Investigation of wire electrical discharge machining characteristics of Al6063/SiCp composites

In this investigation, the effect of wire electrical discharge machining (WEDM) parameters such as pulse-on time (T _ON), pulse-off time (T _OFF), gap voltage (V) and wire feed (F) on material removal rate (MRR) and surface roughness (R _a) in metal matrix composites (MMCs) consisting of aluminium alloy (Al6063) and silicon carbide (SiC_p) is discussed. The Al6063 is reinforced with SiC_p in the form of particles with 5%, 10% and 15% volume fractions. The experiments are carried out as per design of experiments approach using L₉ orthogonal array. The results were analysed using analysis of variance and response graphs. The results are also compared with the results obtained for unreinforced Al6063. From this study, it is found that different combinations of WEDM process parameters are required to achieve higher MRR and lower R _a for Al6063 and composites. Generally, it is found that the increase in volume percentage of SiC resulted in decreased MRR and increased R _a. Regression equations are developed based on the experimental data for the prediction of output parameters for Al6063 and composites. The results from this study will be useful for manufacturing engineers to select appropriate WEDM process parameters to machine MMCs of Al6063 reinforced with SiC_p at various proportions.     

A study on fiber laser micro-spot welding of thin stainless steel using response surface methodology and simulated annealing approach

This study analyzed variations of shear strength that depend on the fiber laser process during micro-spot welding of AISI 304 stainless thin sheets. A preliminary study used ANSYS results to obtain initial process conditions. The experimental plan was based on a Taguchi orthogonal array table. A hybrid method that includes the response surface methodology (RSM)- and back propagation neural network (BPNN)- integrated simulated annealing algorithm (SAA) is proposed to search for an optimal parameter setting of the micro-spot welding process. In addition, an analysis of variance was implemented to identify significant factors influencing the micro-spot welding process parameters, which was also used to compare the results of BPNN-integrated SAA with the RSM approach. The results show that the RSM and BPNN/SAA methods are both effective tools for the optimization of micro-spot welding process parameters. A confirmation experiment was also conducted in order to validate the optimal welding process parameter values.     

Experimental modeling and multi-objective optimization of traveling wire electrochemical spark machining (TW-ECSM) process

The present paper attempts to focus an application of a hybrid methodology comprising of Taguchi methodology (TM) coupled with response surface methodology (RSM) for modeling and TM coupled with weighted principal component (WPC) methodology for multiobjective optimization of a self developed traveling wire electro-chemical spark machining (TW-ECSM) process. First optimum level of input parameters is found using TM which is used as the central values in RSM to develop the second-order response model. For multiobjective optimization two quality characteristics surface roughness (R_a) and material removal rate (MRR), which are of opposite nature (R_a is the lower-the-better type, while MRR is the higher-the-better type), have been selected. The WPC is employed for the calculation of weight corresponding to each quality characteristic. The results indicate that the hybrid approaches applied for modeling and optimization of the TW-ECSM process are reasonable.     

Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology

A study on the radial-mode abrasive waterjet turning (AWJT) of 96 % alumina ceramic is presented and discussed. An experimental investigation is carried out to explore the influence of process parameters (including water pressure, jet feed speed, abrasive mass flow rate, surface speed, and nozzle tilted angle) on the material removal rate (MRR) when turning 96 % alumina ceramic. The experiments are conducted on the basis of response surface methodology (RSM) and sequential approach using face-centered central composite design. The quadratic model of RSM associated with the sequential approximation optimization (SAO) method is used to find optimum values of process parameters in terms of surface roughness and MRR. The results show that the MRR is influenced principally by the water pressure P and the next is abrasive mass flow rate m _a . The optimization results show that the MRR can be improved without increasing the surface roughness when machining 96 % alumina ceramic in the radial-mode abrasive waterjet turning process.     

Analysis and optimization of micro-geometry of miniature spur gears manufactured by wire electric discharge machining

This paper reports about the analysis and optimization of micro-geometry parameters (i.e. total profile deviation ‘F_a’ and accumulated pitch deviation ‘F_p’) of the wire electric discharge machined (WEDMed) fine-pitch miniature spur gears made of brass. Effects of four WEDM process parameters namely voltage, pulse-on time, pulse-off time and wire feed rate on the micro-geometry of the miniature gears were analyzed by conducting the experiments designed using Box–Behnken approach of response surface methodology (RSM). Analysis of variance study found all four input parameters significant. Larger deviations in profile and pitch were observed with higher values of the voltage and pulse-on time, and with lower values of wire feed rate and pulse-off time. Multi-performance optimization of WEDM parameters was done using the desirability analysis to minimize profile deviation and pitch deviation simultaneously. The values of F_a and F_p of the gear obtained by the confirmation experiment conducted at the optimized WEDM parameters were as 11.5 μm and 9.1 μm respectively. These values categorize the WEDMed gear having DIN quality number as 7 and 5 respectively for profile and pitch which are better than those obtained by the conventional miniature gear manufacturing processes.     

