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Sustainable machining of superalloy in minimum quantity lubrication environment: leveraging GEP-PSO hybrid optimization algorithm

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Abstract

The present research focuses on establishing a sustainable manufacturing paradigm through the development of a model for optimizing machining parameters under a minimum quantity lubricating environment. The variables considered for investigation encompass cutting speed, feed, and depth of cut. To gauge the sustainability, five key indicators were assessed: total energy consumption, total carbon emission, total machining cost, surface roughness, and tool wear incurred during the machining of Inconel 690. Besides, in order to proficiently oversee machining parameters, the essential integration of modeling and optimization strategies assumes paramount significance. Consequently, a combined approach employing Gene Expression Programming (GEP) in conjunction with Particle Swarm Optimization (PSO) was adopted. GEP was employed to model the observed outcomes in relation to the design variables. Subsequently, the PSO technique was applied to ascertain the optimal configuration of machining parameters. Notably, a confirmation test demonstrated a minimal average discrepancy (less than 3%) between the experimental and GEP-PSO predicted results, showcasing the robustness of the hybrid optimization approach. Finally, machining with silica-doped sunflower oil in optimized machining conditions yields 20% energy reduction, 18.68% lower carbon emissions, an 11.76% cost decrement, a 20.21% reduction in surface roughness, and a 31.71% drop in tool wear, as compared to dry machining, making it an eco-friendly and cost-effective option for superalloy machining.

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Acknowledgements

The authors gratefully acknowledge Chennai Institute of Technology, Chennai, India, vide funding number CIT/CNS/2023/RP-016 for providing financial assistance to carry out this work.

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Authors and Affiliations

  1. Centre for Computational Modeling, Chennai Institute of Technology, Chennai, Tamil Nadu, 600069, India

    Binayak Sen

  2. Department of Mechanical Engineering, SRKR Engineering College, Bhimavaram, Andhra Pradesh, 534204, India

    Shantanu Debnath

  3. Department of Mechanical Engineering, Dream Institute of Technology, Kolkata, 700104, India

    Abhijit Bhowmik

  4. Division of Research & Development, Lovely Professional University, Phagwara, Punjab, 144001, India

    Abhijit Bhowmik

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  1. Binayak Sen
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Sen, B., Debnath, S. & Bhowmik, A. Sustainable machining of superalloy in minimum quantity lubrication environment: leveraging GEP-PSO hybrid optimization algorithm. Int J Adv Manuf Technol 130, 4575–4601 (2024). https://doi.org/10.1007/s00170-024-12962-9

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