Purpose This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The metric helps to improve the selection process by objectively screening a large number of parts and identifying the parts most suited for AM and enabling experts to prioritize parts from a smaller set based on relevant subjective/contextual factors. Design/methodology/approach The methodology involves calculating a part’s shape complexity based on the concept of view similarity, that is, the similarity of different views of the outer shape and internal cross-sectional geometry. The combined shape complexity metric (weighted sum of the external shape and internal structure complexity) has been used to rank various three dimensional (3D) models. The metric has been tested for its sensitivity to various input parameters and thresholds are suggested for effective results. The proposed metric’s applicability for part selection has also been investigated and compared with the existing metric-based part selection. Findings The proposed shape complexity metric can distinguish the parts of different shapes, sizes and parts with minor design variations. The method is also efficient regarding the amount of data and computation required to facilitate the part selection. The proposed method can detect differences in the mass properties of a 3D model without evaluating the modified parameters. The proposed metric is effective in initial screening of a large number of parts in new product development and for redesign using AM. Research limitations/implications The proposed metric is sensitive to input parameters, such as the number of viewpoints, design orientation, image resolution and different lattice structures. To address this issue, this study suggests thresholds for each input parameter for optimum results. Originality/value This paper evaluates shape complexity using view similarity to rank parts for prototyping or redesigning with AM.

The industry needs generic methods for selecting design variants obtained from the computational tools of Design for Additive Manufacturing (DfAM). Therefore, a decision support system based on quantitative metrics for selecting a design variant is needed to overcome the current industry's barriers to using the unique capabilities of the additive manufacturing process. This study attempts to define multiple criteria for evaluating the design variations under opportunistic and constraint-based design for additive manufacturing. The Multi-Criteria Decision-Making (MCDM) aggregates four different metrics representing the geometric complexity, cost-benefit, and additional cost due to support structure. A fuzzy power Maclaurin symmetric mean operator is employed for the aggregation of metrics for evaluating the design variant for manufacturing in Metal Additive Manufacturing (MAM) using Laser Powder Bed Fusion Process (L-PBF). The efficacy of the proposed approach is exemplified by evaluating the topologically optimized design variants of an airplane bearing bracket and an engine bracket. Ranking and selection of the design variants using the proposed approach resulted in a 50% cost reduction in the case of an airplane bracket and a 75% cost reduction in the case of an engine bracket compared with the original design manufactured in AM.