Multi-Model Machine Learning based Industrial Vision Framework for Assembly Part Quality Control

Schwab M, Madeline-Derou C, Klarmann S, Thielen N, Meier S, Franke J, Chintanippu S, Stork W (2022)

Publication Type: Conference contribution

Publication year: 2022

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2022-September

Conference Proceedings Title: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

Event location: Stuttgart, DEU

ISBN: 9781665499965

DOI: 10.1109/ETFA52439.2022.9921587

Abstract

The predicted drop in prices for automotive sensors and their increasing demand are putting pressure on sensor suppliers. One possible solution is to reduce production costs by expanding automation. Nevertheless, visual quality control in particular is a process step that is often performed by human inspectors, even in the age of Industry 4.0. Software solutions can currently not be used for all types of sensor assembly quality control. This is mainly due to the difficulty of detecting both structural and logical errors and evaluating their severity. We present a machine learning-based software framework that is able to mimic the methodical behavior of a human in error detection and assessment. The framework is based on three types of models, an object recognition model, an anomaly detection model, and a segmentation model. All models are based on convolutional neural networks. An initial proof of concept (PoC) has been performed to prove the usefulness of the models and shows promising results. The initial anomaly detection model is able to reduce the number of objects to be manually tested by 16%. The object detection and segmentation are still in progress and could not be evaluated yet. In addition, a dataset preparation method is presented to use data from industrial practice and relabel it with information from an inspector survey.

Authors with CRIS profile

Nils Thielen Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Sven Meier Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik

Involved external institutions

Valeo S.A. FR France (FR) Karlsruhe Institute of Technology (KIT) DE Germany (DE) Ancud IT-Beratung GmbH DE Germany (DE)

How to cite

APA:

Schwab, M., Madeline-Derou, C., Klarmann, S., Thielen, N., Meier, S., Franke, J.,... Stork, W. (2022). Multi-Model Machine Learning based Industrial Vision Framework for Assembly Part Quality Control. In IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. Stuttgart, DEU: Institute of Electrical and Electronics Engineers Inc..

MLA:

Schwab, Maximilian, et al. "Multi-Model Machine Learning based Industrial Vision Framework for Assembly Part Quality Control." Proceedings of the 27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022, Stuttgart, DEU Institute of Electrical and Electronics Engineers Inc., 2022.

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