Material defects (MD) represent a primary challenge affecting product
performance and giving rise to safety issues in related products. The rapid and
accurate identification and localization of MD constitute crucial research
endeavours in addressing contemporary challenges associated with MD. Although
conventional non-destructive testing methods such as ultrasonic and X-ray
approaches have mitigated issues related to low efficiency in manual
inspections, they struggle to meet the diverse requirements of high precision,
real-time speed, automation, and intelligence. In recent years, propelled by
the swift advancement of machine learning (ML) technologies, particularly
exemplified by deep learning, ML has swiftly emerged as the core technology and
a prominent research direction for material defect detection (MDD). Through a
comprehensive review of the latest literature, we systematically survey the ML
techniques applied in MDD into five categories: unsupervised learning,
supervised learning, semi-supervised learning, reinforcement learning, and
generative learning. We provide a detailed analysis of the main principles and
techniques used, together with the advantages and potential challenges
associated with these techniques. Furthermore, the survey focuses on the
techniques for defect detection in composite materials, which are important
types of materials enjoying increasingly wide application in various industries
such as aerospace, automotive, construction, and renewable energy. Finally, the
survey explores potential future directions in MDD utilizing ML technologies.
This comprehensive survey not only consolidates existing literature on ML-based
MDD technologies but also serves as a foundational reference for future
researchers and industrial practitioners, providing valuable insights and
guidance in developing advanced and efficient MDD systems.

材料缺陷 (MD) 是影响产品性能并引起相关产品安全问题的主要挑战，通过机器学习 (ML) 技术在材料缺陷检测 (MDD) 中，围绕非监督学习、监督学习、半监督学习、强化学习和生成学习五个类别系统地调查了主要原理和技术，并聚焦于复合材料的缺陷检测技术，最后探讨了 ML 技术在 MDD 中的潜在未来发展方向。

基于机器学习的材料缺陷检测的全面调查：挑战、解决方案和未来展望

A Comprehensive Survey on Machine Learning Driven Material Defect  Detection: Challenges, Solutions, and Future Prospects

Composite materials with different microstructural material symmetries are
common in engineering applications where grain structure, alloying and
particle/fiber packing are optimized via controlled manufacturing. In fact
these microstructural tunings can be done throughout a part to achieve
functional gradation and optimization at a structural level. To predict the
performance of particular microstructural configuration and thereby overall
performance, constitutive models of materials with microstructure are needed.
In this work we provide neural network architectures that provide effective
homogenization models of materials with anisotropic components. These models
satisfy equivariance and material symmetry principles inherently through a
combination of equivariant and tensor basis operations. We demonstrate them on
datasets of stochastic volume elements with different textures and phases where
the material undergoes elastic and plastic deformation, and show that the these
network architectures provide significant performance improvements.

本研究提供具有异性组分的材料的有效均质化模型的神经网络体系结构，通过等变和张量基础运算，满足等变性和材料对称性原则，展示了在不同纹理和相位的随机体积元数据集上，这些网络体系结构提供了显著的性能改进。