Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools

Authors:
C. Ahilan;Somasundaram Kumanan;N. Sivakumaran;J. Edwin Raja Dhas
Affiliations:
Department of Mechanical Engineering, Oxford Engineering College, Tiruchirappalli, 620009 Tamil Nadu, India;Department of Production Engineering, National Institute of Technology Tiruchirappalli, 620015 Tamil Nadu, India;Department of Instrumentation and Control Engineering, National Institute of Technology Tiruchirappalli, 620015 Tamil Nadu, India;Department of Automobile Engineering, Noorul Islam Centre for Higher Education, Nagercoil 629180, Tamil Nadu, India
Venue:
Applied Soft Computing
Year:
2013

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Abstract

Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design and demand of quality products. To make decision making process (selection of machining parameters) online, effective and efficient artificial intelligent tools like neural networks are being attempted. This paper proposes the development of neural network models for prediction of machining parameters in CNC turning process. Experiments are designed based on Taguchi's Design of Experiments (DoE) and conducted with cutting speed, feed rate, depth of cut and nose radius as the process parameters and surface roughness and power consumption as objectives. Results from experiments are used to train the developed neuro based hybrid models. Among the developed models, performance of neural network model trained with particle swarm optimization model is superior in terms of computational speed and accuracy. Developed models are validated and reported. Signal-to-noise (S/N) ratios of responses are calculated to identify the influences of process parameters using analysis of variance (ANOVA) analysis. The developed model can be used in automotive industries for deciding the machining parameters to attain quality with minimum power consumption and hence maximum productivity.