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Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning

Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima...

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Autores principales: Zeng, Shi, Pi, Dechang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10222628/
https://www.ncbi.nlm.nih.gov/pubmed/37430883
http://dx.doi.org/10.3390/s23104969
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author Zeng, Shi
Pi, Dechang
author_facet Zeng, Shi
Pi, Dechang
author_sort Zeng, Shi
collection PubMed
description Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution.
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spelling pubmed-102226282023-05-28 Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning Zeng, Shi Pi, Dechang Sensors (Basel) Article Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution. MDPI 2023-05-22 /pmc/articles/PMC10222628/ /pubmed/37430883 http://dx.doi.org/10.3390/s23104969 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zeng, Shi
Pi, Dechang
Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning
title Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning
title_full Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning
title_fullStr Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning
title_full_unstemmed Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning
title_short Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning
title_sort milling surface roughness prediction based on physics-informed machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10222628/
https://www.ncbi.nlm.nih.gov/pubmed/37430883
http://dx.doi.org/10.3390/s23104969
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AT pidechang millingsurfaceroughnesspredictionbasedonphysicsinformedmachinelearning