Zhao, Zhiwei, Li, Yingguang, Liu, Changqing, Liu, Xu and Gao, James ORCID: 0000-0001-5625-3654 (2024) Stable data-driven manufacturing decision-making by introducing causal relationships for high-dimensional data. IEEE Transactions on Industrial Informatics. ISSN 1551-3203 (Print), 1941-0050 (Online) (doi:https://doi.org/10.1109/TII.2024.3453431)

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Abstract

In digital manufacturing, data-driven methods are promising to revolutionize various decision-making processes. However, the relationships between variables in high-dimensional data of data-driven decision-making methods are only correlations. Important causal relationships and knowledge between process variables are not considered. Therefore, existing data-driven systems are unstable, which could result in unreliable and dangerous decisions. To establish a stable decision-making model for complex processes with high-dimensional data, a causal-based decision-making framework that combined causal relationships and knowledge between key manufacturing variables was proposed. The causal relationships between state, decision, and objective data were established in the form of a direct acyclic graph formed by breaking an unexcepted loop between variables using a shadow objective variable. Then, causal knowledge of high-dimensional states was introduced to the neural network, forming a stable decision-making model. Compared with data-driven methods used in robotics and manufacturing scenarios, the proposed framework provided better and more stable decisions, particularly in noised environments.

Item Type:	Article
Uncontrolled Keywords:	manufacturing decision-making, data-driven, causal relationship, causal loop
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	29 Oct 2024 14:10
URI:	http://gala.gre.ac.uk/id/eprint/47836

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