Study on the nano-powder-mixed sinking and milling micro-EDM of WC-Co

Present study investigates the feasibility of improving surface characteristics in the micro-electric discharge machining (EDM) of cemented tungsten carbide (WC?CCo), a widely used die and mould material, using graphite nano-powder-mixed dielectric. In this context, a comparative analysis has been carried out on the performance of powder-mixed sinking and milling micro-EDM with view of obtaining smooth and defect-free surfaces. The surface characteristics of the machined carbide were studied in terms of surface topography, crater characteristics, average surface roughness (R _a) and peak-to-valley roughness (R _max). The effect of graphite powder concentration on the spark gap, material removal rate (MRR) and electrode wear ratio (EWR) were also discussed for both die-sinking and milling micro-EDM of WC?CCo. It has been observed that the presence of semi-conductive graphite nano-powders in the dielectric can significantly improve the surface finish, enhance the MRR and reduce the EWR. Both the surface topography and crater distribution were improved due to the increased spark gap and uniform discharging in powder-mixed micro-EDM. The added nano-powder can lower the breakdown strength and facilitate the ignition process thus improving the MRR. However, for a fixed powder material and particle size, all the performance parameters were found to vary significantly with powder concentration. Among the two processes, powder-mixed milling micro-EDM was found to provide smoother and defect-free surface compared to sinking micro-EDM. The lowest value of R _a (38?nm) and R _max (0.17???m) was achieved in powder-mixed milling micro-EDM at optimum concentration of 0.2?g/L and electrical setting of 60?V and stray capacitance.     

Effect of electrode material in wire electro discharge machining characteristics of Ti50Ni50−xCux shape memory alloy

TiNiCu alloy belongs to new class of shape memory alloy (SMA), which exhibits superior properties like shape memory effect, super elasticity and reversible martensitic transformation phase and thus find broad applications in actuators, micro tools and stents in biomedical components. Even though, SMA demonstrates outstanding property profile, traditional machining of SMAs is fairly complex and hence non-traditional machining like wire electric discharge machining (WEDM) has been performed. Hence, there is a need to investigate the WEDM performance characteristics of shape memory alloys due to excellent property profile and potential applications. In the present investigation, various machining characteristics like material removal rate (MRR), surface roughness, surface topography and metallographic changes have been studied and the influence of wire material on TiNiCu alloy machining characteristics has also been evaluated through ANOVA. Ti₅₀Ni_50−xCu_{x=10, 20} was prepared by vacuum arc melting process. The proposed alloy as-cast material exhibits austenite property (B2 phase) and having higher hardness when compared to TiNi alloy. The investigation on WEDM of Ti₅₀Ni_50−xCu_x alloy reveals that the machining parameters such as servo voltage, pulse on time and pulse off time are the most significant parameters affecting MRR as well as surface roughness using both brass and zinc coated brass wires. However, machining with zinc coated brass wire yields reduced surface roughness and better MRR and also produces less surface defects on the machined surface of Ti₅₀Ni_50−xCu_x alloys.     

Comparative study of conventional and micro WEDM based on machining of meso/micro Sized Spur Gear

This paper discusses the comparison of micro machining process using conventional and micro wire electrical discharge machining (WEDM) for fabrication of miniaturized components. Seventeen toothed miniaturized spur gear of 3.5 and 1.2 mm outside diameter were fabricated by conventional and micro WEDM respectively. The process parameters for both conventional and micro WEDM were optimized by preliminary experiments and analysis. The gears were investigated for the quality of surface finish and dimensional accuracy which were used as the criteria for the process evaluation. An average surface roughness (R_a) of 50 nm and dimensional accuracy of 0.1–1 μm were achieved in micro WEDM. Whenever applied conventional WEDM for meso/micro fabrication, a R_a surface roughness of 1.8 μm and dimensional accuracy of 2–3 μm were achieved. However, this level of surface roughness and dimensional accuracy are acceptable in many applications of micro engineering. A window of conventional WEDM consisting of low energy discharge parameters is identified for micromachining.     

