Unsupervised Machine Learning in Fractography: Evaluation and Interpretation
Unsupervised Machine Learning in Fractography: Evaluation and Interpretation
复制标题
DOI：
10.31224/osf.io/wjtg2
复制
发表时间：
2020-05
影响因子：
4.7
通讯作者：
Shmuel Osovski;Stylianos Tsopanidis
中科院分区：
文献类型：
--
作者：
Shmuel Osovski;Stylianos Tsopanidis
现代计算机视觉和机器学习技术，当应用于断口学时，显示出自动化大部分故障分析过程的潜力，并消除人为引起的模糊或偏见。考虑到导致故障的内在因素（如微观结构）和外在因素（如环境、加载历史）之间的复杂相互作用，深度学习方法在建立输入数据之间的复杂互连方面表现出非常高的效率，最终可能会揭示新的相关性和信息，这些信息被编码到裂缝表面的复杂几何形状上，并且迄今为止仍然对我们隐藏。在这项工作中，我们研究了一种无监督学习管道的潜在用途，根据其化学含量（即钨百分比）对五种重钨合金的断裂面进行分类。在算法成功的鼓舞下，我们进一步分析了控制算法决策过程的断口表面特征。对这些特征的断口学解释表明，断口表面的塑性程度可以作为分类过程的衡量标准。检查后的管道可用于识别由错误的制造工艺引起的故障，这些故障会导致局部钨浓度变化，最终导致过早失效。
Modern computer vision and machine learning techniques, when applied in Fractogra- phy bare the potential to automate much of the failure analysis process and remove human induced ambiguity or bias. Given the complex interaction between intrinsic (e.g. microstructure) and extrinsic (e.g. environment, loading history) factors leading to failure, deep learning methods, which exhibit very high efficiency in establishing complex interconnections between the input data, may end up revealing new correla- tions and information that is encoded onto the complex geometries of fracture surfaces and remained hidden from us so far. In this work, we examine the potential use of an unsupervised learning pipeline to classify fracture surfaces of five tungsten heavy alloys following their chemical content (i.e. Tungsten percentage). Encouraged by the success of the algorithms, we move on and analyze the features on the fracture surfaces which are governing the decision process of the algorithms. The fractographic interpretation of these features shows that the extent of plasticity on the fracture surface serves as a measure for the classification process. The examined pipeline can be used to identify failures originating from erroneous manufacturing processes, leading to locally varying Tungsten concentrations and ultimately premature failure.