Age hardening process modeling and optimization of aluminum alloy A356/Cow horn particulate composite for brake drum application using RSM, ANN and simulated annealing

• Main Authors: Chidozie Chukwuemeka Nwobi-Okoye, Basil Quent Ochieze
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2018-08-01
• Series: Defence Technology
• Online Access: http://www.sciencedirect.com/science/article/pii/S2214914718300059

Most conventional ceramic based aluminum metal matrix composites (MMCs) are either heavy, costly or combination of both. In order to reduce cost and weight, while at the same time maintaining quality, cow horn particles (CHp) was used with aluminum alloy A356 to produce MMC for brake drum application and other engineering uses. The aim of this research is to model the age hardening process of the produced composite using response surface methodology (RSM) and artificial neural network (ANN), and to use the developed ANN as fitness function for a simulated annealing optimization algorithm (SA-NN system) for optimization of age hardening process parameters. The results show that ANN modeled the age hardening data excellently and better than RSM with a correlation coefficient of experimental response with ANN predictions being 0.9921 as against 0.9583 for the RSM. The SA-NN system optimized process parameters were in very close agreement with the experimental values with the maximum relative error of 1.2%, minimum of 0.35% and average of 0.71%. Keywords: Artificial neural network, Response surface methodology, Simulated annealing, Age hardening, Metal matrix composite