How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines
Johannes Huegle, Christopher Hagedorn, Matthias Uflacker
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.
BibTeX
@inproceedings{ijcai2020p758,
title = {How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines},
author = {Huegle, Johannes and Hagedorn, Christopher and Uflacker, Matthias},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {5246--5248},
year = {2020},
month = {7},
note = {Demos},
doi = {10.24963/ijcai.2020/758},
url = {https://doi.org/10.24963/ijcai.2020/758},
}