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Research Article
Bhairavsingh Ghorpade
The University of Oklahoma - Norman Campus: The University of Oklahoma
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9591-9631
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Part design is the principal source of communicating design intent to manufacturing and inspection. The design data is often communicated through CAD systems. Modern analytics tools and artificial intelligence integration into manufacturing has significantly advanced machine recognition of design specification and manufacturing constraints. This paper is aimed at the collaboration among multiple vendors across supply chains to enable efficient order procurement. To this end, the paper discusses the development of a simple framework for extracting the dimensional data from part design and storing them for enhancing machine readability of the part design at multiple levels of manufacturing.
Keywords
Manufacturing democratization, STEP file, CAD design, Part dimensional data extraction
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CURRENT STATUS: Under Review
Version 1
Posted 11 May, 2021
No community comments so far
Reviews received
Received 10 May, 2021
Reviewers invited
Invitations sent on 10 May, 2021
Editor assigned
On 09 May, 2021
First submitted
On 23 Apr, 2021
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Subject Areas
Materials Engineering
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This preprint is under consideration at The International Journal of Advanced Manufacturing Technology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Bhairavsingh Ghorpade
The University of Oklahoma - Norman Campus: The University of Oklahoma
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9591-9631
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 11 May, 2021
No community comments so far
Reviews received
Received 10 May, 2021
Reviewers invited
Invitations sent on 10 May, 2021
Editor assigned
On 09 May, 2021
First submitted
On 23 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Materials Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Part design is the principal source of communicating design intent to manufacturing and inspection. The design data is often communicated through CAD systems. Modern analytics tools and artificial intelligence integration into manufacturing has significantly advanced machine recognition of design specification and manufacturing constraints. This paper is aimed at the collaboration among multiple vendors across supply chains to enable efficient order procurement. To this end, the paper discusses the development of a simple framework for extracting the dimensional data from part design and storing them for enhancing machine readability of the part design at multiple levels of manufacturing.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
This is a list of supplementary files associated with this preprint. Click to download.
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