钛(M4AT)数据的多尺度混合模态微观结构评价

J. Wertz, M. Cherry, Laura Homa, Nick Lorenzo, E. Blasch
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引用次数: 0

摘要

材料的性能取决于多尺度物理性质。在许多情况下,最先进的材料表征方法的微到中尺度特征需要大量的准备或破坏性分析。这些缺点限制了它们用于组件级零件的质量控制,因为广泛的准备或破坏性分析过于昂贵或不可能进行实时评估。一个例子是检测和表征钛中的关键微纹理区域,其中最先进的传感方法既具有破坏性又受实验室环境的限制。新的传感方法实现了基于实验室的表征方法的能力,而无需破坏性评估,为制造,检查和组装提供了希望。一个潜在的解决方案是开发新的数据融合算法来补充现有的无损评估技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Scale Mixed Modality Microstructure Assessment for Titanium (M4AT) Data
The capability of a material depends on multiscale physical properties. In many cases, state-of-the-art material characterization methods for micro-to-mesoscale features require extensive preparation or destructive analysis. These shortcomings limit their use for quality control of component-scale parts, as extensive preparation or destructive analysis are prohibitively expensive or impossible for real-time assessment. One example is the detection and characterization of critical microtexture regions in titanium, where the state-of-the-art sensing method is both damaging and constrained to a laboratory environment. New sensing approaches that achieve the capability of laboratory-based characterization methods without destructive assessment offer promise for manufacturing, inspection, and assembly. A potential solution is to develop novel data fusion algorithms to compliment existing nondestructive evaluation techniques.
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