The solution of instrumented indentation inverse problems by physically-based models still represents a complex challenge yet to be solved in metallurgy and materials science. In recent years, Machine Learning (ML) tools have emerged as a feasible and more efficient alternative to extract complex microstructure-property correlations from instrumented indentation data in advanced materials. On this basis, the main objective of this review article is to summarize the extent to which different ML tools have been recently employed in the analysis of both numerical and experimental data obtained by instrumented indentation testing, either using spherical or sharp indenters, particularly by nanoindentation. Also, the impact of using ML could have in better understanding the microstructure-mechanical properties-performance relationships of a wide range of materials tested at this length scale has been addressed.

The analysis of the recent literature indicates that a combination of advanced nanomechanical/microstructural characterization with finite element simulation and different ML algorithms constitutes a powerful tool to bring ground-breaking innovation in materials science. These research means can be employed not only for extracting mechanical properties of both homogeneous and heterogeneous materials at multiple length scales, but also could assist in understanding how these properties change with the compositional and microstructural in-service modifications. Furthermore, they can be used for design and synthesis of novel multi-phase materials.

通过基于物理的模型解决仪器化压痕反演问题仍然是冶金和材料科学中尚未解决的复杂挑战。近年来，机器学习 (ML) 工具已成为一种可行且更有效的替代方案，可从先进材料的仪器化压痕数据中提取复杂的微观结构-性能相关性。在此基础上，本文的主要目的是总结最近使用不同机器学习工具来分析通过仪器化压痕测试获得的数值和实验数据的程度，无论是使用球形压头还是锋利压头，特别是纳米压痕。还，

对最近文献的分析表明，先进的纳米力学/微观结构表征与有限元模拟和不同的机器学习算法的结合构成了材料科学领域突破性创新的强大工具。这些研究手段不仅可以用于提取多个长度尺度的均质和异质材料的机械性能，而且可以帮助理解这些性能如何随着成分和微观结构的使用中的变化而变化。此外，它们可用于新型多相材料的设计和合成。