Stable Data-Driven Manufacturing Decision- Making by Introducing Causal Relationships for High-Dimensional Data

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.