Abstract
The aim of this work is to develop an integrated study of surface roughness for modeling and optimization of cutting parameters during end milling operation of C40 steel with HSS tools under wet condition. The experimentation is carried out using full factorial design (three factor depth of cut, feed and spindle speed and three level). Artificial neural network (ANN) based on Back-propagation (BP) learning algorithm is used to construct the surface roughness model and second-order response surface model for the surface roughness is developed using Response surface methodology. By analysis three different surface curves it can be concluded that the minimum surface roughness (2.1779 µm) will be achieved when spindle speed, feed and depth of cut are 486 rpm, 46 mm/min and 0.31 mm respectively. Optimum parameters are obtained using GA, is near about same as value of optimum parameters obtained using RSM so it is concluded that RSM method is verified by GA Optimization.
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Mondal, S.C., Mandal, P., Ghosh, G. (2017). Application of Genetic Algorithm for the Optimization of Process Parameters in Keyway Milling. In: Chakrabarti, A., Chakrabarti, D. (eds) Research into Design for Communities, Volume 1. ICoRD 2017. Smart Innovation, Systems and Technologies, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-10-3518-0_7
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DOI: https://doi.org/10.1007/978-981-10-3518-0_7
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