Pragya Shandilya

Experimental Studies on Wire Electric Discharge Cutting of SiCp/6061 Aluminum Metal Matrix Composites

This paper reports about the experimental findings on the wire electric discharge cutting (WEDC) of 6061 aluminum metal matrix composite (MMC) reinforced with silicon carbide particulates (i.e. SiCp/6061 Al). Four WEDC parameters namely servo voltage, pulse-on time, pulse-off time and wire feed rate were varied to study their effect on the quality of cut in SiCp/6061 aluminum MMC using average cutting rate and microstructure of the cut surface as response parameters. The experiments were conducted using one-factor-at-a-time experiment strategy in which only one input parameter was varied while keeping all other input parameters as constant. The experimental results indicate that the average cutting speed is mainly affected by pulse-on time, pulse-off time, and voltage and the changes are more prominent for the MMC 7.5% SiCp. The characteristics of the surface produced by WEDC were analyzed by scanning electron microscopy (SEM). Analysis of SEM images of the cut surface has revealed that the depth of micro-voids and micro-cracks slightly increases as the voltage and pulse-on time increases and as pulse-off time and wire feed rate decreases. there seems to be trade-off between the average cutting speed and better quality of cut for Al-SiCp MMC as far the values of pulse-on time, pulse-off time are considered and final choice will depend upon the type of application. An optimum range of the input parameters has been bracketed as the final outcome of this work for carrying out further research to develop the models for WEDC of SiCp/6061 aluminum MMC and to optimize the WEDC parameters for the best quality of cut and to minimize the wire breakage frequency.

Modelling and process optimisation for wire electric discharge machining of metal matrix composites

This paper describes the process modelling and optimisation of wire electric discharge machining (WEDM) of SiCp/6061 Al metal matrix composite (MMC) through response surface methodology (RSM) and artificial neural network (ANN) approach. The experiments were planned and carried out based on the design of experiments (DOE). Four WEDM input process parameters namely servo voltage (SV), pulse-on time (TON), pulse-off time (TOFF) and wire feed rate (WF) were chosen as machining process parameters. Two response criteria [i.e., material removal rate (MRR) and cutting width (kerf)] were selected during optimisation. The analysis of variance (ANOVA) was carried out to study the effect of process parameters on response variables and models have also been developed for response parameters. RSM was used to determine the optimal values of input process parameters maximum MRR and minimum kerf. The output of the RSM model was used to develop the ANN predictive model. ANN model was validated through experimentation conducted at the RSM optimal setting of input parameters and results show that ANN predictive model and the actual experimental observations are very close to each other which give a good agreement between the two. Comparisons of ANN models and RSM models show that ANN predictions are more accurate than RSM predictions.

Experimental studies on electrical discharge wire cutting of Ni-rich NiTi shape memory alloy

ABSTRACT The experimental investigation explores the effect of electrical discharge wire cutting (EDWC) variable parameters such as spark gap voltage, wire tension, pulse off time, wire feed rate, and pulse on time on the surface roughness, average cutting rate, and metallographic changes of Ni55.95Ti44.05 shape memory alloy (SMA). The spark gap voltage, pulse off time, and pulse on time have the significant effect on the surface roughness and average cutting rate, whereas wire tension and wire feed rate have the trifling effect. Ni55.95Ti44.05 SMA’s surface after EDWC is characterized by many discharge craters, microcracks, voids, and white layer of resolidified molten material. The elemental composition analysis of white layer using energy-dispersive spectroscopy divulges the deposition of the foreign element from the brass wire as well as the dielectric on the surface after EDWC. The machined surface as well as the wire electrode surface consists of various compounds of Ti, Ni, Zn, and Cu which have been identified by X-ray diffraction peak analysis.

