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Model Interpretability, Explainability and Trust for Manufacturing 4.0

In: Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches

Author

Listed:
  • Bianca Maria Colosimo

    (Politecnico di Milano, Department of Mechanical Engineering)

  • Fabio Centofanti

    (University of Naples Federico II, Department of Industrial Engineering)

Abstract

Manufacturing is currently characterized by a widespread availability of multiple streams of big data (e.g., signals, images, video-images, 3-dimensional voxel and mesh-based reconstructions of volumes and surfaces). Manufacturing 4.0 refers to the paradigm shift involving appropriate use of all this rich data environment for decision making in prognostic, monitoring, optimization and control of the manufacturing processes. The paper discusses how the new advent of Artificial Intelligence for manufacturing data mining poses new challenges on model interpretability, explainability and trust. Starting from this general overview, the paper then focuses on examples of big data mining in Additive Manufacturing. A real case study focusing on spatter modeling for process optimization is discussed, where a solution based on robust functional analysis of variance is proposed.

Suggested Citation

  • Bianca Maria Colosimo & Fabio Centofanti, 2022. "Model Interpretability, Explainability and Trust for Manufacturing 4.0," Springer Books, in: Antonio Lepore & Biagio Palumbo & Jean-Michel Poggi (ed.), Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches, chapter 0, pages 21-36, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-12402-0_2
    DOI: 10.1007/978-3-031-12402-0_2
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