Predicting biomechanical properties of additively manufactured polydopamine coated poly lactic acid bone plates using deep learning

Fused Filament Fabrication based bone plates lack mechanical strength, resulting in premature failure. Biocompatible Polydopamine (PDM) coating forms covalent bonds with Poly Lactic Acid (PLA), resulting in enhanced mechanical characteristics. Infill density, submersion time, shaker speed, and coating solution concentration, have a significant effect on the mechanical properties of coated bone plates. Monitoring the mechanical strength experimentally for each set of process parameters is a time-consuming and tedious procedure. Before resorting to experimental tests, it is important to model and predict the mechanical strength in order to increase the mechanical strength of bone plates. Predictive modeling using machine learning approaches has proven to be the best alternative to traditional statistical tools. In this study, various machine learning algorithms, including Random Forest, k-Nearest Neighbors, AdaBoost, and Decision Trees, and Long Short-Term Memory (LSTM) method, were utilized to predict tensile and flexural strengths. Error metrics consisting of Mean Square Error (MSE), Root Mean Square Error (RMSE), Correlation Coefficient (R2), Mean Absolute Percentage Error (MAPE) and RRMSE (Root Relative Mean Squared Error) have been utilized to assess the performance of various models. Owing to the prediction findings, the LSTM model beat all ML-based models by demonstrating the best MSE, RMSE, and R2 values for tensile and flexural strength, which were 5.96 MPa, 2.44 MPa, and 0.9169, respectively, and 11.14 MPa, 3.34 MPa, and 0.9242, respectively. Consequently, the results of this study suggest that LSTM is the best model for predicting the tensile and flexural strength of PDM-coated PLA bone plates without doing experiments.

Automated summary

This study utilized machine learning algorithms to predict the tensile and flexural strengths of PDM-coated PLA bone plates. The LSTM model demonstrated the best performance, accurately predicting the strength values.