A view similarity-based shape complexity metric to guide part selection for additive manufacturing

Abstract

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.

Evaluation of computationally optimized design variants for additive manufacturing using a fuzzy multi-criterion decision-making approach

“…However, the rank order coe cient of weight reduction is lower with the ranking of the FPWMSM operator. The geometric complexity metric considered in this work has a good correlation with weight reduction [24]. So, the composite metric is an effective measure of both geometric complexity and economic factors to decide the selection of design variant to realize in additive manufacturing.…”

“…The details of calculating the external shape and internal structure complexity is mentioned in the previous work of the authors [24]. To eliminate the limitations in selection of design variants based on opportunistic DfAM, users must consider potential AM manufacturing challenges.…”

“…The above metrics are evaluated for the case study involving selection of a design variant of a triple clamp presented in the earlier work of the authors [24]. The details of the design variants and its ranking based on the metric are shown Fig.…”