Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-grey relational analysis

作     者：S.Ajith Arul Daniel R.Pugazhenthi R.Kumar S.Vijayananth 

作者机构：Department of Mechanical Engineering Vels Institute of ScienceTechnology and Advanced Studies 

出 版 物：《Defence Technology（防务技术）》 (Defence Technology)

年 卷 期：2019年第15卷第4期

页      面：545-556页

核心收录：

学科分类：08[工学] 0806[工学-冶金工程] 0817[工学-化学工程与技术] 0826[工学-兵器科学与技术] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 0703[理学-化学] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 

主　　题：Silicon carbide Temperature Surface roughness Cutting forces Artificial neural network Grey relational analysis 

摘      要：This study aims to optimize the input parameters such as mass fraction and particle size of SiC along with depth of cut,feed and cutting speed in the milling of Al5059/SiC/MoS2.The hybrid metal matrix composites are generally fabricated by reinforcing of different sizes(10,20,40 μm)of SiC with aluminium at a different levels(5%,10%& 15%)whereas the MoS2 addition is fixed as 2%.The effect of each control factor on response variables are analyzed through Taguchi S/N ratio method.Also,the most significant method for prediction of response parameters is satisfied by ANN model than the regression model.Analysis of variance(ANOVA)results envisage that mass fraction of SiC,feed rate is the most domineering factor on response variable.