Metal wire-fed Additive Manufacturing (AM) typically produces wavy surfaces, requiring additional machining to meet product tolerance and roughness requirements. Minimizing material waste during this post-processing is essential to uphold the reduced environmental impact sought by using AM.
This communication presents an evaluation of the design process for a metallic part fabricated using Plasma Wire Arc Additive Manufacturing (Plasma-WAAM). The study begins with the path planning for the torch to follow in creating the 2D layers, incorporating welding parameters tailored to each layer's geometry, wire feed speed, and gas flow rates. Adaptive welding parameters are applied to each layer to optimize the fabrication process. The final part undergoes a machining feasibility analysis to ensure alignment with the target design, preventing material scarcity and potential holes in certain areas.
To conduct this process, a 3D scanning procedure followed by mesh triangle reduction was employed to minimize the computational time required for all operations. The first task is to manually position the two parts, placing the target part inside the fabricated one. Following this, the normal distances between a selected subset of points on the target part and the fabricated part are calculated. To select this subset of points, a strategy based on the triangular mesh of the target part was used, employing the barycentric coordinates of each triangle and weighting the area of each triangle. Once the distances are obtained, they will be classified into multiple groups corresponding to the main planes of the target part. This will provide a measurement of the positioning accuracy, allowing for the necessary translations and rotations of the fabricated part to be determined. These adjustments will ensure that the fabricated part (which should be always larger than the target part) is correctly centered and fits properly with the target part.
To assess the goodness of fit between the two parts for subsequent machining, this process can be iterated as needed. The results indicated that the proposed method significantly reduces operational tasks while maintaining accuracy in feasibility evaluation, showcasing a novel strategy for streamlined assessment in additive manufacturing.