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How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines

Authors :
Christopher Hagedorn
Johannes Huegle
Matthias Uflacker
Source :
IJCAI
Publication Year :
2020
Publisher :
International Joint Conferences on Artificial Intelligence Organization, 2020.

Abstract

The efficiency of modern automotive body shop assembly lines is highly related to the reduction of downtimes due to failures and quality deviations within the manufacturing process. Consequently, the need for implementing tools into the assembly lines for on-line monitoring, and failure diagnosis, also under the prism of improving the troubleshooting, is of great importance. While the identification of root causes and elimination of failures is usually built upon individual on-site expert knowledge, causal graphical models (CGMs) have opened the possibility to make a purely data-driven assessment. In this demo, we showcase how a CGM of the production process is incorporated into a monitoring tool to function as a decision-support system for an operator of a modern automotive body shop assembly line and enables fast and effective handling of failures and quality deviations.

Details

Database :
OpenAIRE
Journal :
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Accession number :
edsair.doi...........b1aa7e41ad4c50f4375a1e3601eae900