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3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds
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Sinha, Sumit, Glorieux, Emile, Franciosa, Pasquale and Ceglarek, Dariusz (2019) 3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds. In: SPIE Optical Metrology, Munich, Germany, 24-27 Jun 2019. Published in: Proceedings of SPIE : Multimodal Sensing: Technologies and Applications, 11059 p. 10. ISSN 0277-786X. doi:10.1117/12.2526062
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WRAP-3D-convolutional-neural-networks-estimate-assembly-parameters-Sinha-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1651Kb) | Preview |
Official URL: http://dx.doi.org/10.1117/12.2526062
Abstract
Closed loop dimensional quality control for an assembly system entails controlling process parameters based on dimensional quality measurement data to ensure that products conform to quality requirements. Effective closed-loop quality control reduces machine downtime and increases productivity, as well as enables efficient predictive maintenance and continuous improvement of product quality. Accurate estimation of dimensional variations on the final part is a key requirement, in order to detect and correct process faults, for effective closed-loop quality control. Nowadays, this is often done by experienced process engineers, using a trial-and-error approach, which is time-consuming and can be unreliable. In this paper, a novel model to estimate process parameters error variations using high-density cloud-of-point measurement data captured by 3D optical scanners is proposed. The proposed model termed as PointDevNet uses 3D convolutional neural networks (CNN) that leverage the deviations of key nodes and their local neighbourhood to estimate the process parameter variations. These process parameters variation estimates are leveraged for root cause isolation as a necessary but currently missing step needed for the development of closed-loop quality control framework. The proposed model is compared with an existing state-of-the-art linear model under different scenarios such as a single and multiple root causes, and the presence of measurement noise. The state-of-the-art model is evaluated under different point selections and results are compared to the proposed model with consideration to an industrial case study involving a sheet metal part, i.e. window reinforcement panel.
| Item Type: | Conference Item (Paper) | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Image processing -- Digital techniques, Machine learning, Computer-aided engineering | ||||||
| Journal or Publication Title: | Proceedings of SPIE : Multimodal Sensing: Technologies and Applications | ||||||
| Publisher: | SPIE | ||||||
| ISSN: | 0277-786X | ||||||
| Book Title: | Multimodal Sensing: Technologies and Applications | ||||||
| Official Date: | 21 June 2019 | ||||||
| Dates: |
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| Date of first compliant deposit: | 2 July 2019 | ||||||
| Volume: | 11059 | ||||||
| Page Range: | p. 10 | ||||||
| Article Number: | 110590B | ||||||
| DOI: | 10.1117/12.2526062 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | Sinha, Sumit, Glorieux, Emile, Franciosa, Pasquale and Ceglarek, Dariusz (2019) 3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds. In: SPIE Optical Metrology, Munich, Germany, 24-27 Jun 2019. Published in: Proceedings of SPIE : Multimodal Sensing: Technologies and Applications, 11059 http://dx.doi.org/10.1117/12.2526062I Copyright 2019 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Funder: | This study has been supported by the UK EPSRC project EP/K019368/1: “Self-Resilient Reconfigurable Assembly Systems with In-process Quality Improvement� and the WMG-IIT scholarship offered by WMG, University of Warwick | ||||||
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| Conference Paper Type: | Paper | ||||||
| Title of Event: | SPIE Optical Metrology | ||||||
| Type of Event: | Workshop | ||||||
| Location of Event: | Munich, Germany | ||||||
| Date(s) of Event: | 24-27 Jun 2019 |
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