Statistical Analysis and Automation Through Machine Learning of Resonant Ultrasound Spectroscopy Data from Tests Performed on Complex Additively Manufactured Parts
IF 2.6 3区 材料科学Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
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引用次数: 0
Abstract
Additive manufacturing brings inspection issues for quality assurance of final parts because non-destructive testing methods are faced with shape complexity, size, and high surface roughness. Thus, to drive additive manufacturing forward, advanced non-destructive testing methods are required. Methods based on resonant ultrasound spectroscopy (RUS) can take on all the challenges that come with additive manufacturing. Indeed, these full body inspection methods are adapted to shape complexity, to nearly any size, and to high degrees of surface roughness. Furthermore, they are easy to implement, fast and low cost. In this paper, we present the benefit of a resonant ultrasound spectroscopy method, combined with a statistical analysis through Z score implementation, to classify supposedly identical parts, from a batch comprised of several individual builds. We also demonstrate that the inspection can be further accelerated and automated, to make the analysis operator independent, whether the analysis of the resonant ultrasound spectroscopy data is performed supervised or unsupervised with machine learning algorithms.
增材制造给最终零件的质量保证带来了检测问题,因为非破坏性检测方法要面对形状复杂、尺寸大和表面粗糙度高的问题。因此,要推动增材制造向前发展,就需要先进的无损检测方法。基于共振超声波谱(RUS)的方法可以应对增材制造带来的所有挑战。事实上,这些全身检测方法可适应形状复杂性、几乎任何尺寸和高度表面粗糙度。此外,它们易于实施、速度快、成本低。在本文中,我们介绍了共振超声波光谱方法的优点,该方法结合了通过 Z 分数实施的统计分析,可对由多个单个构建组成的批次中假定相同的部件进行分类。我们还证明,无论是使用机器学习算法对共振超声波谱数据进行监督式分析还是非监督式分析,都可以进一步加快检测速度并实现自动化,从而使分析不受操作人员的影响。
期刊介绍:
Journal of Nondestructive Evaluation provides a forum for the broad range of scientific and engineering activities involved in developing a quantitative nondestructive evaluation (NDE) capability. This interdisciplinary journal publishes papers on the development of new equipment, analyses, and approaches to nondestructive measurements.