Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Abstract

Nowadays, anodic coating on additively manufactured (AM) or 3D printed Al–10Si–Mg alloy are used for various components in spacecraft such as antenna feeds, wave guides, structural brackets, collimators, thermal radiators etc. In this study, artificial neural network (ANN) and power law-based models are developed from experimental nanoindentation data for predicting elastic modulus and hardness of anodized AM Al–10Si–Mg at any desired loads. Data from nanoindentation experiments conducted on plan- and cross-sections of anodized coating on AM Al–10Si–Mg alloy was considered for modeling. Apart from nanomechanical properties, load and displacement curves were predicted using Python software from ANN and the Power law model of nanoindentation. It is observed that the ANN model of 50 mN nanoindentation experimental data can accurately predict the loading pattern at any desired load below 50 mN. Elastic modulus and hardness of anodized AM Al–10Si–Mg computed from ANN and the power law model of the unloading curve are also comparable with the values obtained from Weibull distribution analysis reported elsewhere. The derived models were also used to predict nanomechanical properties at 25 and 35 mN, for which no experimental data was available. The computed hardness of plan section of the anodic coating is 3.99 and 4.02 GPa for 25 and 35 mN, respectively. The computed hardness of cross-section of the anodic coating of is 7.16 and 6.61 GPa for 25 and 35 mN, respectively. Thus, the ANN and Power law model of nanoindentation can predict elastic modulus and hardness at different loads by conducting the minimum number of experiments. The novel approach to predict nanomechanical properties using ANN resulted in determining realistic and design specific data on hardness and modulus of the anodized coating on AM Al–10Si–Mg alloy.