Wire electric discharge machining characteristics of titanium nickel shape memory alloy

Ti Ni shape memory alloys（SMAs） have been normally used as the competent elements in large part of the industries due to outstanding properties, such as super elasticity and shape memory effects. However, traditional machining of SMAs is quite complex due to these properties. Hence, the wire electric discharge machining（WEDM） characteristics of Ti Ni SMA was studied. The experiments were planned as per L27 orthogonal array to minimize the experiments, each experiment was performed under different conditions of pulse duration, pulse off time, servo voltage, flushing pressure and wire speed. A multi-response optimization method using Taguchi design with utility concept has been proposed for simultaneous optimization. The analysis of means（ANOM） and analysis of variance（ANOVA） on signal to noise（S/N） ratio were performed for determining the optimal parameter levels. Taguchi analysis reveals that a combination of 1 μs pulse duration, 3.8 μs pulse off time, 40 V servo voltage, 1.8×105 Pa flushing pressure and 8 m/min wire speed is beneficial for simultaneously maximizing the material removal rate（MRR） and minimizing the surface roughness. The optimization results of WEDM of Ti Ni SMA also indicate that pulse duration significantly affects the material removal rate and surface roughness. The discharged craters, micro cracks and recast layer were observed on the machined surface at large pulse duration.

Simultaneous optimization of multiple performance characteristics in WEDM for machining ZC63/SiC_p MMC

Abstract The compliance of an integrated approach, principal component analysis （PCA）, coupled with Tagu chi＇s robust theory for simultaneous optimization of cor related multiple responses of wire electrical discharge machining （WEDM） process for machining SiCp rein forced ZC63 metal matrix composites （MMCs） is investi gated in this work. The WEDM is proven better for its efficiency to machine MMCs among others, while the particulate size and volume percentage of SiCp with the composite are the utmost important factors. These improve the mechanical properties enormously, however reduce the machining performance. Hence the WEDM experiments are conducted by varying the particulate size, volume fraction, pulseon time, pulseoff time and wire tension. In the view of quality cut, the most important performance indicators of WEDM as surface roughness （Ra）, metal removal rate （MRR）, wire wear ratio （WWR）, kerf （Kw） and white layer thickness （WLT） are measured as respon ses. PCA is used as multiresponse optimization technique to derive the composite principal component （CPC） which acts as the overall quality index in the process. Consequently, Taguchi＇s S/N ratio analysis is applied to optimize the CPC. The derived optimal process responses are confirmed by the experimental validation tests results. The analysis of vari ance is conducted to find the effects of choosing process variables on the overall quality of the machined component.The practical possibility of the derived optimal process conditions is also presented using SEM.

Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm

Machining of shape memory alloys(SMAs)without losing the shape memory effect could immensely extend their applications.Herein,the wire electric discharge machining process was used to machine NiTi—a shape memory alloy.The experimental methodology was designed using a Box-Behnken design approach of the response surface methodology.The effects of input variables including pulse on time,pulse off time,and current were investigated on the material removal rate,surface roughness,and microhardness.ANOVA tests were performed to check the robustness of the generated empirical models.Optimization of the process parameters was performed using a newly formulated,highly efficient heat transfer search algorithm.Validation tests were conducted and extended for analyzing the retention of the shape memory effect of the machined surface by differential scanning calorimetry.In addition,2D and 3D Pareto curves were generated that indicated the trade-offs between the selected output variables during the simultaneous output variables using the multi-objective heat transfer search algorithm.The optimization route yielded encouraging results.Single objective optimization yielded a maximum material removal rate of 1.49 mm^(3)/s,maximum microhardness 462.52 HVN,and minimum surface roughness 0.11μm.The Pareto curves showed conflicting effects during the wire electric discharge machining of the shape memory alloy and presented a set of optimal non-dominant solutions.The shape memory alloy machined using the optimized process parameters even indicated a shape memory effect similar to that of the starting base material.

Electrical Discharge Machining of Al2024-65 vol%SiC Composites

In the present work, the wire electrical discharge machining（WEDM） process of the 65 vol% SiCp/2024 Al composite prepared by pressure infiltration methods has been investigated. The microstructure of the machined composite was characterized by scanning electron microscope, the average surface roughness（Ra）, X-ray diffraction, X-ray photoelectron spectroscopy and transmission electron microscopy（TEM） techniques. Three zones from the surface to the interior（melting zone, heat affected zone and un-affected zone） were found in the machined composites, while the face of SiC particles on the surface toward the outside was ‘‘cut＇＇ to be flat. Increase in Al and Si but decrease in C and O were observed in the core areas of the removed particles. Si phase, which was generated due to the decomposition of SiC, was detected after the WEDM process. The irregular and spherical particles were further observed by TEM. Based on the microstructure observation, it is suggested that the machining mechanism of 65 vol% SiCp/2024 Al composite was the combination of the melting of Al matrix and the decomposition of SiC particles.

Optimizing machining parameters of wire-EDM process to cut Al7075/SiCp composites using an integrated statistical approach

Metal matrix composites （MMCs） as advanced materials, while producing the components with high dimensional accuracy and intricate shapes, are more complex and cost effective for machining than conventional alloys. It is due to the presence of discontinuously distributed hard ceramic with the MMCs and involvement of a large number of machining control variables. However, determination of optimal machining conditions helps the process engineer to make the process efficient and effec- tive. In the present investigation a novel hybrid multi-response optimization approach is proposed to derive the economic machining conditions for MMCs. This hybrid approach integrates the concepts of grey relational analysis （GRA）, principal component analysis （PCA） and Taguchi method （TM） to derive the optimal machining conditions. The machining experiments are planned to machine A17075/SiCp MMCs using wire-electrical discharge machining （WEDM） process. SiC particulate size and its weight percentage are explicitly considered here as the process variables along with the WEDM input variables. The derived optimal process responses are confirmed by the experimental validation tests and the results show satisfactory. The practical possibility of the derived optimal machining conditions is also analyzed and presented using scanning electron microscope （SEM） examinations. According to the growing industrial need of making high performance, low cost components, this investigation provides a simple and sequential approach to enhance the WEDM performance while machining MMCs.

Machine-vision-based electrode wear analysis for closed loop wire EDM process control

The purpose of this study was to develop a closed-loop machine vision system for wire electrical discharge machining(EDM)process control.Excessive wire wear leading to wire breakage is the primary cause of wire EDM process failures.Such process interruptions are undesirable because they affect cost efficiency,surface quality,and process sustainability.The developed system monitors wire wear using an image-processing algorithm and suggests parametric changes according to the severity of the wire wear.Microscopic images of the wire electrode coming out from the machining zone are fed to the system as raw images.In the proposed method,the images are preprocessed and enhanced to obtain a binary image that is used to compute the wire wear ratio(WWR).The input parameters that are adjusted to recover from the unstable conditions that cause excessive wire wear are pulse off time,servo voltage,and wire feed rate.The algorithm successfully predicted wire breakage events.In addition,the alternative parametric settings proposed by the control algorithm were successful in reducing the wire wear to safe limits,thereby preventing wire breakage interruptions.