Crystals (Nov 2021)

Analysis and Optimization of the Machining Characteristics of High-Volume Content SiCp/Al Composite in Wire Electrical Discharge Machining

  • Hongzhi Yan,
  • Bakadiasa Djo Kabongo,
  • Hongbing Zhou,
  • Cheng Wu,
  • Zhi Chen

DOI
https://doi.org/10.3390/cryst11111342
Journal volume & issue
Vol. 11, no. 11
p. 1342

Abstract

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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.

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