College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel). 2023 May 22;23(10):4969. doi: 10.3390/s23104969.
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.
表面粗糙度是机械产品质量的关键指标，它可以精确地描述产品的疲劳强度、耐磨性、表面硬度等性能。当前基于机器学习的表面粗糙度预测方法可能会收敛到局部最小值，从而导致模型泛化能力差或结果违反现有物理规律。因此，本文结合物理知识和深度学习，提出了一种物理约束的铣削表面粗糙度预测的物理信息深度学习方法（PIDL）。该方法在深度学习的输入阶段和训练阶段引入物理知识。通过构建具有可容忍精度的表面粗糙度机制模型，对有限的实验数据进行数据增强。在训练过程中，构建物理指导损失函数，指导具有物理知识的模型的训练过程。考虑到卷积神经网络（CNN）和门控循环单元（GRU）在时空尺度上具有优异的特征提取能力，采用 CNN-GRU 模型作为铣削表面粗糙度预测的主要模型。同时，引入双向门控循环单元和多头自注意力机制来增强数据相关性。本文在开源数据集 S45C 和 GAMHE 5.0 上进行了表面粗糙度预测实验。与最先进方法的结果相比，所提出的模型在两个数据集上均具有最高的预测精度，与最佳对比方法相比，测试集上的平均绝对百分比误差平均降低了 3.029%。物理模型引导的机器学习预测方法可能是机器学习发展的未来途径。
