Decoding industrial-scale battery manufacturing process through integration of causal graphs into explainable artificial intelligence

Modeling and analyzing industrial-scale lithium-ion battery (LIB) manufacturing process present significant challenges due to the numerous variables and their complex interrelationships. While previous studies have utilized machine learning and explainable artificial intelligence to discern complex patterns and identify crucial variables from data, these methods often overlook the causal connections among input variables. This oversight can potentially lead to inaccurate interpretations and limited insights, particularly regarding how influences accumulate throughout the process. To address these limitations, this study introduces a comprehensive modeling and explanatory framework that incorporates causal information among input variables. By employing the Shapley flow algorithm, which propagates attributions along the edges of the causal graph, our framework successfully identified key process variables that could have not been isolated under conventional approaches. Furthermore, a detailed analysis of impact pathways for individual process parameters was also obtained by focusing on relevant edges. Through preliminary validation with a simulated system and subsequent application to real-world data from leading commercial LIB manufacturing enterprise, we confirmed the method’s efficacy in accurately pinpointing significant variables. Our analysis also introduced novel insights into the impact pathways of process parameters, previously unexplored by previous approaches. This new understanding offered engineers deeper insights and actionable strategies, boosting the potential for enhanced process analysis and decision-making capabilities.