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Learning Causal Graphs in Manufacturing Domains Using Structural Equation Models
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DOI：10.1142/S1793351X23630023.png)
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Abstract
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Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known, they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to earlier applications, we do not assume linear relationships leading to more informative results. Furthermore, our results indicate that including expert knowledge seems to be able to reduce the difference between the learned cause-effect relationships and the expert assessment, thus opening a promising direction for future research on manufacturing processes.
Keywords:
Bayesian networks
causal discovery
Industry 4.0
Journal
IF:
1.7
Papers:
250
Citations:
62
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Cited Papers