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Selection of materials in metal additive manufacturing via three-way decision-making

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  • معلومة اضافية
    • الموضوع:
      2023
    • Collection:
      University of Bristol: Bristol Reserach
    • نبذة مختصرة :
      In this paper, an approach for selection of materials in metal additive manufacturing based on three-way decision-making is proposed. The process of this approach is divided into three stages. First, a decision matrix for a material selection problem in metal additive manufacturing is established based on the basic components of the problem and normalised via a ratio model and a unified rule. Second, the summary loss function, conditional probability, and expected losses of each alternative material are calculated according to the weighted averaging operator, grey relational analysis, and the three-way decision theory, respectively. Third, the three-way decision-making results for the problem are generated according to the developed generation rules and the best material for the problem is selected based on the generated results. The application of the approach is illustrated via a material selection example in metal additive manufacturing. The effectiveness of the approach is demonstrated via a quantitative comparison with several existing approaches. The demonstration results suggest that the proposed approach is as effective as the existing approaches and is more flexible and advantageous in solving a material selection problem in metal additive manufacturing.
    • Relation:
      https://research-information.bris.ac.uk/en/publications/703b714b-42c7-4872-aed8-c05e7d0d3ab4
    • الرقم المعرف:
      10.1007/s00170-023-10966-5
    • الدخول الالكتروني :
      https://hdl.handle.net/1983/703b714b-42c7-4872-aed8-c05e7d0d3ab4
      https://research-information.bris.ac.uk/en/publications/703b714b-42c7-4872-aed8-c05e7d0d3ab4
      https://doi.org/10.1007/s00170-023-10966-5
      http://www.scopus.com/inward/record.url?scp=85149102920&partnerID=8YFLogxK
    • Rights:
      info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.860C9899