Feasibility study for an automated engineering change process

Abstract

Engineering change is a significant cost for projects. While avoiding and mitigating the risk of change is ideal, mistakes and improvements are recognised as more is learned about the decisions made in a design. This paper presents a feasibility and performance analysis of automating engineering change requests to demonstrate the promise for increasing speed, efficiency, and effectiveness of product-lifecycle-wide engineering-change-requests. A comparatively simple case is examined to mimic the reduced set of alterable aspects of a typical change request and to highlight the need of appropriate search algorithms as brute force methods are prohibitively resource intensive. Although such cases may seem trivial for human agents, with the volume of expected change requests in a typical facility, the potential opportunity gain by eliminating or reducing the amount of human effort in low-level changes accumulate into significant returns for the industry on time and money. Herein, the genetic algorithm is selected to demonstrate feasibility with its broad scope of applicability and low barriers to deployment. Future refinement of this or other sophisticated algorithms leveraging the nature of the standard representations and qualities of alterable design features could produce tools with strong implications for process efficiency and industry competitiveness in its projects execution.

Keywords:

• Engineering change request
• inspectability
• design-for-inspection
• design-for-manufacturing
• design automation

Acknowledgments

The authors thank Allison Barnard Feeney, Timothy Sprock, and Vijay Srinivasan from NIST, Larry Maggiano from Mitutoyo America, and the peer-reviewers for their comments and input to this paper. This work is done in partial fulfilment of the requirements for initiative funding to study Model-based Product Quality Measurement.

Disclaimers

The work presented in this paper is an official contribution of the National Institute of Standards and Technology (NIST) and not subject to copyright in the United States. Certain commercial systems are identified in this paper. Such identification does not imply recommendation or endorsement by NIST. Nor does it imply that the products identified are necessarily the best available for the purpose.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Note that there exist commercial software specialised in simulating production systems that include fully described kinematic descriptions of machines. This paper does not intend to compete with these powerful tools, but rather focuses on the pipeline of necessary data transfer and linking.

2 3D PDF is a combination of technologies that embed a 3D model, using the PRC format, into a PD container. For more information on the technology, visit http://3dpdfconsortium.org/project/change-request-tetra-4d/

3 Amazon Web Services EC2 t3a.xlarge instance, US East (N. Virginia), 4 cores, 16 GB RAM, 50 GB storage.

Additional information

Notes on contributors

M. E. Sharp

M. E. Sharp is a Reliability Engineer at the National Institute of Standards and Technology (NIST) located in Gaithersburg, MD. He received a B.S. (2007), M.S. (2009), and Ph.D. (2012) in Nuclear Engineering from the University of Tennessee, Knoxville, TN, USA. His research interests include signal analytics, machine learning, artificial intelligence, optimisation, and natural language processing. Michael has worked on a wide array of projects, including image processing for elemental material recognition, navel reliability monitoring, and manufacturing robotics diagnostic monitoring. He currently works with the NIST Systems Integration Division for Smart manufacturing.

T. D. Hedberg

T. D. Hedberg Jr is a mission-area lead at the Applied Research Laboratory for Intelligence and Security (ARLIS). Dr Hedberg joined ARLIS from the National Institute of Standards and Technology (NIST), where he was the Program Manager of the Model-Based Enterprise (MBE) program and the Co-Leader of the Smart Manufacturing Systems Test Bed, which earned him the U.S. Department of Commerce Gold Medal. Dr Hedberg is the Chair of the ASME MBE Standards Committee, a member of the several ASME Y14 committees, and a past member of ISO TC184/SC4/WGs 11, 12, 15, 21 and the ISO TC184/SC4 Quality Committee. Dr Hedberg earned a B.S. in Aeronautical and Astronautical Engineering from Purdue University, a M.Eng. in Engineering Management from the Pennsylvania State University, and a Ph.D. in Industrial and Systems Engineering from the Virginia Polytechnic Institute and State University.

W. Z. Bernstein

W. Z. Bernstein leads the Extended Digital Thread project at the National Institute of Standards and Technology. He contributes to standards focused on integrating manufacturing and design knowledge to realise the digital thread. He has received multiple honours and awards including the 2019 ASME CIE Young Engineer Award and the U.S. Department of Commerce 2020 Bronze Medal Award. Dr Bernstein co-chairs the AME ManTech Subpanel Working Group on Digital Information Visualisation, chairs the NIST-internal Extended Reality Community of Interest, and serves on the ASME DFMLC Technical Committee. He received his PhD and MS in mechanical engineering from Purdue University.

S. Kwon

S. Kwon is an Assistant Professor at the Kumoh National Institute of Technology, Republic of Korea. Previously, he worked as a Guest Researcher at the National Institute of Standards and Technology, USA. His research interests include computer-aided design, design for additive manufacturing, industrial data standards, and digital transformation. He received a B.S. in Naval Architecture and Ocean Engineering from Inha University, an M.S. in Ocean Systems Engineering, and a Ph.D. in Mechanical Engineering from KAIST, Republic of Korea.