Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning

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.

Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization

“…Ra has a major effect on dimensional accuracy, mechanical parts efficiency and cost of production. Furthermore, Ra also affects the machining process stability diagnosis where a reduction of the surface quality can imply non-homogeneity of the workpiece surface, progressive wear of the tool and cutting tool chatter [2].…”

Time-Series Machine Learning Techniques for Modeling and Identification of Mechatronic Systems with Friction: A Review and Real Application

“…However, this does not rule out the possibility of using other NNs. For instance, a physics-informed ML approach was investigated in a recent study for modeling the surface roughness of a milling process [72].…”

Adaptive Surface Roughness Prediction in Turning Processes with Dynamic Lubrication using Fish Swarm-Intelligent Modified Xgboost Approach

“…Dubey et al (2022) presented to estimate the surface roughness and assess its consistency with expected values, namely"linear regression (LR)," "random forest (RF)" and "support vector machine (SVM)" be employed. Zeng et al (2023) presented the "convolutional neural networks (CNN)", "gated recurrent units (GRU)" method as the main model for predicting the roughness of the surface. Pimenov et al (2018) proposed the application of "artificial intelligence (AI)" approaches for the real-time prediction of surface roughness variations.…”