Literature DB >> 27990027

Identified research directions for using manufacturing knowledge earlier in the product lifecycle.

Thomas D Hedberg1, Nathan W Hartman2, Phil Rosche3, Kevin Fischer4.   

Abstract

Design for Manufacturing (DFM), especially the use of manufacturing knowledge to support design decisions, has received attention in the academic domain. However, industry practice has not been studied enough to provide solutions that are mature for industry. The current state of the art for DFM is often rule-based functionality within Computer-Aided Design (CAD) systems that enforce specific design requirements. That rule-based functionality may or may not dynamically affect geometry definition. And, if rule-based functionality exists in the CAD system, it is typically a customization on a case-by-case basis. Manufacturing knowledge is a phrase with vast meanings, which may include knowledge on the effects of material properties decisions, machine and process capabilities, or understanding the unintended consequences of design decisions on manufacturing. One of the DFM questions to answer is how can manufacturing knowledge, depending on its definition, be used earlier in the product lifecycle to enable a more collaborative development environment? This paper will discuss the results of a workshop on manufacturing knowledge that highlights several research questions needing more study. This paper proposes recommendations for investigating the relationship of manufacturing knowledge with shape, behavior, and context characteristics of product to produce a better understanding of what knowledge is most important. In addition, the proposal includes recommendations for investigating the system-level barriers to reusing manufacturing knowledge and how model-based manufacturing may ease the burden of knowledge sharing. Lastly, the proposal addresses the direction of future research for holistic solutions of using manufacturing knowledge earlier in the product lifecycle.

Entities:  

Keywords:  collaborative engineering; design for manufacturing; knowledge management; model-based manufacturing; multi-criteria decision making

Year:  2016        PMID: 27990027      PMCID: PMC5155444          DOI: 10.1080/00207543.2016.1213453

Source DB:  PubMed          Journal:  Int J Prod Res        ISSN: 0020-7543            Impact factor:   8.568


  1 in total

1.  Testing the Digital Thread in Support of Model-Based Manufacturing and Inspection.

Authors:  Thomas Hedberg; Joshua Lubell; Lyle Fischer; Larry Maggiano; Allison Barnard Feeney
Journal:  J Comput Inf Sci Eng       Date:  2016-03-08       Impact factor: 1.855

  1 in total
  4 in total

1.  Using graphs to link data across the product lifecycle for enabling smart manufacturing digital threads.

Authors:  Thomas D Hedberg; Bajaj Manas; Jaime A Camelio
Journal:  J Comput Inf Sci Eng       Date:  2020       Impact factor: 1.855

2.  Contextualising manufacturing data for lifecycle decision-making.

Authors:  William Z Bernstein; Thomas D Hedberg; Moneer Helu; Allison Barnard Feeney
Journal:  Int J Prod Lifecycle Manag       Date:  2018

3.  Towards Knowledge Management for Smart Manufacturing.

Authors:  Shaw C Feng; William Z Bernstein; Thomas Hedberg; Allison Barnard Feeney
Journal:  J Comput Inf Sci Eng       Date:  2017-07-24       Impact factor: 1.855

4.  Industry 4.0 Engineering Product Life Cycle Management Based on Multigranularity Access Control Model.

Authors:  Longfei Yu; Shifan Zhu
Journal:  Comput Intell Neurosci       Date:  2022-01-21
  4 in total

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