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Multi-response optimization using artificial neural network-based GWO algorithm for high machining performance with minimum quantity lubrication

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Abstract

In the current work, an experimental study has been carried out in order to evaluate the influence of cutting settings on surface roughness and tangential force, when turning of X210Cr12 steel using coated carbide insert with various nose radius. The ANOVA analysis has been performed to determine the effect of cutting conditions on studied outputs. The experimental data have been analyzed using S/N ratios, mean effect graphs, and 3D response surface plots. The results indicate that the cutting insert nose radius and the feed rate are the mainly affecting factors on surface roughness, while tangential force is affected principally by depth of cut followed by feed rate. Confirmatory experiments have been established after Taguchi’s optimization. Mathematical prediction models have been developed using artificial neural network (ANN), and the multi-objective GWO algorithm was integrated for multi-objective optimization of Ra and Fz. It has been found that the cutting force is largely affected by the cutting depth with contribution and feed rate. Also, low feeds and cutting inserts with a large nose are useful for finishing process where low roughness is desired. Regarding the cooling mode, the minimum quantity lubrication is an interesting way to minimize lubricant quantity and protect operator health and environment with keeping better machining quality.

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Acknowledgements

This work was achieved in the laboratory LMS (Guelma University, Algeria). The authors would like to thank the Algerian Ministry of Higher Education and Scientific Research (MESRS).

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The work is financed by the Algerian Ministry of Higher education and Scientific Research.

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Authors and Affiliations

  1. Mechanics Research Centre, Po, Box 73B, 25000, Constantine, Algeria

    Mourad Nouioua & Aissa Laouissi

  2. Mechanics and Structures Research Laboratory (LMS), Mechanical Engineering Dept., May 8th 1945 University, 24000, Guelma, Algeria

    Mourad Nouioua, Mohamed Athmane Yallese & Salim Belhadi

  3. Industrial Engineering Department, University of Quebec at Trois-Rivieres, 351 Boulevard des Forges, Trois-Rivières, QC, G9A 5H7, Canada

    Riad Khettabi

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  1. Mourad Nouioua
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  2. Aissa Laouissi
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  3. Mohamed Athmane Yallese
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  4. Riad Khettabi
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  5. Salim Belhadi
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Correspondence to Mourad Nouioua.

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Mourad Nouioua hereby declares that he participated in the study and in the development of the manuscript titled. Multi-response optimization using artificial neural network-based GWO algorithm for high machining performance with minimum quantity lubrication and authorizes the full publishing of manuscript data.

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Nouioua, M., Laouissi, A., Yallese, M.A. et al. Multi-response optimization using artificial neural network-based GWO algorithm for high machining performance with minimum quantity lubrication. Int J Adv Manuf Technol 116, 3765–3778 (2021). https://doi.org/10.1007/s00170-021-07745-5

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  • DOI: https://doi.org/10.1007/s00170-021-07745-5

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