Affiliation:
1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Abstract
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.
Funder
NUAA
New Generation Artificial Intelligence
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference42 articles.
1. Pimenov, D.Y., Hassui, A., Wojciechowski, S., Mia, M., Magri, A., Suyama, D.I., Bustillo, A., Krolczyk, G., and Gupta, M.K. (2019). Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics. Appl. Sci., 9.
2. Investigation of experimental study of end milling of CFRP composite;Kilickap;Sci. Eng. Compos. Mater.,2015
3. Surface integrity in metal machining–Part I: Fundamentals of surface characteristics and formation mechanisms;Liao;Int. J. Mach. Tools Manuf.,2021
4. Investigation on micro-milling of Ti–6Al–4V alloy by PCD slotting-tools;Han;Int. J. Precis. Eng. Manuf.,2020
5. Investigation on self-fabricated PCD cutter and its application in deep-and-narrow micro-grooves;Han;Int. J. Adv. Manuf. Technol.,2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献