Development of a Critical Edge-Based Adaptive Toolpath Strategy to Improve Geometrical Accuracy of Incrementally Formed Titanium Implants

Incremental sheet forming has become increasingly popular due to its advantages over traditional metal forming processes, including flexibility, low setup cost, and enhanced formability. It holds great potential for manufacturing asymmetrical components, particularly biomedical implants like cranio-facial implants. However, a significant drawback of this technique is a lack of geometrical accuracy in the produced parts. This research aims to enhance the geometric accuracy of a patient-specific frontal cranial implant by introducing a novel tool path strategy called the adaptive tool path strategy (ATP). The ATP dynamically adjusts step sizes by selectively removing slices based on local curvature requirements. The proposed strategy begins with slicing a CAD model at an extremely fine step depth (0.01 mm) to generate the initial dataset. Subsequently, slices are strategically eliminated to conform to desired curvature specifications. The manufacturing process utilizes commercially pure titanium grade 2 alloy, which is incrementally formed into the desired implant shape using both the traditional constant step depth method and the new adaptive tool path strategy. The produced parts are then scanned using high resolution 3D scanners and a detailed comparison is made between the final geometries and the design geometry. The assessment includes evaluating the forming depth, analysing bending due to spring back, and measuring geometric deviations. Furthermore, numerical simulations are conducted to assess the estimation capabilities of finite element analysis in predicting the forming process. The results demonstrate that the ATP tool path strategy significantly reduces spring back, indicated by a nearly 30 % decrease in the angle of bend along the critical edge. Moreover, the root mean square deviation is reduced by 11 %. Importantly, the experimental results aligned with the patterns observed in the numerical simulations, validating the findings obtained through the study.