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Journal of Computing and Information Science in Engineering | Volume 18 | Issue 2 | research-article
Research Papers

A Knowledge-Based Method for Innovative Design for Additive Manufacturing Supported by Modular Ontologies

[+] Author and Article Information
Thomas J. Hagedorn

Mem. ASME
Mechanical and Industrial Engineering,
UMass Amherst,
Amherst, MA 01003
e-mail: thagedorn@umass.edu

Sundar Krishnamurty

Fellow ASME
Mechanical and Industrial Engineering,
UMass Amherst,
Amherst, MA 01003
e-mail: skrishna@umass.edu

Ian R. Grosse

Fellow ASME
Mechanical and Industrial Engineering,
UMass Amherst,
Amherst, MA 01003
e-mail: grosse@umass.edu

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received November 15, 2017; final manuscript received February 17, 2018; published online March 19, 2018. Assoc. Editor: Ying Liu.

J. Comput. Inf. Sci. Eng 18(2), 021009 (Mar 19, 2018) (12 pages) Paper No: JCISE-17-1272; doi: 10.1115/1.4039455 History: Received November 15, 2017; Revised February 17, 2018
Article
References
Figures

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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References

1
Gibson, I. , Goenka, G. , and Narasimhan, R. , 2010, “ Design Rules for Additive Manufacture,” International Solid Free Form Fabrication Symposium, Austin, TX, Aug. 9–11, pp. 705–716.
2
Rosen, D. , 2014, “ Design for Additive Manufacturing: Past, Present, and Future Directions,” ASME J. Mech. Des., 136(9), p. 090301. [CrossRef]
3
Kietzmann, J. , Pitt, L. , and Berthon, P. , 2015, “ Disruptions, Decisions, and Destinations: Enter the Age of 3-D Printing and Additive Manufacturing,” Bus. Horiz., 58(2), pp. 209–215. [CrossRef]
4
Weller, C. , Kleer, R. , and Piller, F. T. , 2015, “ Economic Implications of 3D Printing: Market Structure Models in Light of Additive Manufacturing Revisited,” Int. J. Prod. Econ., 164, pp. 43–56. [CrossRef]
5
Rodrigue, H. , and Rivette, M. , 2010, “ An Assembly-Level Design for Additive Manufacturing Methodology,” IDMME-Virtual Concept, Bordeaux, France, Oct.
6
Ruffo, M. , Tuck, C. , and Hague, R. , 2006, “ Cost Estimation for Rapid Manufacturing-Laser Sintering Production for Low to Medium Volumes,” Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf., 220(9), pp. 1417–1427. [CrossRef]
7
Martelli, N. , Serrano, C. , and van den Brink, H. , 2016, “ Advantages and Disadvantages of 3-Dimensional Printing in Surgery: A Systematic Review,” Surgery, 159(6), pp. 1485–1500. [CrossRef] [PubMed]
8
Eddy, D. , Calderara, J. , and Price, M. , 2016, “ Approach Towards a Decision Support System for Additive Manufacturing,” ASME Paper No. DETC2016-60507.
9
Atzeni, E. , and Salmi, A. , 2012, “ Economics of Additive Manufacturing for End-Usable Metal Parts,” Int. J. Adv. Manuf. Technol., 62(9–12), pp. 1147–1155. [CrossRef]
10
Yim, S. , and Rosen, D. , 2012, “ Build Time and Cost Models for Additive Manufacturing Process Selection,” ASME Paper No. DETC2012-70940.
11
Liu, X. , and Rosen, D. W. , 2010, “ Ontology Based Knowledge Modeling and Reuse Approach of Supporting Process Planning in Layer-Based Additive Manufacturing,” International Conference on Manufacturing Automation (ICMA), Hong Kong, China, Dec. 13–15, pp. 261–266.
