چکیده

The current work has mainly focused on predicting the wear performance of femur bone fabricated by modified epoxy-resin composites with varying percentage of kenaf natural fiber (12%, 18% and 24%) along with Al2O3 as filler material. The composites were fabricated by vacuum bag method. The experiments of wear behaviour were conducted with parameters such as applied loads, sliding velocity and percentage of fiber. Supervised machine learning algorithms such as k-nearest neighbor (KNN), support vector regression (SVR) and Random Forest (RF) were used to predict the wear loss. Results of adopted machine learning algorithms with the performance rate of R2 for training and testing are closely nearer. RF has yielded the superior results in R2, MAE and RMSE among the constructed models. The findings could aid in replacement of femur bone with the fabrication of light weight polymer composites using vacuum bag method.

کليدواژه ها

pin-on-dis wear testing machine, femur bone, k-nearest neighbor, random forest, support vector regression, vacuum bag method

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Hulipalled Poornima , 1400 , Prediction of Wear Behavior of Modified Epoxy-based Composites for Orthopaedic Implants using machine learning algorithms , نخستین همایش بین المللی و سومین همایش ملی ریاضیات زیستی