Extraction of mechanical properties of materials through deep learning from instrumented indentation

TLDR

Instrumented indentation is a versatile technique for extracting mechanical properties, especially useful when conventional stress‑strain measurements are impractical, and is applied to 3D‑printed, thin‑film, and multilayered components. This study applies state‑of‑the‑art neural networks, including a multifidelity strategy, to train models that recover elastoplastic properties of metals and alloys from indentation data. The authors develop inverse‑problem algorithms that combine single, dual, and multiple indentation measurements with multifidelity learning—leveraging physical scaling laws and integrated simulation–experiment datasets—to enhance training efficiency and accuracy. These algorithms significantly outperform brute‑force and function‑fitting methods, reduce the need for high‑fidelity data, and demonstrate superior predictive accuracy when benchmarked against experimental indentation of wrought aluminum and 3D‑printed titanium alloys.

Abstract

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress-strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.