Part decomposition and evaluation based on standard design guidelines for additive manufacturability and assemblability

Abstract

In comparison to traditional manufacturing, Additive Manufacturing (AM) provides unique capability to create complex part geometries. However, AM often suffers from its own limitations, such as the need for support structures, the presence of the stair-step effect and long build time. Recently, Part Decomposition (PD) techniques have been deployed to overcome these limitations. PD refers to the process of decomposing an original model into sub-assemblies. In the literature, PD studies have often proposed specific design considerations to evaluate that an original model is properly decomposed. For a comprehensive perspective, we address common and essential design considerations based on the standard design guidelines for AM in ISO/ASTM 52910. Since the standard design guidelines cover a variety of perspectives, we focus on the design considerations for additive manufacturability and assemblability . Then, evaluation indicators are defined for the design considerations as mathematical forms to quantitatively evaluate decomposed parts. The evaluation indicators are applied to the optimization of PD. As an optimization approach, a genetic algorithm (GA) is employed to recursively evaluate the outcomes of PD. In the GA, PD processes including concave feature-based PD (CPD) and convex feature-based PD (VPD) are considered to elaborately decompose an original model. In case studies, 9 models are compared with their decomposed parts for material extrusion (ME) and vat photopolymerization (VP) processes. On average, the build time decreases by 21% for ME and 70% for VP and the material consumption is reduced by 27% for ME and 22% for VP. Furthermore, when our optimization approach is compared to another similar method, Near-convex Decomposition (NCD) , the material consumption is reduced by an average of 26% and 62% for ME and VP processes, respectively. • Design considerations for part decomposition are organized in terms of additive manufacturability and assemblability. • To quantitatively assess decomposition, evaluation indicators are developed based on the design considerations. • An optimization method for part decomposition is suggested to represent how the evaluation indicators are applied.