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A Knowledge-Based Method for Innovative Design for Additive Manufacturing Supported by Modular Ontologies
Author(s) -
Thomas Hagedorn,
Sundar Krishnamurty,
Ian R. Grosse
Publication year - 2018
Publication title -
journal of computing and information science in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.538
H-Index - 50
eISSN - 1944-7078
pISSN - 1530-9827
DOI - 10.1115/1.4039455
Subject(s) - computer science , reuse , context (archaeology) , modular design , ontology , new product development , product design , knowledge management , design knowledge , suite , process (computing) , product (mathematics) , software engineering , systems engineering , data science , engineering , bottleneck , paleontology , history , philosophy , geometry , mathematics , archaeology , epistemology , marketing , waste management , business , biology , embedded system , operating system
Additive manufacturing (AM) offers significant opportunities for product innovation in many fields provided that designers are able to recognize the potential values of AM in a given product development process. However, this may be challenging for design teams without substantial experience with the technology. Design inspiration based on past successful applications of AM may facilitate application of AM even in relatively inexperienced teams. While designs for additive manufacturing (DFAM) methods have experimented with reuse of past knowledge, they may not be sufficient to fully realize AM's innovative potential. In many instances, relevant knowledge may be hard to find, lack context, or simply unavailable. This design information is also typically divorced from the underlying logic of a products' business case. In this paper, we present a knowledge based method for AM design ideation as well as the development of a suite of modular, highly formal ontologies to capture information about innovative uses of AM. This underlying information model, the innovative capabilities of additive manufacturing (ICAM) ontology, aims to facilitate innovative use of AM by connecting a repository of a business and technical knowledge relating to past AM products with a collection of knowledge bases detailing the capabilities of various AM processes and machines. Two case studies are used to explore how this linked knowledge can be queried in the context of a new design problem to identify highly relevant examples of existing products that leveraged AM capabilities to solve similar design problems.

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