Predicting Mechanical Strength in FDM Printed ABS Parts with In-Process Annealing: A Machine Learning Approach

Fused deposition modeling (FDM), a popular additive manufacturing (AM) technology, is widely used for extruding thermoplastic filaments. Acrylonitrile butadiene styrene (ABS) is a widely used polymer for the FDM technique due to its cost-effectiveness, strong mechanical properties, incredible durability, and excellent thermal stability, making it suitable for functional parts. Nonetheless, low mechanical strength along the Z-X axis from small-scale production to large-scale production of ABS parts is yet to be overcome. This study uses a patent-pending modified heater block assembly to apply in-process thermal load and a conventional brass nozzle to print ASTM D638 Type IV tensile specimens. The effects of these two nozzle types, print speed, and part spacing, are studied on the mechanical properties of the 3D printed samples, ultimate tensile strength, to be specific. The findings show that nozzle type greatly impacts the ultimate tensile strength, with in-situ annealing outperforming conventional nozzle, while effects of part spacing and print speed are less. Various machine learning models are utilized for regression to enhance the process and forecast tensile stress (Decision Tree, Random Forest, Gradient Boosting, and Support Vector Regression). The highest prediction accuracy was attained by Random Forest, demonstrating its applicability for simulating mechanical properties linked to the FDM process. The results highlight the challenges and opportunities of incorporating machine learning into optimizing the FDM process. Future endeavors will investigate sophisticated modeling methods to improve predictive precision by increasing the dataset and considering more process parameters as predictors.