3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds

Sumit Sinha; Emile Glorieux; Pasquale Franciosa; Dariusz Ceglarek

doi:10.1117/12.2526062

21 June 2019 3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds

Sumit Sinha, Emile Glorieux, Pasquale Franciosa, Dariusz Ceglarek

Proceedings Volume 11059, Multimodal Sensing: Technologies and Applications; 110590B (2019) https://doi.org/10.1117/12.2526062
Event: SPIE Optical Metrology, 2019, Munich, Germany

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available