Advancements in Additive Manufacturing: Optimizing Mechanical Properties Through AI-Based Design

Modern engineering has been transformed by additive manufacturing (AM), which makes complicated geometries and customized constructions possible. Nonetheless, there are so many interconnected variables that optimising mechanical properties is still difficult. To improve mechanical performance in AM, this work investigates AI-driven design optimization strategies. Tensile strength, material toughness, and weight reduction efficiency are predicted by utilizing machine learning models to analyze critical aspects like layer height, print speed, infill density, and extrusion temperature. Metrics like MSE, MAE, and R2 scores are used to compare the predictive accuracy of various AI models, such as neural networks, random forests, and support vector machines. The findings show that while using less material, AI-based optimization greatly increases structural integrity. The connections between print parameters and performance results are shown graphically. This study demonstrates how AI may simplify AM procedures, opening the door to more intelligent, high-performance manufacturing systems with improved productivity and material use.