Assembly-level design for additive manufacturing: issues and benchmark

The emerging additive manufacturing (AM) technology works in a layer-wise fashion which makes it possible to manipulate material distribution and composition. The resulting effects are reflected on the potential of innovative shape design, consolidated assembly, optimized topology, and functionally graded material. These new characteristics force designers to rethink about how to make a better engineering design. However, existing design theory and methodology cannot take these potentials provided by AM into account. To fill this void, various design for additive manufacturing (DFAM) approaches are reported. Unfortunately, majority of them focused on partlevel redesign without potential of being extended to assemblylevel applications. In order to shed a light into this emerging field, an overview of current assembly-level DFAM is summarized in this paper. After that, existing issues including the absent analysis of AM's impact on conceptual design, the lack of explicit functional analysis method, the shortage of decision-making support for part consolidation, the deficiency of functional reasoning approaches to generate AM-enabled features, and the scarcity of integrating manufacturing and assembly knowledge into design stage are analyzed and discussed. However, it seems that addressing these issues is such a large scope that collaborative efforts are in need from both design and manufacturing communities. Therefore, this paper serves as a call to action for the research community to establish a comprehensive assembly-level/product-level DFAM method to realize product evolution. As an initial benchmark, authors propose a three-stage design methodology on the basis of the Systematic Design approach. In the presented framework, functional analysis, part consolidation, and structural optimization with process knowledge integration are much highlighted. Moreover, a simple redesign case study is exemplified to clarify existing issues and how the benchmark method works. In the end, this paper is wrapped up with future research.