Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis |
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| Authors: | N Muthukrishnan J Paulo Davim |
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| Affiliation: | 1. Department of Mechanical Engineering, Sri Venkateswara College of Engineering, P.O. Box 3, Pennalur, Sriperumbudur 602 105, Tamilnadu, India;2. Department of Mechanical Engineering, University of Aveiro, Campus, Santiago, 3810-193 Aveiro, Portugal;1. Parul Institute of Technology,Vadodara-391760,India;2. Parul Institute of Technology,Vadodara-391760,India;1. Department of Mechanical Engineering, Engineering Faculty, Bursa Uludag University, Turkey;2. Department of Energy Systems Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, Turkey;1. Department of Industrial Manufacturing Engineering and Management, PN Engineering College, National University of Sciences and Technology, PNS Jauhar, Karachi 75350, Pakistan;2. Manufacturing and Laser Processing Research Group, School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M60 1QD, UK |
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| Abstract: | In recent years, the utilization of metal matrix composites (MMC) materials in many engineering fields has increased tremendously. Accordingly the need for accurate machining of composites has also increased enormously. Despite the recent developments in the near net shape manufacture, composite parts often require post-mold machining to meet dimensional tolerances, surface quality and other functional requirements. In the present work, the surface roughness of Al–SiC (20 p) has been studied in this paper by turning the composite bars using coarse grade polycrystalline diamond (PCD) insert under different cutting conditions. Experimental data collected are tested with analysis of variance (ANOVA) and artificial neural network (ANN) techniques. Multilayer perceptron model has been constructed with back-propagation algorithm using the input parameters of depth of cut, cutting speed and feed. Output parameter is surface finish of the machined component. On completion of the experimental test, ANOVA and an ANN are used to validate the results obtained and also to predict the behavior of the system under any condition within the operating range. |
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