wire electrical discharge machining
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Author(s):  
Sadananda Chakraborty ◽  
Souren Mitra ◽  
Dipankar Bose

Precision machining characteristics with high-dimensional accuracy make the material more adaptable towards the applications. The present study employs the powder mixed wire electrical discharge machining process to machine Ti6Al4V alloy material. In spite of limited drawbacks and enhanced output in the powder mixed wire electrical discharge machining process, the present problem has been formulated for improving the machining efficiency of Ti6Al4V. The impact of suspended powder characteristics on responses, that is, material removal rate and surface roughness, is examined throughout the process. The current investigation also focuses on the interaction effect of machining constraints along with Al2O3 abrasive mixed dielectric to achieve economical machining output for the Ti6Al4V material. An effort has been presented to obtain optimal solutions using the different methodologies, namely response surface methodology, grey relation analysis, and particle swarm optimization. The study reveals that discharge energy is deeply influenced by the peak current and pulse off time followed by powder concentration in the powder mixed wire electrical discharge machining process. The maximum material removal rate of 6.628 mm3/min and average surface finish of 1.386 μm are the outcome of the present study for a set of optimal machining settings, that is, pulse off time ( Toff) of 7.247 μs, pulse on time ( Ton) of 30 μs, peak current ( Ip) of 2 A, and powder concentration of 4 g/L. Finally, the proposed model has been verified that the hybrid particle swarm optimization technique has the highest adequate capability to achieve maximum output. Thus, the approach offered an enhancement on performance measures of Ti6Al4V alloy in the powder mixed wire electrical discharge machining process.


Crystals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1342
Author(s):  
Hongzhi Yan ◽  
Bakadiasa Djo Kabongo ◽  
Hongbing Zhou ◽  
Cheng Wu ◽  
Zhi Chen

With the properties of high specific strength, small thermal expansion and good abrasive resistance, the particle-reinforced aluminum matrix composite is widely used in the fields of aerospace, automobile and electronic communications, etc. However, the cutting performance of the particle-reinforced aluminum matrix composite is very poor due to severe tool wear and low machining efficiency. Wire electrical discharge machining has been proven to be a good machining method for conductive material with any hardness. Even so, the high-volume SiCp/Al content composite is still a difficult-to-machine material in wire electrical discharge machining due to the influence of insulative the SiC particle. The goal of this paper is to analyze the machining characteristics and find the optimal process parameters for the high-volume content (65 vol.%) SiCp/Al composite in wire electrical discharge machining. Experimental results show that the material removal method of the SiCp/Al composite includes sublimating, decomposing and particle shedding. The material removal rate is found to increase with the increasing pulse-on time, first increasing and then decreasing with the increasing pulse-off time, servo voltage, wire feed and wire tension. Pulse-on time and servo voltage are the dominant factors for surface roughness. In addition, the multi-objective optimization method of the nondominated neighbor immune algorithm is presented to optimize the process parameters for a fast material removal rate and low surface roughness. The optimized process parameters can increase the material removal rate by 34% and reduce the surface roughness by 6%. Furthermore, the effectiveness of the Pareto optimal solution is proven by the verified experiment.


Author(s):  
Rouhan Rafiq

Abstract: One of the important non-traditional machining processes is Wire Electrical Discharge Machining, used for machining difficult to machine materials like composites and inter-metallic materials. WEDM involves complex physical and chemical process including heating and cooling. Accompanying the development of mechanical industry, the demand for alloy materials having high hardness, toughness and impact resistance are increasing. The WEDM satisfy the present demands of the manufacturing industries such as better finish, low tolerance, higher production rate, miniaturization etc. The consistent quality of parts being machined in WEDM is difficult because the process parameters cannot be controlled effectively. The problem of arriving at the optimum levels of the operating parameters has attracted the attention of the researcher and practicing engineers for a very long time. The objective of the present study was to experimentally investigate the effects of various Wire Electrical Discharge Machining variables on Surface Roughness and Material Removal Rate of AISI 1045 using ANOVA method. Taguchi’s L18 Orthogonal Array was used to conduct experiments, which correspond to randomly chosen different combination of process parameters: wire type, pulse on time, pulse off time, peak current, servo voltage, wire feed rate, flushing pressure each to be varied in three different levels. The surface roughness and material removal rate were selected as output responses for the present investigation. The effect of all the input parameters on the output responses have been analyzed using analysis of variance (ANOVA). The effect of variation in input parameters has been studied on the output responses. Plots of S/N ratio have been used to determine the best relationship between the responses and the input parameters. In other words, the optimum set of input parameters for minimum surface roughness and maximum material removal rate were determined. It has been found that wire type, pulse on time are most significant factors for surface roughness and wire type, pulse on time, pulse off time, wire feed rate are most significant factors for material removal rate. Keywords: Input Parameters, Wire Electric Discharge Machining, ANOVA, Taguchi


2021 ◽  
Author(s):  
P. Thejasree ◽  
Manikandan N ◽  
PC Krishnamachary ◽  
K C Varaprasad ◽  
Binoj Joseph Selvi

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