School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK.
Sensors (Basel). 2023 Jan 17;23(3):1074. doi: 10.3390/s23031074.
Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledge-based approaches. These approaches can be used for a wide range of applications, including material design; predicting material properties; identifying hidden relationships; and classifying microstructures, defects, and damage. However, researchers must carefully consider the appropriateness of various machine learning algorithms, based on the available data, material being studied, and desired knowledge outcomes. In addition, the interpretability of certain machine learning models can be a limitation in materials science, as it may be difficult to understand the reasoning behind predictions. This paper aims to make novel contributions to the field of material engineering by analyzing the compatibility of dynamic response data from various material structures with prominent machine learning approaches. The purpose of this is to help researchers choose models that are both effective and understandable, while also enhancing their understanding of the model's predictions. To achieve this, this paper analyzed the requirements and characteristics of commonly used machine learning algorithms for crack propagation in materials. This analysis assisted the authors in selecting machine learning algorithms (K nearest neighbor, Ridge, and Lasso regression) to evaluate the dynamic response of aluminum and ABS materials, using experimental data from previous studies to train the models. The results showed that natural frequency was the most significant predictor for ABS material, while temperature, natural frequency, and amplitude were the most important predictors for aluminum. Crack location along samples had no significant impact on either material. Future work could involve applying the discussed techniques to a wider range of materials under dynamic loading conditions.
机器学习在材料科学中的损伤检测和预测方面具有巨大的潜力。机器学习还具有生成高度可靠和准确表示的能力，与传统的基于知识的方法相比，可以提高损伤的检测和预测能力。这些方法可应用于广泛的领域，包括材料设计、预测材料性能、识别隐藏关系以及对微观结构、缺陷和损伤进行分类。然而，研究人员必须根据可用数据、研究的材料和所需的知识成果，仔细考虑各种机器学习算法的适用性。此外，某些机器学习模型的可解释性可能是材料科学中的一个限制，因为可能难以理解预测背后的推理。本文旨在通过分析来自各种材料结构的动态响应数据与突出的机器学习方法的兼容性，为材料工程领域做出新的贡献。目的是帮助研究人员选择既有效又易于理解的模型，同时增强对模型预测的理解。为此，本文分析了常用机器学习算法在材料裂纹扩展中的要求和特点。该分析帮助作者选择了机器学习算法（K 最近邻、岭回归和 Lasso 回归）来评估铝和 ABS 材料的动态响应，使用以前研究的实验数据来训练模型。结果表明，固有频率是 ABS 材料的最重要预测因子，而温度、固有频率和振幅是铝的最重要预测因子。裂纹位置对两种材料均无显著影响。未来的工作可以涉及在更广泛的动态加载条件下应用所讨论的技术来研究更多的材料。
