Manufacturing Technology Today

N. Radhika ¹, K. Suresh Kumar ², M. Joseph Davidson ¹, P. S. S. Srinivasan ³

Affiliations
1 School of Mechanical Sciences, Karunya Institute of Technology and Sciences (Deemed University), Karunya Nagar, Coimbatore-641114, IN
2 Science and Technology Entrepreneur Park, P.S.G College of Technology, Coimbatore, IN
3 K.S.R. College of Engineering, IN

Abstract

Today, Design of Experiments (DOE) has been widely used for various designs and manufacturing processes to obtain precisional quality and to optimize the product or processes design. In this contest, the present paper determines the best weld parameters for precisional welding with the aid of DOE & finite element analysis (FEA). Weld quality is mainly depends on penetration area. The factors which drives penetration area are heat flux, welding speed, metal preheat and plate thickness and ambient temperature. In this ambient temperature is an uncontrollable factor and remaining are considered as process control parameters. L9 orthogonal array is used in the investigation. The nine orthogonal array tests are analyzed computationally by ANSYS FEA-thermal module (commercial-software). Finally, best set of process control parameters, which opts better quality, is determined from S/N curves.

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M. Joseph Davidson ¹, S. Porpandi Selvi ², D. G. Harris Samuel ¹, D. Alin Jeyadarlus Sofana ¹, M. Jeeva Rose Shanthi ¹

Affiliations
1 Production Engineering Division, Karunya Institute of Technology and Sciences (Deemed University), Coimbatore-641114, IN
2 Information Technology Division, Karunya Institute of Technology and Sciences (Deemed University), Coimbatore-641114, IN

Abstract

Non-conventional machining processes such as the eiectro discharge machining process are used to machine hard tool and die materials. However, the process produces surfaces that have high tensile residual stresses, high surface roughness, cracks etc. The aim of this paper is to quantify the effect of some of the main EDM parameters on the surface roughness using Response Surface Methodology's Box-Behnken design and to develop a knowledge base using Neural Network. In this work, a mathematical model has been developed using the critical parameters namely the peak current, the pulse on time and the gap voltage using RSM. A response equation has been developed to predict the surface roughness values. The validity of this response equation has been proved by conducting additional experiments within the range of the experimentation. An Analysis of Variance (AVOVA) has been performed which revealed that the model developed is valid. The response equation has been used to generate training data, meant for training a Feed Forward Back Propagation Neural Network (NN). The critical parameters namely the peak current, the pulse on time and the gap voltage form the input parameters for the proposed NN model and the output being the surface roughness values. The predicted surface roughness values from the NN are found to be in reasonable agreement with the experimental values.

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