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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Application of fuzzy logic methodology for predicting dynamic measurement errors related to process parameters of coordinate measuring machines
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Application of fuzzy logic methodology for predicting dynamic measurement errors related to process parameters of coordinate measuring machines

机译：模糊逻辑方法在预测与三坐标测量机过程参数有关的动态测量误差中的应用

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摘要

Coordinate measuring machines (CMM) have a vital and enduring role in the manufacturing process because of their easy adaptation to the systems and high measurement accuracy. Owing to the demand for high accuracy and shorter cycle times of measurement tasks, determining the measurement errors has become more important in precision engineering. Additionally, manufactured components are becoming smaller and tolerances becoming tighter, and therefore, demands for accuracy are increasing. For this reason, dynamic error modeling has become a topic of considerable importance for improving measurement accuracy, manufacturing decisions and process parameter selections. A number of factors such as process parameters, measurement environment, measuring object, reference element, measurement equipment and set-up affect the measurement accuracy of CMM. Considering the complicated inter-relationships among a number of system factors, artificial intelligence-based techniques have become essential tools due to their speed, robustness and non-linear characteristics when working with high-dimensional data. In this study, a fuzzy logic-based methodology was implemented as an artificial intelligence approach for determining measurement errors related to the process parameters for coordinate measuring machines. A Mamdani-type fuzzy inference system was developed within the framework of a graphical user interface. Eight-level trapezoidal membership functions were employed for the fuzzy subsets of each model variable. The product and the centre of gravity methods were performed as the inference operator and defuzzification methods, respectively. The proposed prognostic model provided a well-suited method and produced promising results in predicting measurement errors by monitoring the process parameters such as optimum measuring point numbers, probing speed and probe radius.
机译：坐标测量机（CMM）在制造过程中起着至关重要且持久的作用，因为它们易于适应系统并且具有很高的测量精度。由于需要高精度和更短的测量任务周期，因此确定测量误差在精密工程中变得越来越重要。另外，制造的部件变得越来越小，公差也越来越严格，因此，对精度的要求也越来越高。因此，动态误差建模已成为提高测量精度，制造决策和工艺参数选择的重要课题。过程参数，测量环境，测量对象，参考元件，测量设备和设置等许多因素都会影响CMM的测量精度。考虑到许多系统因素之间的复杂相互关系，基于人工智能的技术由于在处理高维数据时具有速度，鲁棒性和非线性特性等特点，因此已成为必不可少的工具。在这项研究中，基于模糊逻辑的方法被用作一种人工智能方法，用于确定与坐标测量机的过程参数有关的测量误差。在图形用户界面的框架内开发了Mamdani型模糊推理系统。每个模型变量的模糊子集采用八级梯形隶属度函数。乘积和重心方法分别用作推理算子和去模糊方法。所提出的预测模型提供了一种非常合适的方法，并通过监视过程参数（例如最佳测量点数，探测速度和探针半径）来预测测量误差，从而产生了可喜的结果。

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