An experimental investigation on machining parameters of AISI D2 steel using WEDM

The performance of the wire electrodischarge machining (WEDM) machining process largely depends upon the selection of the appropriate machining variables. Optimization is one of the techniques used in manufacturing sectors to arrive for the best manufacturing conditions, which are essential for industries toward manufacturing of quality products at lowest cost. As there are many process variables involved in the WEDM machining process, it is difficult to choose a proper combination of these process variables in order to maximize material removal rate and to minimize tool wear and surface roughness. The objective of the this work is to investigate the effects of process variables like pulse on time, pulse off time, peak current, servo voltage, and wire feed on material removal rate (MRR), surface roughness (SR), gap voltage, gap current, and cutting rate in the WEDM machining process. The experiment has been done using Taguchi’s orthogonal array L27 (3⁵). Each experiment was conducted under different conditions of input parameters and statistically evaluated the experimental data by analysis of variance (ANOVA) using MINITAB and Design Expert tools. The present work also aims to develop mathematical models for correlating the inter-relationships of various WEDM machining parameters and performance parameters of machining on AISI D2 steel material using response surface methodology (RSM).The significant machining parameters and the optimal combination levels of machining parameters associated with performance parameters were also drawn. The observed optimal process parameter settings based on composite desirability (61.4 %) are pulse on time 112.66 μs, pulse off time 45 μs, spark gap voltage 46.95 V, wire feed 2 mm/min, peak current of 99.99 A for achieving maximum MRR, gap current, gap voltage, cutting rate, and minimum SR; finally, the results were experimentally verified.     

Grinding-aided electrochemical discharge machining of particulate reinforced metal matrix composites

A grinding-aided electrochemical discharge machining (G-ECDM) process has been developed to improve the performance of the conventional ECDM process in machining particulate reinforced metal matrix composites (MMCs). The G-ECDM process functions under a combined action of electrochemical dissolution, spark erosion, and direct mechanical grinding. The tool electrode has a coating containing a hard reinforcement phase of diamond particles. The MMC employed in this study was Al₂O₃ particulate reinforced aluminum 6061 alloy. The material removal mechanism of this hybrid process has been analyzed. The results showed that the grinding action can effectively remove re-cast material deposited on the machining surface. The surface roughness (R _a) measured for the G-ECDM specimen was ten times smaller than that of the specimen machined without grinding aid (i.e., ECDM alone). Moreover, the material removal rate (MRR) of G-ECDM was about three times higher than that of ECDM under the experimental conditions of this study. The voltage waveform and crater distribution were also analyzed, and the experimental results showed that the G-ECDM process operates in a stable condition. The relative importance of the various processing parameters on MRR was established using orthogonal analysis. The results showed that MRR is influenced by the machining parameters in the order of duty cycle?>?current?>?electrolyte concentration. This study showed that the G-ECDM process is superior to the ECDM process for machining particulate reinforced MMCs, where a higher machining efficiency and a better surface quality can be obtained.     

An integrated optimization of cutting parameters and tool path generation in ultraprecision raster milling

This paper provides a new methodology for the integrated optimization of cutting parameters and tool path generation (TPG) based on the development of prediction models for surface roughness and machining time in ultraprecision raster milling (UPRM). The proposed methodology simultaneously optimizes the cutting feed rate, the path interval, and the entry distance in the feed direction to achieve the best surface quality in a given machining time. Cutting tests are designed to verify the integrated optimization methodology. The experimental results show that, in the fabrication of plane surface, the changing of entry distance improves surface finish about 40 nm (R _a ) and 200 nm (R _t ) in vertical cutting and decreases about 8 nm (R _a ) and 35 nm (R _t ) in horizontal cutting with less than 2 s spending extra machining time. The optimal shift ratio decreases surface roughness about 7 nm (R _a ) and 26 nm (R _t ) in the fabrication of cylinder surfaces, while the total machining time only increases 2.5 s. This infers that the integrated optimization methodology contributes to improve surface quality without decreasing the machining efficiency in ultraprecision milling process.     

