This work introduces a methodology to assess data quality for the tensile, creep/stress relaxation, and fatigue properties of alloys (as well as metadata associated with manufacture) as a part of a project to develop new materials for extreme environments. The extreme environments in question deal with those found in the power generation sector. Data quality assessment is needed to ensure the reliability of data used in analytics to develop new materials for the power generation sector and to predict the performance of established materials in current use. As data quality metrics have not been standardized for material properties data, quality rating guidelines are developed here for the aspects of data completeness, accuracy, usability, and standardization. The specific design requirements for heat-resistant alloy development were considered in creating each metric. Establishing the quality of a dataset in these areas will enable robust analysis. High-quality data can be set aside to develop predictive models. Lower-quality data need not be discarded but can be used for experimental design. Determining the quality of a materials dataset will also provide additional metadata with the data resource and will promote data reusability. A sample high-quality dataset is presented to indicate the typical data attributes collected from relevant mechanical property testing results, which were considered when generating the data quality metrics. A data template of these attributes was created as a tool for data generators and collectors to promote uniformity and reusability of alloy data. The sparsity of the sample dataset was calculated in order to highlight the areas where data gaps pose a challenge for reliable prediction of creep rupture lifetime.

中文翻译：

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支持抗蠕变合金发展的数据评估方法

这项工作引入了一种方法来评估合金的拉伸，蠕变/应力松弛和疲劳特性（以及与制造相关的元数据）的数据质量，这是为极端环境开发新材料的项目的一部分。所涉及的极端环境涉及发电行业中发现的那些极端环境。需要进行数据质量评估，以确保用于分析的数据的可靠性，以开发用于发电行业的新材料并预测当前使用中已建立材料的性能。由于尚未针对材料属性数据对数据质量指标进行标准化，因此此处针对数据完整性，准确性，可用性和标准化方面制定了质量评级准则。创建每个度量标准时，都考虑了耐热合金开发的特定设计要求。在这些区域中建立数据集的质量将使分析更可靠。可以保留高质量数据来开发预测模型。较低质量的数据不必丢弃，但可以用于实验设计。确定材料数据集的质量还将为数据资源提供其他元数据，并提高数据可重用性。呈现了一个高质量的样本数据集，以指示从相关的机械性能测试结果中收集的典型数据属性，这些属性在生成数据质量指标时已予以考虑。创建了具有这些属性的数据模板，作为数据生成器和收集器的工具，以促进合金数据的一致性和可重用性。