A physics-informed machine learning model for surface roughness prediction in milling operations

• Published in: International journal of advanced manufacturing technology Vol. 123; no. 11-12; pp. 4065 - 4076
• Main Authors: Wu, Pengcheng, Dai, Haicong, Li, Yufeng, He, Yan, Zhong, Rui, He, Jinsen
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2022; Springer Nature B.V
• Subjects: CAE) and Design; Complexity; Computer-Aided Engineering (CAD; Cutting tools; Deep learning; Engineering; Industrial and Production Engineering; Machine learning; Mechanical Engineering; Media Management; Milling (machining); Model accuracy; Modelling; Neural networks; Original Article; Prediction models; Process parameters; Rapid prototyping; Surface roughness; Workpieces; Surface roughness; Milling process; Data dependencies; Physical informed neural network
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-022-10470-2

Surface roughness has played a crucial role in determining the quality and performance in service of the machined workpiece. To enhance the performance of the final product, it is necessary to quantify the final surface roughness accurately. To this end, massive physical models and data-driven methods have been devoted to modeling surface roughness. However, a high-performance physical and data-driven surface roughness prediction model is often subject to the complex modeling process and data insufficient in the milling process. To this end, a physics-informed neural network for surface roughness prediction in milling operations is proposed in this paper. By using the proposed method, the physical knowledge can be incorporated into the deep learning prediction model, which can effectively reduce the complexity and data dependencies in the modeling phase. To verify the applicability and accuracy of the model, cutting tests were conducted using various workpieces, cutting tools, and process parameters. The results demonstrated that the proposed method can effectively reduce the data dependence while depicting high performance, which is more reliable to be applied in the manufacturing industries.