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<p>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.</p>

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Transparent machine learning for crimp force monitoring using phase-based SHAP explanations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月31日

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  • Bernd Hofmann,
  • Patrick Bründl,
  • Huong Giang Nguyen,
  • Jörg Franke

摘要

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.