On-machine measurement and compensation of thin-walled surface

Abstract

On-machine measurement technology is considered as a key technology for realizing closed-loop feedback control in intelligent manufacturing due to the reduction of the transfer process. However, the complexity of the machine tool process system introduces some uncertainty into the accuracy of on-machine measurement, which severely limits the application in the actual industrial field. To overcome the shortcomings of the existing uncertainty, an inspection framework for on-machine measurement of thin-walled surface is proposed. Firstly, a low-cost wireless on-machine measurement system based on potential signals is established and integrated into a manufacturing process line for automatic sampling. Then, the similarity of momentum conservation is introduced into sampling planning, and an adaptive sampling model based on momentum conservation and multi-objective particle swarm optimizer is proposed. Finally, a stacked deep learning model under the vertical inspection direction is proposed to improve the inspection accuracy by correcting the sampling data. Compared with existing sampling methods, the proposed model is similar to an attention mechanism that enables adaptive enhancement of profile features. The inspection performance by data correction is improved by about 16.14% in the mean inspection error. Simulations and experiments show that the proposed method has great advantages in terms of efficiency and robustness, which can provide a theoretical reference for adaptive toolpath correction for thin-walled surfaces.