Abstract

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.

Highlights

  • The particle-reinforced aluminum matrix composite is a material that is prepared by adding reinforcement to the aluminum matrix, such as carbide, nitride or graphite.Compared with the aluminum matrix, the particle-reinforced aluminum matrix composite has better physical and chemical properties, such as low density, high specific strength, excellent high-temperature properties, high wear resistance and excellent stability dimensional [1,2,3]

  • The SiCp/Al composite is one of the most common particle-reinforced aluminum matrix composites, which is widely used in the fields of aerospace, automobiles and electronic communications, etc

  • The specific stiffness of the 65 vol.% SiCp/Al composite is three times higher than the aluminum matrix and 25 times higher than copper. This material is praised as a thirdgeneration electronic packaging material, which is widely used in civil electronic equipment, IGBT plate substrates and wireless base stations

Read more

Summary

Introduction

The particle-reinforced aluminum matrix composite is a material that is prepared by adding reinforcement to the aluminum matrix, such as carbide, nitride or graphite. Analyzed the effects of process parameters on the material removal rate (MRR), surface roughness (SR) and electrode wear rate (TWR) in EDM of aluminum matrix composites with a particle content of 7.5–20%. Kumar T.T.S. et al [19] adopted a response surface methodology to determine the optimal process parameters for aluminum matrix composites with a particle content of 20% in WEDM. [20] analyzed the effects of process parameters on the machining speed and surface roughness of aluminum hybrid composites with a particle content of 20% in EDM. It was pointed out that the optimal process parameters for aluminum hybrid composites changed with the content of the reinforced particle. We can find that EDM/WEDM has been proven to be a good machining method for particle-reinforced aluminum matrix composites. The feasibility and precision of the optimal process parameters are evaluated by a verified experiment

Material
Machine Tools
The Experiment Design
Experiment Result
Machined Surface Characteristics
Table shows the composition on the machined surface of machined
The Effects of Process Parameters on MRR and SR
Process Parameters Optimization
Optimization
Verified Experiment
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call