Towards a Numerical Approach of Finding Candidates for Additive Manufacturing-Enabled Part Consolidation

Abstract

Part consolidation (PC) is one of the typical design freedoms enabled by additive manufacturing (AM) processes. However, how to select potential candidates for PC is rarely discussed. This deficiency has hindered AM from wider applications in industry. Currently available design guidelines are based on obsolete heuristic rules provided for conventional manufacturing processes. This paper first revises these rules to take account of AM constraints and lifecycle factors so that efforts can be saved and used at the downstream detailed design stage. To automate the implementation of these revised rules, a numerical approach named PC candidate detection (PCCD) framework is proposed. This framework is comprised of two steps: construct functional and physical interaction (FPI) network and PCCD algorithm. FPI network is to abstractly represent the interaction relations between components as a graph whose nodes and edges have defined physical attributes. These attributes are taken as inputs for the PCCD algorithm to verify conformance to the revised rules. In this PCCD algorithm, verification sequence of rules, conflict handling, and the optimum grouping approach with the minimum part count are studied. Compared to manual ad hoc design practices, the proposed PCCD method shows promise in repeatability, retrievability, and efficiency. Two case studies of a throttle pedal and a tripod are presented to show the application and effectiveness of the proposed methods.