Self-tuning fuzzy control with a grey prediction for wire rupture prevention in WEDM

Wire rupture in the wire electrical discharge machining (WEDM) process is one of the most troublesome problems in practical applications. In this paper, the abnormal ratio R_ab, defined as the proportion of abnormal sparks in a sampling period, is taken to represent the gap state in machining. The grey predictor is adopted to compensate the time-delayed R_ab caused by the low pass filter data processing. A gain self-tuning fuzzy control system has been developed to cope with the conditions that often occur with wire rupture in the WEDM process, such as an improper setting of machining parameters, machining the workpiece with varying thickness, etc. Experimental results of several cases show that the proposed controller results in a satisfactory performance. Not only can it immediately suppress transient situation once there is a sudden change of workpiece thickness, but a stable performance can also be achieved during machining a workpiece of constant thickness. As a result, wire rupture problems in most WEDM processes can be successively solved by the proposed control strategy.     

Experimental investigation and parameter optimization of near-dry wire-cut electrical discharge machining using multi-objective evolutionary algorithm

The near-dry wire-cut electrical discharge machining (WEDM) process is an environment-friendly manufacturing process, in which there is no harmful effect to the operators. The authors focus on the non-polluting ways to cut the materials and to meet the technical requirements like high material removal rate (MRR) and low surface roughness (Ra). In the near-dry WEDM, the finite discrete periodic series sparks between the wire electrode and conducting work material separated by minimum quantity of deionized water mixed with compressed air (air-mist) as a dielectric medium. In the present research, parametric analysis of the process has been performed with the molybdenum wire tool and high speed steel (HSS-M2) work piece. Experiments have been performed using air-mist as the dielectric medium to study the impact of gap voltage, pulse-on time, pulse-off time, air-mist pressure and discharge current on the MRR and Ra using the mixed orthogonal (L₁₈) array-Taguchi method. Taguchi based analysis of variance test was performed to identify the significant parameters. The gap voltage, pulse-on time, discharge current and air-mist pressure were found to have momentous effects on MRR and Ra. The best regression models for MRR and Ra have been developed by regression analysis. The optimal rough and finish cutting parameters have been predicted by Pareto-front using the multi-objective evolutionary algorithm (MOEA).     

A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology

This study analyzed variations of mechanical characteristics that depend on the injection molding techniques during the blending of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites. A hybrid method including back-propagation neural network (BPNN), genetic algorithm (GA), and response surface methodology (RSM) are proposed to determine an optimal parameter setting of the injection molding process. The specimens are prepared under different injection molding processing conditions based on a Taguchi orthogonal array table. The results of 18 experimental runs were utilized to train the BPNN predicting ultimate strength, flexural strength, and impact resistance. Simultaneously, the RSM and GA approaches were individually applied to search for an optimal setting. In addition, the analysis of variance was implemented to identify significant factors for the injection molding process parameters and the result of BPNN integrating GA was also compared with RSM approach. The results show that the RSM and BPNN/GA methods are both effective tools for the optimization of injection molding process parameters.     

Soft computing models and intelligent optimization system in electro-discharge machining of SiC/Al composites

In this paper, a multi-variable regression model, a back propagation neural network (BPNN) and a radial basis neural network (RBNN) have been utilized to correlate the cutting parameters and the performance while electro-discharge machining (EDM) of SiC/Al composites. The four cutting parameters are peak current (I_p), pulse-on time (T_on), pulse-off time (T_off), and servo voltage (S_v); the performance measures are material remove rate (MRR) and surface roughness (Ra). By testing a large number of BPNN architectures, 4-5-1 and 4-7-1 have been found to be the optimal one for MRR and Ra, respectively; and it can predict them with 10.61 % overall mean prediction error. As for RBNN architectures, it can predict them with 12.77 % overall mean prediction error. The multivariable regression model yields an overall mean prediction error of 13.93 %. All of these three models have been used to study the effect of input parameters on the material remove rate and surface roughness, and finally to optimize them with genetic algorithm (GA) and desirability function. Then, an intelligent optimization system with graphical user interface (GUI) has been built based on these multi-optimization techniques, in which users can obtain the optimized cutting parameters under the desired surface roughness (Ra).     

Modelling and analysis of hybrid electrochemical turning-magnetic abrasive finishing of 6061 Al/Al2O3 composite

This paper integrates the electrochemical turning (ECT) process and magnetic abrasive finishing (MAF) to produce a combined process that improves the material removal rate (MRR) and reduces surface roughness (SR). The present study emphasizes the features of the development of comprehensive mathematical models based on response surface methodology (RSM) for correlating the interactive and higher-order influences of major machining parameters, i.e. magnetic flux density, applied voltage, tool feed rate and workpiece rotational speed on MRR and SR of 6061 Al/Al₂O₃ (10% wt) composite. The paper also highlights the various test results that also confirm the validity and correctness of the established mathematical models for in-depth analysis of the effects of hybrid ECT- MAF process parameters on metal removal rate and surface roughness. Further, optimal combination of these parameters has been evaluated and it can be used in order to maximize MRR and minimize SR. The results demonstrate that assisting ECT with MAF leads to an increase machining efficiency and resultant surface quality significantly, as compared to that achieved with the traditional ECT of some 147.6% and 33%, respectively.     

Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression

In this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (R _a) and total (R _t) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding R _a and R _t values for each case are measured. Input–output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.     

Simulation of surface generated during abrasive flow finishing of Al/SiCp-MMC using neural networks

Abrasive flow machining (AFM) is a multivariable finishing process which finds its use in difficult to finish surfaces on difficult to finish materials. Near accurate prediction of generated surface by this process could be very useful for the practicing engineers. Conventionally, regression models are used for such prediction. This paper presents the use of artificial neural networks (ANN) for modeling and simulation of response characteristics during AFM process in finishing of Al/SiC_p metal matrix composites (MMCs) components. A generalized back-propagation neural network with five inputs, four outputs, and one hidden layer is designed. Based upon the experimental data of the effects of AFM process parameters, e.g., abrasive mesh size, number of finishing cycles, extrusion pressure, percentage of abrasive concentration, and media viscosity grade, on performance characteristics, e.g., arithmetic mean value of surface roughness (R _a, micrometers), maximum peak–valley surface roughness height (R _t, micrometers), improvement in R _a (i.e., ΔR _a), and improvement in R _t (i.e., ΔR _t), the networks are trained for finishing of Al/SiC_p-MMC cylindrical components. ANN models are compared with multivariable regression analysis models, and their prediction accuracy is experimentally validated.     

Optimum selection of abrasive flow machining conditions during fine finishing of Al/15 wt% SiC-MMC using Taguchi method

Abrasive flow machining (AFM) is gaining widespread application finishing process on difficult to reach surfaces in aviation, automobile, and tooling industry. Al/SiC_p-MMC is a promising material in these industries. Here, AFM has been used to finish conventionally machined cylindrical surface of Al/15 wt% SiC_p-MMC workpiece. This paper presents the utilization of robust design-based Taguchi method for optimization of AFM parameters. The influences of AFM process parameters on surface finish and material removal have been analyzed. Taguchi experimental design concept, L₁₈ (6¹?×?3⁷) mixed orthogonal array is used to determine the S/N ratio and optimize the AFM process parameters. Analysis of variance and F test values also indicates the significant AFM parameters affecting the finishing performance. The mathematical models for R _a, R _t, ΔR _a, and ΔR _t and material removal are established to investigate the influence of AFM parameters. Conformation test results verify the effectiveness of these models and optimal parametric combination within the considered range. Scanning electron micrographs testifies the effectiveness of AFM process in fine finishing of Al/15 wt% SiC_p-MMC.     

Experimental investigation and economic analysis of surfactant (Span-20) in powder mixed electrical discharge machining (PMEDM) of AISI D2 hardened steel

Abstract

Powder mixed EDM (PMEDM) is recognized as an advanced and innovative technique with enhanced performance and limited drawbacks in comparison to conventional EDM method. This study investigates the effect of powder particle size, various powder concentrations (C_p), and surfactant concentrations (C_s) on the performance of EDM. Since the machining characteristics are highly dependent on the dielectric performances, significant attention has been directed to introduce Cr powder and Span-20 surfactant into the dielectric fluid to achieve higher productivity and enhanced surface integrity. The EDM machining was carried out on AISI D2 hardened steel through ´Plug & Plaý dielectric circulating system attached to the main machine in order to evaluate the machining performances (i.e. MRR, EWR, and R_a). Interestingly, machining performance was improved with combination of Cr powder mixed and span-20 surfactant. By comparing the performance of span-20 surfactant and micro-nano chromium, the result within selected parameters shows that the span-20 surfactant and nano-chromium is the better choice for the EDM of AISI D2 hardened steel. In the machinability studies, the EDM machining of AISI D2 hardened steel by using span-20 surfactant and nano-chromium has exhibited the excellent machining performances, which led to 45.08% MRR enhancement and 68.89% R_a enhancement comparing to micro-chromium powder and span-20 surfactant led to 35.28% MRR and 28.96% R_a. Furthermore, cost analysis revealed that the nano-Cr powder size was approximately 4 times more economical than micro-Cr powder in machining of AISI D2 hardened steel, although the price for 1?kg is quite expensive.