Optimization of tribology parameters of AZ91D magnesium alloy in dry sliding condition using response surface methodology and genetic algorithm

被引:8
作者
Beniyel, M. [1 ]
Sivapragash, M. [2 ]
Vettivel, S. C. [3 ]
Kumar, P. Senthil [4 ]
Kumar, K. K. Ajith [5 ]
Niranjan, K. [6 ]
机构
[1] Anna Univ, Dept Mech Engn, Chennai, Tamil Nadu, India
[2] Univ Coll Engn & Technol, Dept Mech Engn, Tirunelveli, Tamil Nadu, India
[3] Chandigarh Coll Engn & Technol, Dept Mech Engn, Chandigarh, India
[4] MET Engn Coll, Dept Mech Engn, Kanyakumari, Tamil Nadu, India
[5] Rohini Coll Engn & Technol, Dept Mech Engn, Anjugramam, Tamil Nadu, India
[6] Annamalai Univ, Dept Mfg Engg, Annamalainagar 608002, Tamil Nadu, India
关键词
magnesium alloy; pin-on-disc; tribology; dry condition; optimization; casting; ARTIFICIAL NEURAL-NETWORK; MECHANICAL-PROPERTIES; WEAR; MICROSTRUCTURE; BEHAVIOR; PERFORMANCE; EMISSIONS; FRICTION;
D O I
10.24425/bpasts.2021.135835
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In the present research, the wear behaviour of magnesium alloy (MA) AZ91D is studied and optimized. MA AZ91D is casted using a die-casting method. The tribology experiments are tested using pin-on-disc tribometer. The input parameters are sliding velocity (1-3 m/s), load (1-5 kg), and distance (0.5-1.5 km). The worn surfaces are characterized by a scanning electron microscope (SEM) with energy dispersive spectroscopy (EDS). The response surface method (RSM) is used for modelling and optimising wear parameters. This quadratic equation and RSM-optimized parameters are used in genetic algorithm (GA). The GA is used to search for the optimum values which give the minimum wear rate and lower coefficient of friction. The developed equations are compared with the experimental values to determine the accuracy of the prediction.
引用
收藏
页数:10
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