12
Rosen, D. W. , 2007, “ Design for Additive Manufacturing: A Method to Explore Unexplored Regions of the Design Space,” Eighteenth Annual Solid Freeform Fabrication Symposium (SFF), Austin, TX, Aug. 1–3, pp. 402–415. https://sffsymposium.engr.utexas.edu/Manuscripts/2007/2007-34-Rosen.pdf
13
Lynn-Charney, C. , and Rosen, D. W. , 2000, “ Usage of Accuracy Models in Stereolithography Process Planning,” Rapid Prototyping J., 6(2), pp. 77–87. [CrossRef]
14
Dinar, M. , and Rosen, D. W. , 2017, “ A Design for Additive Manufacturing Ontology,” ASME J. Comput. Inf. Sci. Eng., 17(2), p. 021013. [CrossRef]
15
Ranjan, R. , Samant, R. , and Anand, S. , 2015, “ Design for Manufacturability in Additive Manufacturing Using a Graph Based Approach,” ASME Paper No. MSEC2015-9448.
16
Nelaturi, S. , Kim, W. , and Kurtoglu, T. , 2015, “ Manufacturability Feedback and Model Correction for Additive Manufacturing,” ASME J. Manuf. Sci. Eng., 137(2), p. 021015. [CrossRef]
17
Chu, C. , Graf, G. , and Rosen, D. W. , 2008, “ Design for Additive Manufacturing of Cellular Structures,” Comput.-Aided Des. Appl., 5(5), pp. 686–696. [CrossRef]
18
Bendsøe, M. P. , and Kikuchi, N. , 1988, “ Generating Optimal Topologies in Structural Design Using a Homogenization Method,” Comput. Methods Appl. Mech. Eng., 71(2), pp. 197–224. [CrossRef]
19
Langelaar, M. , 2016, “ Topology Optimization of 3D Self-Supporting Structures for Additive Manufacturing,” Addit. Manuf., 12(Pt. A), pp. 60–70. [CrossRef]
20
Gaynor, A. T. , and Guest, J. K. , 2014, “ Topology Optimization for Additive Manufacturing: Considering Maximum Overhang Constraint,” AIAA Paper No. 2014-2036.
21
Eschenauer, H. A. , and Olhoff, N. , 2001, “ Topology Optimization of Continuum Structures: A Review,” ASME Appl. Mech. Rev., 54(4), pp. 331–390. [CrossRef]
22
Laverne, F. , Segonds, F. , and Anwer, N. , 2015, “ Assembly Based Methods to Support Product Innovation in Design for Additive Manufacturing: An Exploratory Case Study,” ASME J. Mech. Des., 137(12), p. 121701. [CrossRef]
23
Yang, S. , and Zhao, Y. , 2015, “ Additive Manufacturing-Enabled Design Theory and Methodology: A Critical Review,” Int. J. Adv. Manuf. Technol., 80(1–4), pp. 327–342.
24
Boyard, N. , Rivette, M. , and Christmann, O. , 2013, “ A Design Methodology for Parts Using Additive Manufacturing,” Sixth International Conference on Advanced Research in Virtual and Rapid Prototyping, Leiria, Portugal, Oct. 1–5, pp. 399–404. https://www.researchgate.net/publication/282863835_A_design_methodology_for_parts_using_Additive_Manufacturing
25
Bin Maidin, S. , Campbell, I. , and Pei, E. , 2012, “ Development of a Design Feature Database to Support Design for Additive Manufacturing,” Assem. Autom., 32(3), pp. 235–244. [CrossRef]
26
Rias, A. , Segonds, F. , and Bouchard, C. , 2017, “ Towards Additive Manufacturing of Intermediate Objects (AMIO) for Concepts Generation,” Int. J. Interact. Des. Manuf. (IJIDeM), 11(2), pp. 301–315. [CrossRef]
27
D'Antonio, G. , Segonds, F. , and Laverne, F. , 2017, “ A Framework for Manufacturing Execution System Deployment in an Advanced Additive Manufacturing Process,” Int. J. Prod. Lifecycle Manage., 10(1), pp. 1–19. [CrossRef]
