Optimization of Wear Phenomenon of Al6061/Gr MMCs using Non-Traditional Optimization Methods

3rd International Conference on Materials and Manufacturing Engineering 2018

Gangadhara Rao, P.^1 ; Gopala Krishna, A.^2 ; Vundavilli, Pandu R.^3

Department of Mechanical Engineering, KL University, Vaddeswaram, AP

522502, India^1

Department of Mechanical Engineering, JNT University, Kakinada, AP

533002, India^2

School of Mechanical Sciences, IIT Bhubaneswar, Odisha

752050, India^3

关键词: Al6061/Gr MMCs;    Invasive weed optimization;    Optimization algorithms;    Particle swarm optimization algorithm;    Percentage of reinforcements;    Response surface methodology;    Tribological properties;    Weight loss;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/390/1/012055/pdf
DOI  :  10.1088/1757-899X/390/1/012055

来源: IOP

【 摘 要 】

The tribological properties like wear rate/weight loss plays a significant role when the newly developed metal matrix composites are put in to the service condition. Therefore, deciding the optimal parameters for wear is an important research area. In the present manuscript, two non-traditional optimization algorithms, namely invasive weed optimization (IWO) and particle swarm optimization (PSO) algorithms are used to optimize the said process. The non-linear regression equations developed for as cast and heat-treated Al6061/Gr MMCs using response surface methodology is used for the said purpose. The four independent process parameters, namely sliding velocity, percentage of reinforcement, load and sliding distance are considered to optimize the weight loss during wear test. The performance of the developed optimization algorithms is compared in terms of their ability to produce optimal solution.