The machined surface quality is adversely influenced by thermal effect which requires pacification of temperature by coolant. Lately, excess use of coolant has been found as environmentally hostile and costly. As ultimate resolution, minimum quantity lubrication (MQL) wherein a trivial amount of coolant is implemented to cutting zone is deemed as an alternative. However, the effects of different aspects of MQL require absolute evaluation. Therefore, in this study, an investigation of average surface roughness parameter has been performed in turning of hardened steel of 600 BHN with uncoated carbide under the application of MQL. The novelty of this work is the use of variable time-controlled pulse in delivering coolant at flow rates of 500–1100 ml/h along with cutting speed and feed rate range of 66–100 m/min and 0.18–0.25 mm/rev, respectively. The results show that higher cutting speed and lower feed rate contribute to the minimization of roughness in machined surface. Regarding time dependency, the MQL pulse driven by a 1-s time gap induces the best surface quality, i.e., lowest surface roughness. Furthermore, an artificial neural network-based predictive model of surface roughness has been formulated which is capable of preserving 97.5% accuracy.
The International Journal of Advanced Manufacturing Technology – Springer Journals
Published: Jan 18, 2017
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