The investigation of the effect of particle size on wear performance of AA7075/Al2O3 composites using statistical analysis and different machine learning methods

摘要

In this study, 7075 - Al2O3 (5 wt%) composites with a particle size of 0.3 mu m, 2 mu m, and 15 mu m were developed by hot pressing. The dry sliding wear performance of the specimens was evaluated under loads of 5 N, 10 N, 20 N, 30 N, and at sliding speeds of 80 mm/s, 110 mm/s, 140 mm/s by reciprocating wear tests. The wear tests showed that 7075 5 - Al2O3 (15 mu m) exhibited the best wear performance. The volume loss of 7075 5 - Al2O3 (15 mu m) under load of 30 N for sliding speed of 140 mm/s was 37.1% lower than the unreinforced 7075 alloy. The volume loss (mm(3)) of composites reinforced with the particle size of 0.3 mu m, 2 mu m, and 15 mu m was 11.62, 9.87, and 8.07, respectively, for load of 30 N and sliding speed of 140 mm/s. An increase in the applied load and sliding speed increased the wear severity by changing the wear mechanism from abrasion to delamination. The analysis of variance (ANOVA) showed that the load was the most significant parameter on the volume loss. The linear regression (LR), support vector regression (SVR), artificial neural network (ANN), and extreme learning machine (ELM) were used for the prediction of volume loss. The determination coefficient (R-2) of the LR, SVR, ANN, and ELM was 0.814, 0.976, 0.935, and 0.989, respectively. The ELM model has the highest success. Thus, the ELM model has significant potential for the prediction of wear behaviour for Al matrix composites. (c) 2020 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.