Knowledge engineering for additive manufacturing-driven customization
Date of Issue2019
School of Mechanical and Aerospace Engineering
Singapore Centre for 3D Printing
The unique tool-less and layer-upon-layer capabilities of additive manufacturing (AM) have largely lessened manufacturing constraints and significantly broadened design freedom. AM offers new opportunities for developing customized products by achieving significant improvements in product performances and lower overall costs. The AM-enabled customization opportunities inevitably challenge traditionally design theories and methodologies (DTMs) that lack design knowledge for AM products. Here, to counteract this limitation, this study presents a novel methodology for constructing knowledge supporting DTMs for AM-enabled customization: customized design for AM (CDfAM). The CDfAM supports two main phases of design for additive manufacturing (DfAM): (1) opportunistic DfAM in a conceptual design phase and (2) restrictive DfAM in an embodiment design phase. In terms of the knowledge construction for the opportunistic DfAM, it is essential to construct knowledge that supports to explore a customized design space enabled by AM’s design freedom from the conceptual design phase. To address the challenge, this study provides a novel method for capturing knowledge of customized AM product behaviors. First, this study proposes an affordance-based DfAM framework by integrating the concept of affordance into three link chain models. Second, an affordance structure is proposed to develop a formal method for representing the knowledge of the customized AM product behaviors utilizing finite state automata. In terms of the knowledge construction for the restrictive DfAM, this study presents a formal method for representing and reasoning customizable modular knowledge of design rules for AM. First, a novel structured modular hierarchy and associated formalisms of AM design rule elements are presented. Second, machine learning is employed to automatically learn design rules from AM data, which leverages the proposed design rule formalisms. A knowledge graph is designed and built to demonstrate the proposed methodology. Case studies with AM products such as a transtibial prosthesis leg and AM data such as ex-situ AM measurement data provide illustrations in this study. The case studies show that the proposed methodology explicitly constructs structured and correlated AM design knowledge of customized AM product behaviors, product geometries, and additive manufacturability constraints. It is also shown that qualified design knowledge in the knowledge graph enhances automation of DfAM decision-making and data-driven knowledge constructions. The proposed CDfAM methodology enables incorporating both customized AM product functionalities and additive manufacturability constraints into product designs. This study provides a foundation for AM knowledge construction and data- and knowledge-driven automation of DfAM decision-making. Validated design rules and representations are expected to contribute to developing international standards on AM design rules.