| 关节臂式测量机动态误差分析与补偿 |
| Analysis and Compensation of Dynamic Errors of Articulated Arm Measuring Machine |
| |
| HTML 查看全文 查看/发表评论 下载PDF阅读器 |
| 中文摘要: |
| 关节臂式测量机误差来源多且易累积放大，其动态误差分析与补偿成为了国内外学者研究的重点。本文通过分析热变形误差、测量力误差与角度编码误差三种主要误差因素，得出了关节臂式测量机工作的最佳温度值以及表征其动态误差的三种误差因子——最大定位误差（MPE）、残余定位误差（RPE）和关节转角值（JA）。针对以上误差因子，本文提出了将T-S模糊神经网络（T-S Fuzzy Neural Network, T-S FNN）在自学能力和大规模运算方面的优势以及模拟退火算法（Simulated Annealing, SA）对全局寻优的能力相结合的新补偿方法，并建立了模型。经正交实验表明本文提出的补偿方法使动态过程中的误差分别减小了88.8%、80.2%、71.3%，证明该模型能够有效提高测量机的动态测量精度。 |
| 英文摘要: |
| The dynamic errors analysis and compensation of articulated arm measuring machine has become the focus of domestic and foreign scholars. In this paper, by analyzing the thermal deformation error, the measurement force error and the angle coding error, the optimal temperature value of the articulated arm measuring machine is obtained, and the three error factors representing its dynamic error, namely maximum positioning error (MPE), residual positioning error (RPE) and joint angle value (Ja), are obtained. In view of the above error factors, this paper proposes a new compensation method which combines the advantages of T-S fuzzy neural network (T-S FNN) in self-learning ability and large-scale operation, and the ability of simulated annealing (SA) to global optimization, and establishes the model. The orthogonal experiment shows that the error of dynamic process is reduced by 88.8%, 80.2% and 71.3% respectively by the proposed compensation method, which proves that the model can effectively improve the dynamic measurement accuracy of the measuring machine. |
|
| 中文关键词: 关节臂式测量机 误差建模 误差补偿 模糊神经网络 模拟退火算法 |
| 英文关键词:articulated arm measuring machine error modeling error compensation fuzzy neural network simulated annealing |
| 基金项目: |
| DOI：10.11823/j.issn.1674-5795.2020.05.06 |
| 引用本文：李少芝，杨凯.关节臂式测量机动态误差分析与补偿[J].计测技术,2020,(5):. |
| 关闭 |
