Machine learning based corrosion prediction of as cast Mg-Sn alloys for biomedical applications

Magnesium and its various alloys are the promising materials for orthopedic implant applications because of its light weight, biocompatible and biodegradable characteristics. The prediction of corrosion rate is important when the implant material is present in the human body for longer time period and it can be done by machine learning approach. Corrosion data of Mg-Sn alloy samples were collected and fit in to the machine learning algorithm. Extreme Gradient Boost model has given the best output for the prediction of corrosion behavior with the accuracy of R2 value of 0.961 in EIS plots and 0.6145 in Tafel plots.