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Proposal of a Combined AHP-PROMETHEE Decision Support Tool for Selecting Sustainable Machining Process Based on Toolpath Strategy and Manufacturing Parameters

Author

Listed:
  • Hadhami Ben Slama

    (Mechanical Engineering Laboratory, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia)

  • Raoudha Gaha

    (Laboratoire Roberval, Département D’Ingenierie Mecanique, Université de Technologie de Compiègne, 60203 Compiegne, France)

  • Mehdi Tlija

    (Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

  • Sami Chatti

    (Mechanical Engineering Laboratory, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia)

  • Abdelmajid Benamara

    (Mechanical Engineering Laboratory, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia)

Abstract

Sustainable manufacturing technologies are the new challenge faced by enterprises, industries, and researchers. The development of a sustainability-based assessment method considering the environmental and economic impacts is crucial to realize viable manufacturing. However, few studies have addressed environmental economics and social flows using a common perspective. Mechanical machining is one of the most-used manufacturing techniques. The overall ecological, economic, and social footprint requires accurate and effective estimation and optimization. Several studies have addressed this issue by examining the entire process of machining, but sustainability flows for machining parameters and toolpaths have remained relatively unexplored. The lack of systematic assistance tools bridging the gap between decision-maker preferences and the three sustainability pillars—economic, social, and environmental—has impeded the widespread adoption of sustainable machining practices. To this end, this paper proposes an integrated approach to the decision-making problem that combines the Analytical Hierarchy Process (AHP) with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) for selecting a sustainable machining strategy. The sustainability criteria are driven by manufacturing process parameters commonly employed and regulated during the manufacturing phase. This includes toolpath strategies as a qualitative input factor and manufacturing parameters such as cutting speed, feed rate, depth of cut, and stepover as quantitative input factors, affirming the practical applicability of the method in industrial contexts. New fundamental methods are also presented for selecting the most efficient machining parameters and toolpaths according to the weights assigned to each ecological, social, and economic footprint by the decision-maker (the manufacturer or production manager). In this way, sustainable machining strategies in the manufacturing industry will be strengthened in integrity. In a case study of part-end milling, both manufacturing parameters and toolpath strategies are considered to establish sustainable feature-based machining decisions.

Suggested Citation

  • Hadhami Ben Slama & Raoudha Gaha & Mehdi Tlija & Sami Chatti & Abdelmajid Benamara, 2023. "Proposal of a Combined AHP-PROMETHEE Decision Support Tool for Selecting Sustainable Machining Process Based on Toolpath Strategy and Manufacturing Parameters," Sustainability, MDPI, vol. 15(24), pages 1-20, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16861-:d:1300493
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    References listed on IDEAS

    as
    1. Chen, Xingzheng & Li, Congbo & Tang, Ying & Li, Li & Du, Yanbin & Li, Lingling, 2019. "Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time," Energy, Elsevier, vol. 175(C), pages 1021-1037.
    2. Misbah Niamat & Shoaib Sarfraz & Wasim Ahmad & Essam Shehab & Konstantinos Salonitis, 2019. "Parametric Modelling and Multi-Objective Optimization of Electro Discharge Machining Process Parameters for Sustainable Production," Energies, MDPI, vol. 13(1), pages 1-20, December.
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