Adaptive AI-based causal control: Toward an autonomous factory in solder paste printing - Herchenbach, Marvin; Weinzierl, Sven; Zilker, Sandra; Schwulera, Erik; Matzner, Martin (Computers in Industry, 2025)
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%
Keywords: manufacturing; electronics production; process optimization; control system; causal machine learning; surrogate model

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision - Harl, Maximilian; Herchenbach, Marvin; Kruschel, Sven; Hambauer, Nico; Zschech, Patrick; Kraus, Mathias (Proceedings of the 17th International Conference on Wirtschaftsinformatik (WI), 2022)
In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.<br
Keywords: Computer Vision; Deep Learning; Convolutional Neural Networks; Transfer Learning; Quality Inspection

Explaining Image Classifications with Near Misses, Near Hits and Prototypes: Supporting Domain Experts in Understanding Decision Boundaries - Herchenbach, Marvin; Müller, Dennis; Scheele, Stephan; Schmid, Ute (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
We propose a method for explaining the results of black box image classifiers to domain experts and end users, combining two example-based explanatory approaches: Firstly, prototypes as representative data points for classes, and secondly, contrastive example comparisons in the form of near misses and near hits. A prototype globally explains the relevant characteristics for a entire class, whereas near hit and near miss explain the local decision boundary of a specific prediction. To combine both types of explanations within one framework is novel and we propose that presenting both types of explanations is especially helpful for domain experts in visual domains. To improve the faithfulness of the explanations, we investigated an unbiased, generic embedding and a model-related (model-specific) embedding for handling the images. The proposed approaches are evaluated regarding parameter selection and suitability on two different data sets – the well-known MNIST and a real-world industrial quality control data set. Finally, it is shown how global and local example-based explanation can be combined and realized within a demonstrator.
CRIS-Team Scopus Importer:2022-07-08
Keywords: Example-based explanation; Explainable AI; Near hits; Near misses; Prototypes