International Journal of Innovative Research and Development

Abstract

Research during the past several years has established the effectiveness of acoustic emission (AE) based sensing methodologies for machine condition analysis and process monitoring. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing processes. Optimum selection of cutting conditions importantly contribute to the increase of productivity and the reduction of cost, therefore utmost attention is paid to this problem in this contribution. In this paper we proposed AE as non-contact and indirect technique for in-process surface roughness assessment in turning. Three cutting conditions dry cut, cutting with water as coolant and normal coolant will be used. The materials in study are mild steel and EN8. Three cutting parameters namely feed rate, depth of cut, cutting speed will optimized with consideration with surface roughness. AE signals will be acquired from tool post jig. Surface roughness of finished surface will be measured. Taguchi method will be used find optimal cutting parameters for surface roughness (Ra) in turning. The L-9 orthogonal array, the signal-to-noise ratio and analysis of variance are employed to study the performance characteristics. Regression models will be developed and will be validated to predict the surface roughness and AE Signal value. 

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