and surface roughness. This research provides an important guidance for improving the machining efficiency, controlling and optimizing the machined surface quality of C/SiC ceramic matrix composites in the milling process.     

An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design

Many previous researches on high-speed machining have been conducted to pursue high machining efficiency and accuracy. In the present study, the characteristics of cutting forces, surface roughness, and chip formation obtained in high and ultra high-speed face milling of AISI H13 steel (46–47 HRC) are experimentally investigated. It is found that the ultra high cutting speed of 1,400?m/min can be considered as a critical value, at which relatively low mechanical load, good surface finish, and high machining efficiency are expected to arise at the same time. When the cutting speed adopted is below 1,400?m/min, the contribution order of the cutting parameters for surface roughness Ra is axial depth of cut, cutting speed, and feed rate. As the cutting speed surpasses 1,400?m/min, the order is cutting speed, feed rate, and axial depth of cut. The developing trend of the surface roughness obtained at different cutting speeds can be estimated by means of observing the variation of the chip shape and chip color. It is concluded that when low feed rate, low axial depth of cut, and cutting speed below 1,400?m/min are adopted, surface roughness Ra of the whole machined surface remains below 0.3?μm, while cutting speed above 1,400?m/min should be avoided even if the feed rate and axial depth of cut are low.     

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.     

Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM

The results of mathematical modeling and the experimental investigation on the machinability of aluminium (Al6061) silicon carbide particulate (SiCp) metal matrix composite (MMC) during end milling process is analyzed. The machining was difficult to cut the material because of its hardness and wear resistance due to its abrasive nature of reinforcement element. The influence of machining parameters such as spindle speed, feed rate, depth of cut and nose radius on the cutting force has been investigated. The influence of the length of machining on the tool wear and the machining parameters on the surface finish criteria have been determined through the response surface methodology (RSM) prediction model. The prediction model is also used to determine the combined effect of machining parameters on the cutting force, tool wear and surface roughness. The results of the model were compared with the experimental results and found to be good agreement with them. The results of prediction model help in the selection of process parameters to reduce the cutting force, tool wear and surface roughness, which ensures quality of milling processes.     

Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology

The hard turning process has been attracting interest in different industrial sectors for finishing operations of hard materials. In this paper, the effects of cutting speed, feed rate, and depth of cut on surface roughness, cutting force, specific cutting force, and power in the hard turning were experimentally investigated. An experimental investigation was carried out using ceramic cutting tools, composed approximately with (70 %) of Al₂O₃ and (30 %) of TiC, in surface finish operations on cold work tool steel AISI D3 heat-treated to a hardness of 60 HRC. Based on 3³ full factorial designs, a total of 27 tests were carried out. The range of each parameter is set at three different levels, namely, low, medium, and high. Analysis of variance is used to check the validity of the model. Experimental observations show that higher cutting forces are required for machining harder work material. This cutting force gets affected mostly by feed rate followed by depth of cut. Feed rate is the most influencing factor on surface roughness. Feed rate followed by depth of cut become the most influencing factors on power; 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 depth of cut value, higher cutting speed, and by limiting the feed rate to 0.12 and 0.13 mm/rev, while hard turning of AISI D3 hardened steel, respectively, ensures minimum cutting forces and better surface roughness. Higher values of depth of cut are necessary to minimize the specific cutting force.     

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.     

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.     

Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations

Optimization of cutting parameters is valuable in terms of providing high precision and efficient machining. Optimization of machining parameters for milling is an important step to minimize the machining time and cutting force, increase productivity and tool life and obtain better surface finish. In this work a mathematical model has been developed based on both the material behavior and the machine dynamics to determine cutting force for milling operations. The system used for optimization is based on powerful artificial intelligence called genetic algorithms (GA). The machining time is considered as the objective function and constraints are tool life, limits of feed rate, depth of cut, cutting speed, surface roughness, cutting force and amplitude of vibrations while maintaining a constant material removal rate. The result of the work shows how a complex optimization problem is handled by a genetic algorithm and converges very quickly. Experimental end milling tests have been performed on mild steel to measure surface roughness, cutting force using milling tool dynamometer and vibration using a FFT (fast Fourier transform) analyzer for the optimized cutting parameters in a Universal milling machine using an HSS cutter. From the estimated surface roughness value of 0.71 μm, the optimal cutting parameters that have given a maximum material removal rate of 6.0×10³ mm³/min with less amplitude of vibration at the work piece support 1.66 μm maximum displacement. The good agreement between the GA cutting forces and measured cutting forces clearly demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results indicate that the optimized parameters are capable of machining the work piece more efficiently with better surface finish.     

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.     

Experimental investigations of cutting parameters influence on cutting forces in turning of Fe-based amorphous overlay for remanufacture

Fe-based amorphous alloy is a new-type material dedicated to the remanufacture due to its unique property. Fe-based amorphous alloy is deposited on the abrased, fatigued, and fractured surface for resuming and upgrading its performance. In the present research, properties of amorphous alloy overlay, such as the microstructure, the phase content, thermal behavior, and mechanical property were evaluated and its machinability with respect to machining forces was experimentally investigated. Based on the response surface methodology and Box–Behnken design, four-factor (cutting speed, feed, depth of cut, and rake angle) three-level experiments were applied and analysis of variance (ANOVA) was performed. It is found that depth of cut is the dominant cutting parameter that affects the machining force components. Rake angle and interaction of feed rate and depth of cut can provide secondary significance to machining forces. Cutting speed, alone, has insignificant influence on machining force components. Predicting model for machining forces is established. ANOVA indicates that a linear model best fits the radial force and while a quadratic model best describes the axial force and cutting force. The optimal cutting parameters under these experimental conditions are searched.     

EFFECT OF PROCESS PARAMETERS AND TOOL SHAPE ON THE MACHINABILITY OF A PARTICULATE FILLED-POLYMER COMPOSITE MATERIAL FOR RAPID TOOLING

This paper presents the results of an experimental study on the effects of machining parameters (cutting speed, feed, depth of cut) and tool shape on chip formation, surface topography, resultant cutting force and surface roughness produced in flat and ball end milling of the Ren Shape-Express 2000™ aluminum particulate filled-polymer composite material. This material is shown to exhibit a brittle-to-ductile transition in chip formation with decreasing cutting speed. The transition is explained by the strain-rate sensitivity of the polymer matrix and is found to correlate well with a corresponding change in the surface roughness. The absence of clear feed marks on the milled surface explains why molds made from the composite material require less hand polishing than machined metal molds. The influence of cutting conditions and tool shape (flat end vs. ball-nose) on the cutting force, surface roughness, and workpiece breakout are discussed and relevant comparisons with conventional metal and polymer machining are made.     

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.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

Integration of cutting force control and chatter suppression control into automatic cutting feed adjustment system design

Abstract

This study designed an automatic cutting feed adjustment system for computer numerical control (CNC) turning machine tools, which integrate the operational characteristics of cutting force control and chatter suppression control to shorten the machining time and maintain the quality of workpieces. The setting of appropriate machining conditions (such as cutting feed, spindle speed and depth of cut) to consider both machining quality and efficiency often causes difficulties for machine tool operators. Therefore, this study uses cutting force control to design an automatic cutting feed adjustment method for cutting tools, and then, the chatter suppression control design is used to modify the cutting force command to suppress cutting chatter. The experimental results of the CNC turning machine tool show that the use of the cutting force control to adjust the cutting feed can shorten the machining time; however, the cutting chatter results in larger surface waviness on the workpiece surface. When the cutting force command is properly modified by actuating the chatter suppression control, the workpiece shows better surface roughness with prolonged machining time. Therefore, the cutting tests demonstrate that the proposed system is feasible for satisfying the machining requirements of the manufacturing processes of mechanical parts for high speed and high accuracy.