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Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool

Author

Listed:
  • Md. Rezaul Karim

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Juairiya Binte Tariq

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Shah Murtoza Morshed

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Sabbir Hossain Shawon

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Abir Hasan

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Chander Prakash

    (School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, Punjab, India)

  • Sunpreet Singh

    (Department of Mechanical Engineering, National University of Singapore, Singapore 119077, Singapore)

  • Raman Kumar

    (Department of Mechanical Engineering, Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India)

  • Yadaiah Nirsanametla

    (Department of Mechanical Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli 791109, India)

  • Catalin I. Pruncu

    (Department of Mechanical Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK
    Department of Design, Manufacturing & Engineering Management, University of Strathclyde, Glasgow G1 1XJ, Scotland, UK)

Abstract

Clean technological machining operations can improve traditional methods’ environmental, economic, and technical viability, resulting in sustainability, compatibility, and human-centered machining. This, this work focuses on sustainable machining of Al-Mg-Zr alloy with minimum quantity lubricant (MQL)-assisted machining using a polycrystalline diamond (PCD) tool. The effect of various process parameters on the surface roughness and cutting temperature were analyzed. The Taguchi L 25 orthogonal array-based experimental design has been utilized. Experiments have been carried out in the MQL environment, and pressure was maintained at 8 bar. The multiple responses were optimized using desirability function analysis (DFA). Analysis of variance (ANOVA) shows that cutting speed and depth of cut are the most prominent factors for surface roughness and cutting temperature. Therefore, the DFA suggested that, to attain reasonable response values, a lower to moderate value of depth of cut, cutting speed and feed rate are appreciable. An artificial neural network (ANN) model with four different learning algorithms was used to predict the surface roughness and temperature. Apart from this, to address the sustainability aspect, life cycle assessment (LCA) of MQL-assisted and dry machining has been carried out. Energy consumption, CO 2 emissions, and processing time have been determined for MQL-assisted and dry machining. The results showed that MQL-machining required a very nominal amount of cutting fluid, which produced a smaller carbon footprint. Moreover, very little energy consumption is required in MQL-machining to achieve high material removal and very low tool change.

Suggested Citation

  • Md. Rezaul Karim & Juairiya Binte Tariq & Shah Murtoza Morshed & Sabbir Hossain Shawon & Abir Hasan & Chander Prakash & Sunpreet Singh & Raman Kumar & Yadaiah Nirsanametla & Catalin I. Pruncu, 2021. "Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7321-:d:585588
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    References listed on IDEAS

    as
    1. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    2. Raman Kumar & Sehijpal Singh & Ardamanbir Singh Sidhu & Catalin I. Pruncu, 2021. "Bibliometric Analysis of Specific Energy Consumption (SEC) in Machining Operations: A Sustainable Response," Sustainability, MDPI, vol. 13(10), pages 1-30, May.
    3. Jesus M. Padilla-Atondo & Jorge Limon-Romero & Armando Perez-Sanchez & Diego Tlapa & Yolanda Baez-Lopez & Cesar Puente & Sinue Ontiveros, 2021. "The Impact of Hydrogen on a Stationary Gasoline-Based Engine through Multi-Response Optimization: A Desirability Function Approach," Sustainability, MDPI, vol. 13(3), pages 1-18, January.
    4. Yusuf Tansel Ic & Ebru Saraloğlu Güler & Ceren Cabbaroğlu & Ezgi Dilan Yüksel & Huri Maide Sağlam, 2018. "Optimisation of cutting parameters for minimizing carbon emission and maximising cutting quality in turning process," International Journal of Production Research, Taylor & Francis Journals, vol. 56(11), pages 4035-4055, June.
    5. Khan, A.M. & Liang, L. & Mia, M. & Gupta, M.K. & Wei, Z. & Jamil, M. & Ning, H., 2021. "Development of process performance simulator (PPS) and parametric optimization for sustainable machining considering carbon emission, cost and energy aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
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    1. Ardamanbir Singh Sidhu & Sehijpal Singh & Raman Kumar & Danil Yurievich Pimenov & Khaled Giasin, 2021. "Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study," Energies, MDPI, vol. 14(16), pages 1-39, August.

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