Machine learning models to predict the relationship between printing parameters and tensile strength of 3D Poly (lactic acid) scaffolds for tissue engineering applications.

3D printing is an effective method to prepare 3D scaffolds for tissue engineering applications. However, optimization of printing conditions to obtain suitable mechanical properties for various tissue engineering applications is costly and time consuming. To address this problem, in this study, scikit-learn Python machine learning library was used to apply four machine learning-based approaches which are ordinary least squares (OLS) linear regression, random forest (RF), light gradient Boost (LGBM), extreme gradient boosting (XGB) and artificial neural network models to understand the relationship between 3D printing parameters and tensile strength of poly(lactic acid) (PLA).