基于试件特征分解的五轴机床几何与热误差逆向辨识及通用补偿方法

Five-axis machine tool can process a variety of complex surfaces, and plays an extremely important role in the manufacturing industry. But because of its high degrees of freedom and deeply coupled error items, the error identification and compensation are difficult. Existing methods for direct and indirect error measurements are not only time-consuming, but also need high instrument costs. What’s more, they do not reference to actual cutting workpieces. To meet the requirement of machine tool high processing accuracy for industry applications, this project focuses on methods for optimizing the error identification process and improving the compensation accuracy. Based on workpiece feature decomposition, methods for precisely identifying and compensating geometric and thermal errors, as well as key techniques are studied. The main contents are as follows: workpiece design with multi-features and mapping relationship establishment between features and error items; identification model development of geometric and thermal error elements based on the workpiece feature decomposition; volumetric error compensation model for five-axis machine tools considering both geometric and thermal errors; universal error compensation method for multiple types of five-axis machine tools; algorithm and model experimental validation. Through the above system research, defects of existing error identification methods, such as no real cutting dependency, low identification and compensation accuracy, and poor modeling versatility, are effectively improved. Finally, a feature workpiece design-processing-measuring-compensation integrated system is established and the processing accuracy of the five-axis machine tool is validly improved.

五轴机床能加工各种复杂表面，在装备制造业中发挥着极其重要的作用，但因其自由度多且误差项高度耦合，误差辨识与补偿难度大。现有直接和间接的误差测量方法不仅耗时、仪器成本高，且不以实际切削工件为误差参考源。本项目围绕如何优化五轴机床误差辨识过程并提高其加工精度的问题，研究基于试件特征分解的几何与热误差高效辨识和高精度补偿方法及其关键技术。具体研究内容有：设计可反映几何与热误差的多特征零件，并确立误差项同特征之间的映射关系；提出基于试件特征分解的几何与热误差元素辨识方法；建立五轴机床几何与热误差的高精度补偿模型；给出适用于多种不同结构五轴机床的误差通用补偿方法；算法与模型的实验验证。通过以上系统研究，解决现有误差辨识和补偿方法不以切削工件为本源、误差模型通用性差且补偿精度低等问题，最终构建五轴机床“特征零件设计-加工-测量-补偿一体化”的误差补偿系统，并提高其加工精度。

五轴机床在装备制造业中发挥着极其重要的作用，但因其自由度多且误差项高度耦合，误差辨识与补偿难度大。现有几何误差测量方法效率低且不以实际切削工件为本源、热误差建模方法鲁棒性差、空间误差补偿模型精度低。本项目围绕如何优化五轴机床误差辨识、建模与补偿展开研究，旨在提高其加工精度。.误差辨识方面：设计了包含多特征的零件，确定了特征设计准则并建立了多特征的误差映射关系库。基于不同误差类型对加工误差的作用形式，明确了试件特征分解规则，根据误差映射关系库精确辨识出了平动轴和旋转轴动静态运动误差，大幅提高了辨识效率与精度。此外，针对非正交五轴机床，采用基于45°楔块的高效球杆仪测试以解耦非正交旋转轴位置相关和无关的几何误差。.误差建模方面：利用卷积神经网络，基于热图像分别建立了主轴与旋转轴的热误差鲁棒预测模型，预测精度高达90%。提出了基于数据驱动预测方法的主轴热误差动态线性化建模方法，该模型没有物理信息机制，而是基于历史温度数据预测以预测当前热误差，具有鲁棒性高的特点。此外，针对龙门机床横梁，建立了考虑重力和热效应的横梁挠度的分析预测模型，在热状态下横梁总偏差可建模为三个部分的叠加，即重力引起的误差，热误差和制造引起的误差。.误差补偿方面：传统五轴机床空间误差补偿策略直接生成补偿值而不考虑RTCP（旋转刀具中心点）效应，这会导致平移轴随着旋转轴的补偿而产生额外的运动，从而影响补偿精度。因此，提出了考虑RTCP影响的五轴机床空间误差补偿量重构方法，最终提高了补偿精度。此外，分析了六轴铣齿机基于参数设置修改的误差补偿方法的缺点，对运动轴的关键几何误差及其对齿轮性能的影响进行了建模和分析，通过螺旋理论的正向和逆向运动学建模实现了空间误差预测和补偿。. 通过系统研究，解决了现有误差辨识、建模和补偿方法的问题，最终构建五轴机床“特征零件设计-加工-测量-补偿一体化”的误差补偿系统，并提高了其加工精度。