Study on Wire Electric Discharge Machining Based on Response Surface Methodology and Genetic Algorithm

Wire electric discharge machining (WEDM) is one of the most popular non-conventional machining processes for machining metal matrix composites (MMCs). The present research work deals the parametric optimization of the input process parameters for response parameter during WEDM of SiCp/6061 Al metal matrix composite (MMC). Response surface methodology (RSM) and genetic algorithm (GA) integrated with each other to optimize the process parameters. RSM has been used to plan and analyze the experiments. Four WEDM parameters namely servo voltage, pulse-on time, pulse-off time and wire feed rate were varied to study their effect on the quality of cut in SiCp/6061 Al MMC using cutting width (kerf) as response parameter. The relationship between kerf and machining parameters has been developed by using RSM. The mathematical model thus than developed was then employed on GA to optimized the process parameters.

Neural Network Based Modeling in Wire Electric Discharge Machining of SiCp/6061 Aluminum Metal Matrix Composite

Wire electric discharge machining (WEDM) process is considered to be one of the most suitable processes for machining metal matrix composite (MMC) materials. Lot of research work has been done on WEDM process, but very few investigations have been done on WEDM of MMCs. The purpose of this research work is to develop the artificial neural network (ANN) model to predict the material removal rate (MRR) during WEDM of SiCp/6061 Al MMC. In this work four input parameters namely servo voltage, pulse-on time, pulse-off time and wire feed rate were used to develop the ANN model. The output parameter of the model was MRR. A Box-Behnken design (BBD) approach of response surface methodology (RSM) was used to generate the input output database required for the development of ANN model. Training of the neural network models were performed on 29 experimental data points. The predicted values obtained from ANN model show that model can predict MRR with reasonable accuracy. The good agreement is obtained between the ANN predicted values and experimental values. In the present case, the value of correlation coefficient (R) equal to 0.9968, is closer to unity for ANN model of MRR. This clearly indicates that prediction accuracy is higher for ANN model.

The machining characteristics and surface integrity of Ni-rich NiTi shape memory alloy using wire electric discharge machining

Owing to the increasing demand for Ni-rich shape memory alloys in various sectors such as biomedical, aerospace, and robotics, the efficient machining of shape memory alloys is vital for their productive exploitation. The aim of this experimental investigation is to explore the influence of wire electric discharge machining process parameters such as spark gap voltage, wire tension, spark off time, wire speed, and spark on time, on the cutting efficiency and surface roughness of Ni50.89Ti49.11 SMA using one factor at a time approach. The results reveal that cutting efficiency and surface roughness are strongly influenced by spark off time, spark on time, and spark gap voltage, whereas wire speed and wire tension have the inconsequential effect. The presence of many microcracks, craters, voids, bulges of debris, and the re-solidified layer of molten material on the machined surface have been detected in scanning electron micrographs. The results of phase analysis using energy-dispersive X-ray spectroscopy and X-ray diffraction divulge the migration of foreign elements from the brass wire and dielectric to the machined surface. Due to the formation of recast layer and various oxides, the hardening effect near the machined surface was also observed. The hardness near the machined surface has been increased several times in comparison to bulk hardness.

Parametric study on the recast layer during EDWC of a Ni-rich NiTi shape memory alloy

Abstract Shape memory alloys (SMAs) have exceptional properties, namely, shape memory effect, superelasticity and other enriched physical and mechanical properties. The conventional machining of these alloys is associated with several severe difficulties. Electric discharge wire cutting (EDWC), an advanced machining process, is utilized to machine Ni50.89Ti49.11 SMA under this investigation. The aim of this investigation was to reveal the effect of EDWC variable parameters such as pulse on time (TON), wire feed (WF) rate, spark gap voltage (SV), wire tension (WT) and pulse off time (TOFF) on recast-layer thickness (RLT) and a foreign element atomic content (FEAC) using the one-factor-at-a-time approach. Scanning electron microscope equipped with energy dispersive X-ray spectroscopy facility was utilized to analyse RLT and FEAC. RLT and FEAC are varied linearly and nonlinearly respectively with TON, TOFF and SV. A thick recast layer with a high percentage of FEAC was formed on the machined surface of Ni50.89Ti49.11 SMA at a higher TON and a lower TOFF and SV. However, RLT and FEAC are slightly varied with WF and WT.