Automated analysis method for high throughput nanoindentation data with quantitative uncertainty

Abstract

High throughput nanoindentation techniques can provide rapid materials screening and property mapping and can span millimeter length scales and up to 106 data points. To facilitate rapid sorting of these data into similar groups, a necessary task for establishing structure–property relationships, use of an unsupervised machine learning analysis called clustering has grown in popularity. Here, a method is proposed and tested that evaluates the uncertainty associated with various clustering algorithms for an example high entropy alloy data set and explores the effect of the number of data points in a second Damascus steel data set. The proposed method utilizes the bootstrapping method of Efron to resample a modeled probability distribution function based upon the original data, which allows the uncertainty related to the clustering to be evaluated in contrast to the classical standard error on the mean calculations. For the Damascus, it was found that results data from a 104 point subsample are comparable to those from the full 106 set while representing a significant reduction in data acquisition.

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

“…Researchers at Ansys [20] and Bruker [21,22] have demonstrated the scope of utilizing data science algorithms for analyzing nanoindentation data, including data generated through atomic force microscopy. Researchers are proposing approaches which will be helpful in decreasing number of experiments required for analyzing a material or a coating [22,23].…”

“…Novelty This is the first work of its kind in which a user can predict AFM image. AFM image has been used by researchers and correlated with experiments [20][21][22]. However, in this work, a user can predict an AFM image as a function of new test conditions/parameters, which is a novelty.…”