An adaptive spherical indentation test integrating targeted testing scenarios, data acquisition, and model selection for uniaxial mechanical property predictions

Abstract

To improve the applicability of spherical indentation tests (SITs) in complex targeted testing scenarios, such as piping with contaminated surfaces and high temperatures, this study proposes a targeted data acquisition and the following stress-strain prediction schemes based on the differences in targeted testing scenarios. The data acquisition scheme is classified depending on whether an additional digital image correlation (DIC) is applicable for the plastic zone radius measurements and whether the unloading information is of reliable accuracy. Then, based on the characteristics of the data acquisition scheme, a model selection, including incremental indentation energy model (IIEM), simplified indentation energy model (SIIEM), indentation energy model (IEM), and numerical model (NM), is introduced to achieve the most preferable uniaxial mechanical property predictions. Reliability of the adaptive SITs proposed in this study is verified through experiments (covering 20 °C, 400 °C, 565 °C, and 650 °C) on P91 steels in two service states, one as received and another service exposure for 300,000 h.