A machine learning perspective on the inverse problem indentation: uniqueness, surrogate modeling, and learning elasto-plastic properties from pile-up
The inverse analysis of indentation curves,aimed at extracting the stress-strain curve of a ma-terial,has been under intense development for decades,with progress relying mainly on the use of analytical expressions derived from small data sets.Here,we take a fresh,data-driven perspective to this classic problem,leveraging machine learning techniques to advance indentation technology.Using a neural network (NN),we efficiently assess uniqueness and identify materials that have indistinguishable indentation responses without the need for complex. domain knowledge-based algorithms.We then demonstrate that inclusion of the residual imprint information resolves the non-uniqueness problem.We show that the elasto-plastic properties of a material can be learned directly from indentation pile-up.Notably,an accurate stress-strain curve can be derived using solely the applied indentation load and pile-up information,thereby eliminating the need for depth-sensing.We also present a systematic analysis of the machine learning model,covering important aspects such as prediction performance. sensitivity,feature selection,and permutation importance,providing insight for model development and evaluation.This work introduces and provides the groundwork of a machine-learning-based profilometry- informed indentation inversion(Pl)technique.It showcases the potential of machine learning as a transformative alter-native when analytical solutions are difficult or impossible to obtain