Towards Identifying the Elements of a Minimum Information Model for Use in a Model-Based Definition
Alexandar M. Miller, Nathan W. Hartman, Thomas D. Hedberg Jr., Allison Barnard Feeney, Jesse Zahner
The concept of Model-Based Definition (MBD) is being integrated into manufacturing companies in a variety of industries. Companies benefit from enhanced visualization, documentation, and communication capabilities offered by a 3D product representation when 2D drawings are replaced with 3D annotated models. In this transition, it is necessary that product information is not lost. A complication arises from the amount of product information defined implicitly in drawings. This presents a challenge when authoring and translating 3D models through the product lifecycle. It requires a semantic understanding of the drawing to extract the implicit information. The capture of implicit and explicit information is critical to successfully transition to MBD. The research study described in this paper has yielded the term Minimum Information Model (MIM) to describe the minimum amount of information necessary in a given workflow, with the understanding that any model-based definition must be as rich in its implicit and explicit levels of information as drawings were historically. A survey was conducted across various industry sectors to identify the foundational elements of MIM in selected workflows. This study identified the information used within the specific workflows, the capabilities of 3D CAD models to carry this information, and the implications for doing so.
Proceedings of the ASME 2017 International Manufacturing Science and Engineering Conference
June 4-8, 2017
Los Angeles, CA, US
ASME 2017 International Manufacturing Science and Engineering Conference
, Hartman, N.
, Hedberg Jr., T.
, Barnard Feeney, A.
and Zahner, J.
Towards Identifying the Elements of a Minimum Information Model for Use in a Model-Based Definition, Proceedings of the ASME 2017 International Manufacturing Science and Engineering Conference, Los Angeles, CA, US, [online], https://doi.org/10.1115/MSEC2017-2979, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922587
(Accessed November 29, 2023)