A novel hybrid explainable artificial intelligence modelling approach for smart manufacturing

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Abhilash, Puthanveettil Madathil and Luo, Xichun and Liu, Qi and Qin, Yi (2026) A novel hybrid explainable artificial intelligence modelling approach for smart manufacturing. The International Journal of Advanced Manufacturing Technology. ISSN 1433-3015 (https://doi.org/10.1007/s00170-025-17157-4)

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Abstract

Modelling complex manufacturing processes presents significant challenges related to accuracy and explainability. Physics-based models, while interpretable and generalizable, often suffer from reduced accuracy due to simplifications and incomplete system understanding. On the other hand, purely data-driven models are typically more accurate but lack transparency, limiting their trust and adoption in critical manufacturing applications. Existing hybrid approaches attempt to address these issues but often retain black-box AI components that compromise interpretability. In this study, we propose a novel hybrid modelling framework that intrinsically integrates physics-based models with explainable AI, to correct for modelling inaccuracies. This approach offers both high accuracy and transparent, traceable decision-making. Its effectiveness is demonstrated through a case study predicting the real-time position of cutting tools from accelerometer signals during ultra-precision diamond turning.

ORCID iDs

Abhilash, Puthanveettil Madathil ORCID logoORCID: https://orcid.org/0000-0001-5655-6196, Luo, Xichun ORCID logoORCID: https://orcid.org/0000-0002-5024-7058, Liu, Qi and Qin, Yi ORCID logoORCID: https://orcid.org/0000-0001-7103-4855;
CORE (COnnecting REpositories)