Multiple-response optimization of cutting forces in turning of UD-GFRP composite using Distance-Based Pareto Genetic Algorithm approach

• Published in: Engineering science and technology, an international journal Vol. 18; no. 4; pp. 680 - 695
• Main Authors: Kumar, Surinder, Gupta, Meenu, Satsangi, P.S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2015; Elsevier
• Subjects: ANOVA; Carbide (K10) tool; Cutting forces (tangential and feed force); Distance-Based Pareto Genetic Algorithm; Multiple regression methodology; UD-GFRP composite; Carbide (K10) tool; Distance-Based Pareto Genetic Algorithm; Multiple regression methodology; Cutting forces (tangential and feed force); ANOVA; UD-GFRP composite
• ISSN: 2215-0986; 2215-0986
• DOI: 10.1016/j.jestch.2015.04.010

This paper presents the investigation of cutting forces (tangential and feed force) by turning of unidirectional glass fiber reinforced plastics (UD-GFRP) composite. Composite materials are used in variety of engineering applications in different fields such as aerospace, oil, gas and process industries. Process parameters (tool nose radius, tool rake angle, feed rate, cutting speed, depth of cut and cutting environment) are investigated using Taguchi's robust design methodology. Taguchi's L18 orthogonal array is used to conduct experimentation. The experimentation is carried out with Carbide (K10) Tool, covering a wide range of machining conditions. Analysis of variance (ANOVA) is performed for significant parameters and later regression model is developed for the significant parameters. The relative significance of various factors has also been evaluated and analyzed using ANOVA. Distance-Based Pareto Genetic Algorithm (DBPGA) approach is used to optimize tangential and feed force. Predicted optimum values for tangential force and feed force are 39.93 N and 22.56 N respectively. The results of prediction are quite close with the experimental values.