Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge

• URL: http://arxiv.org/abs/2305.00229v1
• Date: Sat, 29 Apr 2023 10:54:57 GMT
• Title: Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge
• Authors: Jeremy Cleeman, Kian Agrawala, Rajiv Malhotra
• Abstract summary: This paper proposes a transfer learning based approach to address this issue. A ML model is trained on a large amount of computationally inexpensive data from a physics-based process model (source) and then fine-tuned on a smaller amount of costly experimental data (target) Despite extreme functional and quantitative inaccuracies in the source our approach reduces the model development cost by years, experimental cost by 56-76%, computational cost by orders of magnitude, and prediction error by 16-24%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Learning (ML) is of increasing interest for modeling parametric effects in manufacturing processes. But this approach is limited to established processes for which a deep physics-based understanding has been developed over time, since state-of-the-art approaches focus on reducing the experimental and/or computational costs of generating the training data but ignore the inherent and significant cost of developing qualitatively accurate physics-based models for new processes . This paper proposes a transfer learning based approach to address this issue, in which a ML model is trained on a large amount of computationally inexpensive data from a physics-based process model (source) and then fine-tuned on a smaller amount of costly experimental data (target). The novelty lies in pushing the boundaries of the qualitative accuracy demanded of the source model, which is assumed to be high in the literature, and is the root of the high model development cost. Our approach is evaluated for modeling the printed line width in Fused Filament Fabrication. Despite extreme functional and quantitative inaccuracies in the source our approach reduces the model development cost by years, experimental cost by 56-76%, computational cost by orders of magnitude, and prediction error by 16-24%.

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