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Machinability analysis of AZ31 magnesium alloys using the Taguchi gray relational analysis

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Abstract

The manufacturing sector is among the fastest growing in today’s industrialized world. Increasing the efficiency and sustainability of manufacturing processes is one way to improve productivity and improve profit margins. Learning about cutting conditions and how they affect machined surfaces and tool life can help improve productivity. This research aims to analyze the machinability of difficult-to-cut magnesium alloys through lubrication and different cooling strategies and their influence on the environment. Flank wear, chip morphology, tool contact length, and surface roughness were all measured in this study using controlled machining experiments with dry and vegetable oil mist cutting conditions and coated carbide tools. To improve the machinability of magnesium alloys, researchers looked at surface roughness, tool wear processes, and primary and secondary machining components such as effective shear angle, compression ratio, and coefficient of friction. In this study, we found that minimum quantity lubrication (MQL) performed well under various speed ranges for coated tools. Tool wear, surface roughness, and other output response characteristics were significantly connected to feed rate and cutting speed. MQL improved the surface finish and material removal rate by 20% compared to dry machining, and microhardness has been improved by 11.5%. MQL-based systems offer great potential to improve the machinability of magnesium alloys, and they should be explored further.

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Acknowledgements

The authors would like to thank the financial support provided by the Rochester Institute of Technology, Dubai Campus for this research work.

Funding

Authors would like to acknowledge the financial support provided by Rochester Institute of Technology – Dubai Campus to conduct this research.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Rochester Institute of Technology, Dubai Campus, P.O. Box, 341055, Dubai, United Arab Emirates

    Shafahat Ali & Salman Pervaiz

  2. School of Engineering, University of Guelph, Guelph, ON, Canada

    Shafahat Ali

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  1. Shafahat Ali
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  2. Salman Pervaiz
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Contributions

Conceptualization, S.P. and S.A.; methodology, S.A.; validation, S.A.; formal analysis, S.A. and S.P.; investigation, S.A.; resources, S.P.; data curation, S.A.; writing—original draft preparation, S.A.; writing—review and editing, S.P.; supervision, S.P.; project administration, S.P.; funding acquisition; S.P

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Correspondence to Shafahat Ali.

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Ali, S., Pervaiz, S. Machinability analysis of AZ31 magnesium alloys using the Taguchi gray relational analysis. Int J Adv Manuf Technol 126, 4171–4190 (2023). https://doi.org/10.1007/s00170-023-11354-9

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