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Error measures for functional product testing

机译：功能产品测试的误差措施

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During product development, testing of models and prototypes offers significant advantages over direct product testing, including easier, cheaper, and faster fabrication. However, two issues prevent effective functional testing with prototypes: prediction accuracy and confidence in scale testing results. The traditional similarity method, which is based on dimensional analysis, is commonly applied to perform scale testing. However, the method may not provide accurate scale testing results, especially when available model materials are different from the final product materials. The authors have developed a new empirical similarity method, wherein specimen pairs and partial knowledge of systems are systematically utilized, to improve the prediction accuracy. In this paper we describe the construction of error measures to utilize scale testing results with confidence. In practice, scale testing results are validated based on experiences with previous testing results. This approach to predicting accuracy is difficult to formalize. We develop and simulate a systematic two-level error estimation procedure. Realistic numerical examples demonstrate the feasibility of the approach.
机译：在产品开发期间，模型和原型测试可通过直接产品测试提供显着的优势，包括更容易，更便宜，制造更易于制造。然而，两个问题可以防止具有原型的有效功能测试：预测准确性和规模测试结果的置信度。通常应用于尺寸分析的传统相似方法以执行比例测试。然而，该方法可能无法提供准确的规模测试结果，尤其是当可用的模型材料与最终产品材料不同时。作者开发了一种新的经验相似性方法，其中系统地利用了样本对和系统的部分知识，以提高预测精度。在本文中，我们描述了利用规模测试结果的误措措施的构建。在实践中，基于先前测试结果的经验验证了规模测试结果。这种预测精度的方法难以正式化。我们开发和模拟系统的两级误差估计过程。现实的数值例证表明了方法的可行性。

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