machining parameters

grey relational

neural network ann

Defence Technology

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. Keywords: Silicon carbide, Temperature, Surface roughness, Cutting forces, Artificial neural network, Grey relational analysis

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