Abstract
The present study reports the effect of different process parameters on machining forces, surface roughness, dimensional deviation and material removal rate during hard turning of EN31, SAE8620 and EN9 tool steels. Feed rate followed by hardness, cutting speed and nose radius-depth of cut significantly affected machining forces whereas feed rate had the largest effect on surface roughness. The four responses were subsequently optimized for both rough and finish machining using genetic algorithm to determine the optimum combination of input parameters. Machined surfaces were subsequently analyzed using XRD followed by analysis of grain size and crystallite size of the machined samples and SEM analysis. Higher chromium content was observed at the machined surface as manganese dissolves in cementite and may replace iron atoms in the cementite lattice after machining. High heat is generated when machining at higher cutting speeds causing severe strain. The depth of the white layer decreases with increasing tool nose radius and increases at larger feeds because of greater heat generation. The SEM observations showed a smooth pattern with very low undulations with almost no crack damage.
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Ajay Batish is Ph.D. in Engineering and presently working as Professor and Head Mechanical Engineering Department at Thapar University Patiala, India. He has almost 9 years industry and 14 years of teaching and research experience. He has published many research papers in international and national journals of repute.
Anirban Bhattacharya graduated in Mechanical Engineering and earned his Masters degree in Manufacturing Technology. Presently he is working as Assistant Professor in Mechanical Engineering Department at Thapar University Patiala, India. He has almost ten years of teaching experience and has published many research papers in international and national journals.
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Batish, A., Bhattacharya, A., Kaur, M. et al. Hard turning: Parametric optimization using genetic algorithm for rough/finish machining and study of surface morphology. J Mech Sci Technol 28, 1629–1640 (2014). https://doi.org/10.1007/s12206-014-0308-y
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DOI: https://doi.org/10.1007/s12206-014-0308-y