Application of the Combined ANN and GA for Multi-Response Optimization of Cutting Parameters for the Turning of Glass Fiber-Reinforced Polymer Composites

• Main Authors: Azhar Equbal, Mohammad Shamim, Irfan Anjum Badruddin, Md. Israr Equbal, Anoop Kumar Sood, Nik Nazri Nik Ghazali, Zahid A. Khan
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Mathematics
• Subjects: glass fiber-reinforced polymer composite; turning; full factorial design of experiments; artificial neural network; genetic algorithm; multi-response optimization
• Online Access: https://www.mdpi.com/2227-7390/8/6/947

Glass fiber-reinforced polymer (GFRP) composites find wide applications in automobile, aerospace, aircraft and marine industries due to their attractive properties such as lightness of weight, high strength-to-weight ratio, high stiffness, good dimensional stability and corrosion resistance. Although these materials are required in a wide range of applications, their non-homogeneous and anisotropic properties make their machining troublesome and consequently restrict their use. It is thus important to study not only the machinability of these materials but also to determine optimum cutting parameters to achieve optimum machining performance. The present work focuses on turning of the GFRP composites with an aim to determine the optimal cutting parameters that yield the optimum output responses. The effect of three cutting parameters, i.e., spindle rotational speed (<i>N</i>), feed rate (<i>f</i>) and depth of cut (<i>d</i>) in conjunction with their interactions on three output responses, viz., Material Removal Rate (<i>MRR</i>), Tool Wear Rate (<i>TWR</i>), and Surface roughness (<i>R</i>_a), is studied using full factorial design of experiments (FFDE). The statistical significance of the cutting parameters and their interactions is determined using analysis of variance (ANOVA). To relate the output response and cutting parameters, empirical models are also developed. Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) is employed for multi-response optimization to simultaneously optimize the <i>MRR</i>, <i>TWR</i> and <i>R</i>_a.