基于表征参量的标准测试数据集构建技术研究

    Research on the Construction of Standardized Test Datasets Based on Characterization Parameters

    • 摘要: 几何要素拟合作为测量评价软件中评定误差的关键步骤,不同逼近方法、评价策略的选择及有效数字的取舍等都会对评价结果产生影响,导致不同测量分析软件对同一测量数据生成不同的评价结果。面向几何要素拟合评价算法难以认证的问题,分析和讨论了标准输入测试数据集的生成依据和规则,以及由此确定了不同几何要素的表征参量,实现了基于表征参量的标准输入测试数据集的动态构建。基于标准输入测试数据,通过研究最小二乘双重拟合算法及几何公差评价算法,实现了标准输出测试数据集的生成,最后将评价结果与蔡司测量分析软件CALYPSO进行了比对验证。通过动态构建的标准输出测试数据集与测量评价软件的评价结果进行比对,以完成测量评价软件中不同几何要素基于最小二乘拟合评价算法的认证。

       

      Abstract: The fitting of geometric elements is a critical step in evaluating errors in measurement evaluation software. Different approximation methods, evaluation strategies, and the rounding of significant figures can all influence the evaluation results, leading to inconsistent outcomes from different measurement analysis software for the same measurement data. To address the difficulty in certifying the evaluation algorithms for geometric element fitting, this paper analyzes and discusses the basis and rules for generating standardized input test datasets. Characterization parameters for different geometric elements are determined, enabling the dynamic construction of standardized input test datasets based on these parameters. Using the standardized input test datasets, the least squares double fitting algorithm and geometric tolerance evaluation algorithm are studied to generate standardized output test datasets. The evaluation results are then compared and verified with Zeiss' CALYPSO measurement analysis software. By comparing the dynamically constructed standardized output test datasets with the evaluation results from the measurement evaluation software, the certification of the least squares fitting-based evaluation algorithms for different geometric elements in the measurement evaluation software is completed.

       

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