A numerical-based part consolidation candidate detection approach with modularization considerations

Abstract

Aided by the capabilities of additive manufacturing in building a part with multiple materials, dynamic sub-components, and complex geometries, the number of parts that are feasible for consolidation has increased drastically. However, to decide which components to consolidate is difficult. Therefore, to identify these potential candidates out of a complex product is highly demanded. We define this issue as a part consolidation candidate detection (PCCD) problem. To solve this problem, we proposed three principles that rationalize the PCCD process with regard to the maximum number and the priority of parts to be consolidated. Based on which, we developed a modularity-based PCCD (MPCCD) framework which is featured by the need for module division and community detection as well as two PCCD algorithms [i.e., strength-based numerical PCCD (NPCCD) and community-based PCCD (CPCCD)]. Two case studies of a throttle pedal and an octocopter are given to demonstrate the effectiveness of the proposed CPCCD algorithm and the MPCCD framework, respectively. In the end, this paper is wrapped up with important conclusions and future research.