A Design for Additive Manufacturing Ontology to Support Manufacturability AnalysisSource: Journal of Computing and Information Science in Engineering:;2019:;volume( 019 ):;issue: 004::page 41014DOI: 10.1115/1.4043531Publisher: American Society of Mechanical Engineers (ASME)
Abstract: Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure the manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals. Furthermore, the wide variety of AM processes, materials, and machines creates challenges in determining manufacturability constraints. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to semantically model DFAM knowledge and retrieve that knowledge. The goal of the proposed DFAM ontology is to provide a structure for information on part design, AM processes, and AM capability to represent design rules. Furthermore, the manufacturing feature concept is introduced to indicate design features that are considerably constrained by given AM processes. After developing the DFAM ontology, queries based on design rules are represented to explicitly retrieve DFAM knowledge and analyze manufacturability using Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules enable effective reasoning to evaluate design features against manufacturing constraints. The usefulness of the DFAM ontology is demonstrated in a case study where design features of a bracket are selected as manufacturing features based on a rule development process. This study contributes to developing a reusable and upgradable knowledge base that can be used to perform manufacturing analysis.
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contributor author | Kim, Samyeon | |
contributor author | Rosen, David W. | |
contributor author | Witherell, Paul | |
contributor author | Ko, Hyunwoong | |
date accessioned | 2019-09-18T09:00:43Z | |
date available | 2019-09-18T09:00:43Z | |
date copyright | 6/13/2019 12:00:00 AM | |
date issued | 2019 | |
identifier issn | 1530-9827 | |
identifier other | jcise_19_4_041014 | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4257856 | |
description abstract | Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure the manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals. Furthermore, the wide variety of AM processes, materials, and machines creates challenges in determining manufacturability constraints. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to semantically model DFAM knowledge and retrieve that knowledge. The goal of the proposed DFAM ontology is to provide a structure for information on part design, AM processes, and AM capability to represent design rules. Furthermore, the manufacturing feature concept is introduced to indicate design features that are considerably constrained by given AM processes. After developing the DFAM ontology, queries based on design rules are represented to explicitly retrieve DFAM knowledge and analyze manufacturability using Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules enable effective reasoning to evaluate design features against manufacturing constraints. The usefulness of the DFAM ontology is demonstrated in a case study where design features of a bracket are selected as manufacturing features based on a rule development process. This study contributes to developing a reusable and upgradable knowledge base that can be used to perform manufacturing analysis. | |
publisher | American Society of Mechanical Engineers (ASME) | |
title | A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis | |
type | Journal Paper | |
journal volume | 19 | |
journal issue | 4 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4043531 | |
journal fristpage | 41014 | |
journal lastpage | 041014-10 | |
tree | Journal of Computing and Information Science in Engineering:;2019:;volume( 019 ):;issue: 004 | |
contenttype | Fulltext |