Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness

Abstract

This research presents a comprehensive analysis of deep neural network models (DNNs) for the precise prediction of Vickers hardness (HV) in nitrided and carburized M50NiL steel samples, with hardness values spanning from 400 to 1000 HV. By conducting rigorous experimentation and obtaining corresponding nanoindentation data, we evaluated the performance of four distinct neural network architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and Transformer. Our findings reveal that MLP and LSTM models excel in predictive accuracy and efficiency, with MLP showing exceptional iteration efficiency and predictive precision. The study validates models for broad application in various steel types and confirms nanoindentation as an effective direct measure for HV hardness in thin films and gradient-variable regions. This work contributes a validated and versatile approach to the hardness assessment of thin-film materials and those with intricate microstructures, enhancing material characterization and potential application in advanced material engineering.

Estimation of Mechanical Properties of Aluminum Alloy Based on Indentation Curve and Projection Area of Contact Zone

“…This opened up new possibilities in the field of indentation techniques. With the continuous advancement of technology, there is a growing demand for understanding the mechanical properties of materials, such as hardness [3][4][5][6][7], Young's modulus [8][9][10][11][12], residual stress [13][14][15][16][17], and plasticity [18][19][20][21][22]. These studies have demonstrated that the mechanical properties of materials can be extracted by comparing the load-displacement curves obtained from finite element simulations with those obtained from experimental measurements when the data are in good agreement.…”