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21 June 2019 3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds
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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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sumit Sinha, Emile Glorieux, Pasquale Franciosa, and Dariusz Ceglarek "3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds", Proc. SPIE 11059, Multimodal Sensing: Technologies and Applications, 110590B (21 June 2019); https://doi.org/10.1117/12.2526062
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Cited by 2 scholarly publications.
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KEYWORDS
Data modeling

3D modeling

Manufacturing

Metals

Systems modeling

3D metrology

Convolutional neural networks

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