How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines
How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines
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因果结构知识如何为汽车车身车间装配线的监控添加决策支持
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
M. Uflacker
中科院分区：
文献类型：
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作者：
Johannes Huegle;C. Hagedorn;M. Uflacker
现代汽车车身车间装配线的效率与减少制造过程中的故障和质量偏差引起的停机时间密切相关。因此，需要将工具实施到装配线中以进行在线监测和故障诊断，并且在改善故障排除的棱镜下，这是非常重要的。虽然根本原因的识别和故障的消除通常是建立在个人的现场专家知识，因果图模型（CGMs）已经打开了一个纯粹的数据驱动的评估的可能性。在此演示中，我们展示了如何将生产过程的CGM集成到监控工具中，作为现代汽车车身车间装配线操作员的决策支持系统，并快速有效地处理故障和质量偏差。
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.