Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia.
Mechanical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak Darul Ridzuan, Malaysia.
PLoS One. 2020 Nov 13;15(11):e0242022. doi: 10.1371/journal.pone.0242022. eCollection 2020.
Damage assessment is a key element in structural health monitoring of various industrial applications to understand well and predict the response of the material. The big uncertainty in carbon fiber composite materials response is because of variability in the initiation and propagation of damage. Developing advanced tools to design with composite materials, methods for characterizing several damage modes during operation are required. While there is a significant amount of work on the analysis of acoustic emission (AE) from different composite materials and many loading cases, this research focuses on applying an unsupervised clustering method for separating AE data into several groups with distinct evolution. In this paper, we develop an adaptive sampling and unsupervised bivariate data clustering techniques to characterize the several damage initiations of a composite structure in different lay-ups. An adaptive sampling technique pre-processes the AE features and eliminates redundant AE data samples. The reduction of unnecessary AE data depends on the requirements of the proposed bivariate data clustering technique. The bivariate data clustering technique groups the AE data (dependent variable) with respect to the mechanical data (independent variable) to assess the damage of the composite structure. Tensile experiments on carbon fiber reinforced composite laminates (CFRP) in different orientations are carried out to collect mechanical and AE data and demonstrate the damage modes. Based on the mechanical stress-strain data, the results show the dominant damage regions in different lay-ups of specimens and the definition of the different states of damage. In addition, the states of the damage are observed using Scanning Electron Microscope (SEM) analysis. Based on the AE data, the results show that the strong linear correlation between AE and mechanical energy, and the classification of various modes of damage in all lay-ups of specimens forming clusters of AE energy with respect to the mechanical energy. Furthermore, the validation of the cluster-based characterization and improvement of the sensitivity of the damage modes classification are observed by the combined knowledge of AE and mechanical energy and time-frequency spectrum analysis.
损伤评估是各种工业应用结构健康监测的关键要素，有助于深入了解和预测材料的响应。碳纤维复合材料响应的巨大不确定性是由于损伤的起始和扩展存在变异性。为了开发用于复合材料设计的先进工具，需要在操作过程中对几种损伤模式进行特征化的方法。虽然已经有大量关于不同复合材料和多种加载情况下声发射（AE）分析的工作，但本研究侧重于应用无监督聚类方法将 AE 数据分为具有明显不同演化的几个组。在本文中，我们开发了一种自适应采样和无监督双变量数据聚类技术，以在不同铺层中对复合材料结构的几种损伤起始进行特征化。自适应采样技术预处理 AE 特征并消除冗余的 AE 数据样本。不必要的 AE 数据的减少取决于所提出的双变量数据聚类技术的要求。双变量数据聚类技术根据机械数据（独立变量）对 AE 数据（因变量）进行分组，以评估复合材料结构的损伤。对不同取向的碳纤维增强复合材料层压板（CFRP）进行拉伸实验，以收集机械和 AE 数据并演示损伤模式。基于机械应力-应变数据，结果显示了不同铺层试件的主要损伤区域和不同损伤状态的定义。此外，使用扫描电子显微镜（SEM）分析观察损伤状态。基于 AE 数据，结果表明 AE 与机械能之间存在很强的线性相关性，并且在所有铺层的试件中，各种损伤模式可以形成 AE 能量与机械能相关的聚类，从而对损伤模式进行分类。此外，通过结合 AE 和机械能的知识以及时频谱分析，观察到基于聚类的特征化的验证和损伤模式分类灵敏度的提高。
