A Framework for Set-based Manufacturing Analysis and Visual Feedback

Open Access
Author:
Kim, Wonmo
Graduate Program:
Industrial Engineering
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
December 19, 2014
Committee Members:
  • Timothy William Simpson, Dissertation Advisor
  • Timothy William Simpson, Committee Chair
  • Soundar Rajan Tirupatikumara, Committee Member
  • Sanjay B Joshi, Committee Member
  • Christopher J Saldana, Committee Member
  • Mary I Frecker, Committee Member
  • Daniel Antion Finke, Special Member
Keywords:
  • manufacturing analysis
  • design for manufacturing
  • visual manufacturing feedback
  • set-based approach
Abstract:
Design changes and subsequent verifications happen frequently during the development stages for a complex product. These iterative loops between design, manufacturing, and testing delay the entire product development process. This research introduces a framework that shortens or reduces these iterative loops by letting designers perform manufacturing feasibility checks on multiple manufacturing processes at the early design stage. If feedback information for manufacturability of a design can be delivered to designers in a timely manner, then it can significantly reduce the entire product development cycle. A set-based manufacturing analysis and feedback framework is proposed to provide early, fast, and informative visual feedback on manufacturability to designers for a complex product for multiple manufacturing processes. Instead of applying automatic reasoning algorithms, questions regarding part geometry are asked directly of designers. Using the obtained geometric information, the framework analyzes manufacturability in terms of part geometry with respect to a given set of process capabilities based on Design for Manufacturing (DFM) guidelines. To minimize the number of questions, a method that derives process decision blocks for manufacturing process families is proposed. A manufacturing process family is a set of processes that share the same geometric parameters among related DFM guidelines. The process decision block is then refined either heuristically using statistical data from a product domain or analytically based on geometric connections between parameters in the decision block. Using the process decision block, infeasible manufacturing processes are quickly screened out for subsequent analysis. Moreover, the proposed framework provides efficient and dynamic visual feedback for geometric advice at the feature level for each selected manufacturing process. Through dynamic visual feedback generation, designers can quickly search for design alternatives while maintaining manufacturability.