Surface modification of multi-directional forged biodegradable Mg-Zn alloy by ball burnishing process: Modeling and analysis using deep neural network

This research investigates the feasibility of improving surface integrity of the Mg-2%Zn alloy via a novel combined process of multidirectional forging (MDF) with ball burnishing technique. Mg-2%Zn alloy was subjected to multidirectional forging up to 5 passes at 280 °C and same sample is ball burnished with depth of press of 0.3 mm, feed of 300 mm/min and force of 250 N for 1 pass. Microstructure (optical and transmission electron microscope), phases (X ray diffraction), roughness (atomic force microscope) and mechanical properties (microhardness and tensile) were analyzed for cast, MDF processed and MDF + ball burnished samples. The study also defines a new reliability simulation framework to predict the roughness and hardness of the MDF + ball burnished samples using Deep Neural Network (DNN) approach. For the analysis, depth of press, feed and number of passes served as inputs for the DNN model. After configuring and training procedure of the model, the predicted roughness and microhardness of the MDF + ball burnished were compared with the results of experimental data. A large increase of microhardness was observed for MDF-5 + BB-1 (5 pass MDF processed Mg-2%Zn alloy + Ball burnished sample), found to be 98 Hv and surface roughness of 306 nm was achieved. According to the results, the obtained R-squared value is very close to the 99.5 percentage from the model for predicting roughness and hardness which is a promising result. Corrosion behavior of the AS-1, MDF-5 and MDF-5 + BB-1 was evaluated in Hank's balanced salt solution. Corrosion rate of the MDF-5 + BB-1 samples were found to be 0.87 mm/y which is 7 times better than that of AS-1 samples (6.10 mm/y). Grain refinement and residual stresses were higher in the MDF-5 + BB-1 sample, which helped significantly to enhance corrosion resistance.