Software (GUI/APP) for Developing AI-Based Models Capable of Predicting Load-Displacement Curve and AFM Image during Nanoindentation

Abstract

During nanoindentation tests, the load-displacement curve is used for estimating mechanical properties, while an indent image obtained through atomic force microscopy (AFM) is used for studying deformation of a material. We present a computational platform for developing artificial intelligence-based models for predicting indentation depth (load-displacement curve) and AFM image as a function of test parameters like maximum applied load, loading rate, and holding time. A user can directly use machine generated data in text (.txt) and hierarchical data format (HDF, hdf) format for developing the AI-based models for indentation depth and AFM image, respectively. The software was tested on three different coatings/materials for indentation depth: heat-treated (HT) sample of cold sprayed aluminum-based bulk metallic glass (Al-BMG) coating, carbon nanotube reinforced aluminum composite (Al-5CNT) coating, and spark-plasma-sintered hydroxyapatite (SPS HA) sample. For AFM imaging, a heat-treated (HT) sample of cold sprayed aluminum-based bulk metallic glass (Al-BMG) coating was considered. Correlation or R-values are close to 1 for all the models developed in this work. Predicted load-displacement curve and AFM image are in good agreement with the experimental findings. Our approach will be helpful in virtual simulation of load-displacement curves and AFM indent images for a large number of new test parameters, thus significantly reducing the number of indents needed for characterizing/analyzing a material.