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Cutting Tool Failure and Surface Finish Analysis in Pulsating MQL-Assisted Hard Turning

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Abstract

Current work emphasized the cutting tool failure, surface roughness, surface topology and chip morphology analysis in hard turning of AISI 4340 steel under a novel pulsating MQL cooling/lubricating circumstance. Pulsating MQL is a newer cooling strategy through which lubricant is supplied into cutting region by time-controlled pulse mode. Al2O3 top-layered coated carbide tool is utilized to furnish the turning task. Tool failure analysis is carried out considering principal flank wear and auxiliary flank wear. Principal flank wear is traced to be lower than the standard limit of 0.2 mm with the maximum surface roughness of 0.99 µm. Abrasion mechanism is the prime cause for the formation of principle flank wear in each test. Auxiliary flank wear majorly influences the dimensional error and surface quality of the turned surface. Cutting speed as well as depth of cut is the significant and impactful term toward principal and auxiliary flank wears. Feed trailed by the depth of cut and cutting speed are the significant terms toward surface roughness. Helical or ribbon pattern of chips is noticed during the test. The feed is the uppermost term which influences the chip reduction coefficient. Further, neural network modeling has been accomplished to simulate the output response data. Two different training algorithms, namely BFGS quasi-Newton (trainbfg) and Levenberg–Marquardt (trainlm), are utilized for the modeling. Three different architectures like 4-4-1, 4-5-1 and 4-8-1 have been considered to simulate the outputs. Levenberg–Marquardt (trainlm) algorithm attributed the smaller absolute percentage mean error (APME) value relative to BFGS quasi-Newton (trainbfg) algorithm. Neural network architecture 4-8-1 with trainlm algorithm exhibited the least values of APME and largest R-square for principal and auxiliary flank wears, while architecture 4-4-1 with trainlm algorithm exhibited the least values of APME and largest R-square for surface roughness.

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Acknowledgments

The authors are very thankful to Kalinga Institute of Technology (KIIT) deemed to be university to provide the required machining requirements.

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  1. School of Mechanical Engineering, KIIT University, Bhubaneswar-24, Odisha, India

    Soumikh Roy, Ramanuj Kumar, Ashok Kumar Sahoo & Amlana Panda

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Correspondence to Ramanuj Kumar.

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Roy, S., Kumar, R., Sahoo, A.K. et al. Cutting Tool Failure and Surface Finish Analysis in Pulsating MQL-Assisted Hard Turning. J Fail. Anal. and Preven. 20, 1274–1291 (2020). https://doi.org/10.1007/s11668-020-00940-8

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