Learning Causal Graphs in Manufacturing Domains using Structural Equation Models

TL;DR

This paper demonstrates how Structural Equation Models can uncover complex cause-and-effect relationships in manufacturing processes by integrating prior knowledge with data, moving beyond linear assumptions for more informative insights.

Contribution

It introduces a method for applying SEMs to manufacturing data without assuming linearity, enhancing the discovery of causal relationships.

Findings

Effective identification of causal relationships in manufacturing

Improved process understanding through SEMs

Non-linear SEM application yields richer insights

Abstract

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 existing applications, we do not assume linear relationships leading to more informative results.