Scholarlink

This study proposes a three level Decision Support System (DSS) specifically designed to help stakeholders in assessing parts for AM by analysing across diverse decision-making stages. At the product level, our DSS implements a centrality score-based approach for identifying suitable candidate part consolidation from the product/assembly. This method evaluates the centrality score of each part within a product and enhances manufacturing efficiency. Moving to part-level decision making, the system utilizes a shape complexity evaluation employing a view similarity metric. This approach assesses the compatibility of individual parts with AM by calculating the combined shape complexity of each part both internally and externally. Finally, for design variant selection, the DSS compares various design iterations to determine the most AM-compatible option, ensuring optimal functionality at a competitive cost. By integrating this DSS, manufacturers can maximize the benefits of Additive Manufacturing in all stages of product development. At each stage, the effectiveness of the DSS is evaluated with the help of suitable case studies.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development of Interactive Decision Support System for Part Selection in Additive Manufacturing

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Jayakrishnan Jayapal,
  • Senthilkumaran Kumaraguru,
  • Sudhir Varadarajan

摘要

This study proposes a three level Decision Support System (DSS) specifically designed to help stakeholders in assessing parts for AM by analysing across diverse decision-making stages. At the product level, our DSS implements a centrality score-based approach for identifying suitable candidate part consolidation from the product/assembly. This method evaluates the centrality score of each part within a product and enhances manufacturing efficiency. Moving to part-level decision making, the system utilizes a shape complexity evaluation employing a view similarity metric. This approach assesses the compatibility of individual parts with AM by calculating the combined shape complexity of each part both internally and externally. Finally, for design variant selection, the DSS compares various design iterations to determine the most AM-compatible option, ensuring optimal functionality at a competitive cost. By integrating this DSS, manufacturers can maximize the benefits of Additive Manufacturing in all stages of product development. At each stage, the effectiveness of the DSS is evaluated with the help of suitable case studies.