Investigation of melt flow index and tensile properties of dual metal reinforced polymer composites for 3D printing using machine learning approach: Biomedical and engineering applications

Abstract

This study investigates the enhancement of mechanical properties of metal/polymer composites produced through fused deposition modeling and the prediction of the ultimate tensile strength (UTS) by machine learning using a Classification and Regression Tree (CART). The composites, comprising 80% acrylonitrile butadiene styrene matrix and 10% each of aluminum (Al) and copper (Cu) fillers, were subjected to a comprehensive exploration of printing parameters, including printing temperature, infill pattern, and infill density using the Taguchi method. The CART unveiled a hierarchical tree structure with four terminal nodes, each representing distinct subgroups of materials characterized by similar UTS properties. The predictors’ importance was assessed, highlighting their role in determining material strength. The model exhibited a high predictive power with an R-squared value of 0.9154 on the training data and 0.8922 on the test data, demonstrating its efficacy in capturing variability. The optimal combination of parameters for maximizing UTS was a zigzag infill pattern, a printing temperature of 245 °C, and an infill density of 10%, which is associated with the highest UTS of 680 N. The model’s reliability was confirmed through a paired t-test and test and confidence interval for two variances, revealing no significant difference between the observed and predicted UTS values. This research contributes to advancing additive manufacturing processes by leveraging CART analysis to optimize printing parameters and predict material strength. The identified optimal conditions and subgroup characteristics pave the way for developing robust and predictable metal/polymer composites, offering valuable insights for material design in the era of advanced manufacturing technologies.