Optimization of V-Bending for Grade 4A Titanium: A Combined Experimental and Artificial Intelligence Approach

Cold bending of Grade 4A titanium bone plates is a crucial process requiring optimization to prevent crack formation. This study integrates experimental analysis, numerical simulations, and artificial intelligence modeling. Tensile tests were performed to determine the mechanical properties of titanium, validating a finite element model for V-bending simulation. A design of experiments was conducted to assess the influence of bending parameters on key responses: equivalent plastic strain (PEEQ) and springback. Results highlight the predominant role of material thickness and die shoulder distance. To enhance prediction accuracy and process optimization, neural networks using the Bayesian regularization algorithm were applied. This approach contributes to improving the design and manufacturing of orthopedic implants.