Herchenbach M, Weinzierl S, Zilker S, Schwulera E, Matzner M (2025)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2025
URI: https://www.sciencedirect.com/science/article/pii/S0166361525000211
DOI: 10.1016/j.compind.2025.104256
In recent years, there has been a remarkable shift from automated plants to intelligent production in the industrial context, accelerated by technologies such as artificial intelligence (AI). The ultimate goal is an autonomous plant that is capable of self-regulation and self-optimization. In electronics production, the first approaches have been proposed for deriving and adjusting machine parameters for solder paste printing in the surface-mount technology production of printed circuit boards. However, these approaches are often static and perform reactive actions since they are either based on expert systems or data-driven models. To reach a dynamic optimization, this work proposes a methodology, called adaptive AI-based causal control, allowing offline and online optimization. Following the principles of the Design for Six Sigma method, customer-oriented key performance indicators were derived, that aimed at a stable soldering process by focusing on the spread of the solder volume and a dedicated overall spread metric. The offline optimization (open-loop control) is based on a surrogate model approach to find optimal initial printing parameters. The online optimization (closed-loop control) employs a data-driven model predictive control to adjust the printing parameters dynamically. In addition, to consider the causal effects of the control variables in the online optimization, a causal graph is exploited in the predictive controller. Regarding the effectiveness of the open-loop control, our evaluation reveals a reduction in spread by 11.3% in production. Furthermore, in terms of the efficacy of the closed-loop control, we obtain a reduction in volume range by 16.7% in a simulated setting of the predictive controller. Thereby, the integration of a causal inference component based on a generated causal graph, achieving a recall of 76.9% by considering process knowledge identified with domain experts, accounts for about 2.8%.
APA:
Herchenbach, M., Weinzierl, S., Zilker, S., Schwulera, E., & Matzner, M. (2025). Adaptive AI-based causal control: Toward an autonomous factory in solder paste printing. Computers in Industry. https://doi.org/10.1016/j.compind.2025.104256
MLA:
Herchenbach, Marvin, et al. "Adaptive AI-based causal control: Toward an autonomous factory in solder paste printing." Computers in Industry (2025).
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