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Scalable Concept Extraction in Industry 4.0

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Explainable Artificial Intelligence (xAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1903))

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Abstract

The industry 4.0 is leveraging digital technologies and machine learning techniques to connect and optimize manufacturing processes. Central to this idea is the ability to transform raw data into human understandable knowledge for reliable data-driven decision-making. Convolutional Neural Networks (CNNs) have been instrumental in processing image data, yet, their “black box” nature complicates the understanding of their prediction process. In this context, recent advances in the field of eXplainable Artificial Intelligence (XAI) have proposed the extraction and localization of concepts, or which visual cues intervene on the prediction process of CNNs. This paper tackles the application of concept extraction (CE) methods to industry 4.0 scenarios. To this end, we modify a recently developed technique, “Extracting Concepts with Local Aggregated Descriptors” (ECLAD), improving its scalability. Specifically, we propose a novel procedure for calculating concept importance, utilizing a wrapper function designed for CNNs. This process is aimed at decreasing the number of times each image needs to be evaluated. Subsequently, we demonstrate the potential of CE methods, by applying them in three industrial use cases. We selected three representative use cases in the context of quality control for material design (tailored textiles), manufacturing (carbon fiber reinforcement), and maintenance (photovoltaic module inspection). In these examples, CE was able to successfully extract and locate concepts directly related to each task. This is, the visual cues related to each concept, coincided with what human experts would use to perform the task themselves, even when the visual cues were entangled between multiple classes. Through empirical results, we show that CE can be applied for understanding CNNs in an industrial context, giving useful insights that can relate to domain knowledge.

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Notes

  1. 1.

    The dataset is available under the link: https://doi.org/10.5281/zenodo.7970596.

  2. 2.

    The dataset is available under the link: https://doi.org/10.5281/zenodo.7970490.

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Acknowledgements

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy-EXC-2023 Internet of Production-390621612.

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Authors and Affiliations

  1. Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Dennewartstr. 27, 52068, Aachen, Germany

    Andrés Felipe Posada-Moreno, Friedrich Solowjow & Sebastian Trimpe

  2. Institut für Textiltechnik of RWTH Aachen University, Otto-Blumenthal-Straße 1, 52074, Aachen, Germany

    Kai Müller, Florian Brillowski & Thomas Gries

Authors
  1. Andrés Felipe Posada-Moreno
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Correspondence to Andrés Felipe Posada-Moreno .

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  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

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Posada-Moreno, A.F., Müller, K., Brillowski, F., Solowjow, F., Gries, T., Trimpe, S. (2023). Scalable Concept Extraction in Industry 4.0. In: Longo, L. (eds) Explainable Artificial Intelligence. xAI 2023. Communications in Computer and Information Science, vol 1903. Springer, Cham. https://doi.org/10.1007/978-3-031-44070-0_26

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  • DOI: https://doi.org/10.1007/978-3-031-44070-0_26

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