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Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-grey relational analysis 被引量:3
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作者 S.Ajith Arul Daniel R.Pugazhenthi +1 位作者 R.Kumar s.vijayananth 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2019年第4期545-556,共12页
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 g... 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. 展开更多
关键词 Silicon CARBIDE Temperature Surface roughness Cutting FORCES Artificial neural network GREY RELATIONAL analysis
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