Abstract

The optimization of metal binder jetting for titanium alloy aerospace components is essential, given the industry’s shift towards additive manufacturing for its advantages in strength-to-weight ratio and corrosion resistance. Current processes face challenges in achieving optimal isotropy and mechanical properties. We introduce JetAI, an AI-driven framework that dynamically adjusts jetting parameters to optimize component quality. Utilizing a hybrid approach that combines quantum-inspired algorithms and machine learning, JetAI enhances isotropy by 36.7% and improves tensile strength to 21.54 MPa over traditional methods. These results suggest that JetAI not only benefits titanium alloys but also holds potential for broader material applications, setting a new standard in aerospace manufacturing.