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Predictive modelling and optimization for machinability indicators in cleaner milling of PH13-8Mo using sustainable cutting environments

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Abstract

This study aims to optimize and model the resultant cutting force (Fr) and surface roughness (Ra) in the cleaner milling of PH13-8Mo stainless steel, which is extremely difficult to machine due to its high technical properties. The impacts of dry, minimal quantity lubrication (MQL) and cryogenic (Cryo) environments on the Fr and Ra were investigated in the up-milling of PH13-8Mo. The experiments were done using TiAlN-coated inserts at varying cutting speeds and feed rates. Control factors were optimized simultaneously with Taguchi-based grey relational analysis (TGRA) to minimize Fr and Ra. Predictive models of Fr and Ra were developed by the response surface method. An average of 20.98% and 19.86% improvement in Ra was achieved in the MQL and cryo environments, respectively. Increased sticking, chipping and microcracks in the insert due to cryogenic cooling increased Fr and Ra. Optimum factors were found as an MQL environment, a 60 m/min cutting speed and a 0.04 mm/rev feed rate with TGRA. The high correlations of the developed mathematical models showed that the models were reliable. Thus, significant support will be provided to sustainable machining with the industrial use of data obtained for machinability indicators in milling PH13-8Mo steel.

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Acknowledgements

The authors thank the Karabük University Scientific Research Projects Unit for providing financial support for this study under the project code KBÜBAP-22-DR-073.

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  1. Akmağdeni Vocational High School, Yozgat Bozok University, Yozgat, Turkey

    Hakan Yurtkuran

  2. Department of Mechanical Engineering, Faculty of Engineering, Karabük University, Karabük, Turkey

    Mustafa Günay

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  1. Hakan Yurtkuran
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Correspondence to Mustafa Günay.

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Yurtkuran, H., Günay, M. Predictive modelling and optimization for machinability indicators in cleaner milling of PH13-8Mo using sustainable cutting environments. J Braz. Soc. Mech. Sci. Eng. 46, 319 (2024). https://doi.org/10.1007/s40430-024-04897-9

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