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.

中文翻译：

---

薄壁表面在机测量与补偿

在机测量技术因减少传递过程而被认为是智能制造中实现闭环反馈控制的关键技术。然而，机床加工系统的复杂性给在机测量的精度带来了一定的不确定性，严重限制了在实际工业领域的应用。为了克服现有不确定性的缺点，提出了一种薄壁表面在机测量的检测框架。首先，建立基于电位信号的低成本无线在机测量系统，并将其集成到制造工艺线中以进行自动采样。然后，将动量守恒的相似性引入采样规划中，提出一种基于动量守恒和多目标粒子群优化器的自适应采样模型。最后，提出了垂直检测方向下的堆叠式深度学习模型，通过校正采样数据来提高检测精度。与现有的采样方法相比，所提出的模型类似于注意力机制，可以自适应增强轮廓特征。通过数据修正的检验性能平均检验误差提高了约16.14%。仿真和实验表明，该方法在效率和鲁棒性方面具有很大优势，可为薄壁曲面自适应刀具路径修正提供理论参考。