Transparent machine learning for crimp force monitoring using phase-based SHAP explanations
Year: 2026
Type: article
Abstract: Ensuring consistent product quality in modern manufacturing is crucial, particularly in safety-critical applications. Conventional quality control approaches rely on manually defined features and lack adaptability to the complexity and variability inherent in production data. Conversely, data-driven methods, such as machine learning, demonstrate high detection performance but typically function as black-box models, thereby limiting their acceptance in industrial environments. This paper introduces a methodology for industrial fault detection in the domain of crimping, a safety-critical joining technique, which is both data-driven and transparent. The approach integrates a supervised machine learning model for multi-class fault classification, Shapley Additive Explanations for post-hoc interpretability and a domain-specific visualization technique that maps model explanations to interpretable features. The model explanations are assessed with a quantitative perturbation analysis and the visualization technique is evaluated qualitatively by domain experts. The approach achieves a fault detection accuracy of 95.9 %, and both quantitative selectivity analysis and qualitative expert evaluations confirmed the relevance of the generated explanations. This case study contributes to data-driven and interpretable quality control systems in manufacturing.
Authors: Bernd Hofmann, Patrick Bründl, Huong Giang Nguyen, Jörg Franke
Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,090
Award: G968210948
OpenAlex ID: W4416844950