The Library
Object shape error response using Bayesian 3D convolutional neural networks for assembly systems with compliant parts
Tools
Sinha, Sumit, Franciosa, Pasquale and Ceglarek, Darek (2021) Object shape error response using Bayesian 3D convolutional neural networks for assembly systems with compliant parts. IEEE Transactions on Industrial Informatics , 17 (10). pp. 6676-6686. doi:10.1109/TII.2020.3043226 ISSN 1551-3203.
|
PDF
WRAP-Object-shape-error-response-Bayesian-3D-neural-networks-assembly-parts-Ceglarek-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (19Mb) | Preview |
|
PDF
WRAP-Object-shape-error-response-Bayesian-3D-neural-networks-assembly-parts-Ceglarek-2020.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (20Mb) |
Official URL: https://doi.org/10.1109/TII.2020.3043226
Abstract
The paper proposes a novel Object Shape Error Response (OSER) approach to estimate the dimensional and geometric variation of assembled products and then, relate, these to process parameters, which can be interpreted as root causes (RC) of the object shape defects. The OSER approach leverages Bayesian 3D-Convolutional Neural Networks integrated with Computer-Aided Engineering (CAE) simulations for RC isolation. Compared with existing methods, the proposed approach (i)addresses a novel problem of applying deep learning for object shape error identification instead of object detection; (ii)overcomes fundamental performance limitations of current linear approaches for Root Cause Analysis (RCA) that cannot be used on point cloud data; and, (iii)provides capabilities for unsolved challenges such as ill-conditioning, fault-multiplicity, RC isolation with uncertainty quantification and learning at design phase when no measurement data is available. Comprehensive benchmarking with machine learning models demonstrates superior performance with R2=0.98 and MAE=0.05 mm, thus improving RCA capabilities by 29%.
Item Type: | Journal Article | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TS Manufactures |
||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||||||
Library of Congress Subject Headings (LCSH): | Machine learning, Neural networks (Computer science), Root cause analysis , Manufacturing processes -- Computer simulation | ||||||||||||
Journal or Publication Title: | IEEE Transactions on Industrial Informatics | ||||||||||||
Publisher: | IEEE | ||||||||||||
ISSN: | 1551-3203 | ||||||||||||
Official Date: | October 2021 | ||||||||||||
Dates: |
|
||||||||||||
Volume: | 17 | ||||||||||||
Number: | 10 | ||||||||||||
Page Range: | pp. 6676-6686 | ||||||||||||
DOI: | 10.1109/TII.2020.3043226 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Re-use Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 8 December 2020 | ||||||||||||
Date of first compliant Open Access: | 10 December 2020 | ||||||||||||
RIOXX Funder/Project Grant: |
|
||||||||||||
Conference Paper Type: | Paper | ||||||||||||
Title of Event: | 18th IEEE International Conference on Industrial Informatics (INDIN) | ||||||||||||
Type of Event: | Conference | ||||||||||||
Location of Event: | Coventry | ||||||||||||
Date(s) of Event: | 20-23 Jul 2020 | ||||||||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year