ForceNet: An offline cutting force prediction model based on neuro-physical learning approach |
| |
Affiliation: | 1. National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 21 0016, China;2. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China;1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, China;2. Jiangxi Hongdu Aviation Industry Group Co. LTD, China |
| |
Abstract: | Accurate cutting force prediction serves as an important reference to the optimization of numerically controlled machining process. Traditional cutting force modeling via theoretical cutting mechanism hampers accurate prediction for actual machining process due to its highly suppressed modeling flexibility. On the other hand, machine learning based modeling approaches demand large amount of diversified labeled samples to achieve comparable prediction results, while collecting these samples can be tedious and costly because the cutter workpiece engagement (CWE) keeps changing during actual process. This paper presents a cutting force prediction model, named ForceNet, which incorporates elementary physical priori into structured neural networks to predict cutting force for end-milling process of complex CWE. The main idea is to use grayscale images to represent CWE geometry, providing a universal input to the ForceNet. Unlike traditional deep neural networks served as an unexplainable black box, the core of the ForceNet is constructed by the vector summation of directional primitive cutting force elements, which are approximated using elementary neural networks. Preliminary results indicate that ForceNet outperformed existing methods not only with greater prediction accuracy in unseen cutting situations, but also with less training data needed thanks to its inherent neuro-physical structure. |
| |
Keywords: | NC machining Cutting force Data-driven intelligent manufacturing Neuro-physical modeling |
本文献已被 ScienceDirect 等数据库收录! |
|