28
Kumke, M. , Watschke, H. , and Vietor, T. , 2016, “ A New Methodological Framework for Design for Additive Manufacturing,” Virtual Phys. Prototyping, 11(1), pp. 3–19. [CrossRef]
29
Kumke, M. , Watschke, H. , and Hartogh, P. , 2017, “ Methods and Tools for Identifying and Leveraging Additive Manufacturing Design Potentials,” Int. J. Interact. Des. Manuf. (IJIDeM), 42(22), pp. 1–13.
30
Chandrasegaran, S. K. , Ramani, K. , and Sriram, R. D. , 2013, “ The Evolution, Challenges, and Future of Knowledge Representation in Product Design Systems,” Comput.-Aided Des., 45(2), pp. 204–228. [CrossRef]
31
Gruber, T. R. , 1993, “ A Translation Approach to Portable Ontology Specifications,” Knowl. Acquis., 5(2), pp. 199–220. [CrossRef]
32
Fenves, S. J. , Foufou, S. , Bock, C. , and Sriram, R. D. , 2008, “ CPM2: A Core Model for Product Data,” ASME J. Comput. Inf. Sci. Eng., 8(1), p. 014501.
33
Hirtz, J. M. , Stone, R. B. , and McAdams, D. , 2001, “ Evolving a Functional Basis for Engineering Design,” ASME Paper No. DETC01/DTM-21688. https://www.researchgate.net/profile/Kristin_Wood/publication/268323233_EVOLVING_A_FUNCTIONAL_BASIS_FOR_ENGINEERING_DESIGN/links/5479d6040cf205d1687fa99e/EVOLVING-A-FUNCTIONAL-BASIS-FOR-ENGINEERING-DESIGN.pdf
34
Fernandes, R. , Grosse, I. , and Krishnamurty, S. , 2007, “ Design and Innovative Methodologies in a Semantic Framework,” ASME Paper No. DETC2007-35446.
35
Grosse, I. R. , Milton–Benoit, J. M. , and Wileden, J. C. , 2005, “ Ontologies for Supporting Engineering Analysis Models,” AIE Edam, 19(1), pp. 1–18.
36
Rockwell, J. , Grosse, I. R. , and Krishnamurty, S. , 2009, “ A Decision Support Ontology for Collaborative Decision Making in Engineering Design,” International Symposium on Collaborative Technologies and Systems (CTS'09), Baltimore, MD, May 18–22, pp. 1–9.
37
Eddy, D. , Krishnamurty, S. , and Grosse, I. , 2014, “ An Integrated Approach to Information Modeling for the Sustainable Design of Products,” ASME J. Comput. Inf. Sci. Eng., 14(2), p. 021011. [CrossRef]
38
Ameri, F. , and Summers, J. D. , 2008, “ An Ontology for Representation of Fixture Design Knowledge,” Comput.-Aided Des. Appl., 5(5), pp. 601–611. [CrossRef]
39
Witherell, P. , Krishnamurty, S. , and Grosse, I. R. , 2007, “ Ontologies for Supporting Engineering Design Optimization,” ASME J. Comput. Inf. Sci. Eng., 7(2), pp. 141–150. [CrossRef]
40
Kornyshova, E. , and Deneckère, R. , 2010, “ Decision-Making Ontology for Information System Engineering,” International Conference on Conceptual Modeling, Vancouver, BC, Canada, Nov. 1–4. https://hal-paris1.archives-ouvertes.fr/hal-00662930/document
41
Witherell, P. , Grosse, I. R. , and Krishnamurty, S. , 2013, “ AIERO: An Algorithm for Identifying Engineering Relationships in Ontologies,” Adv. Eng. Inf., 27(4), pp. 555–565. [CrossRef]
42
Premkumar, V. , Krishnamurty, S. , and Wileden, J. C. , 2014, “ A Semantic Knowledge Management System for Laminated Composites,” Adv. Eng. Inf., 28(1), pp. 91–101. [CrossRef]
43
Eddy, D. , Krishnamurty, S. , and Grosse, I. , 2011, “ Support of Product Innovation With a Modular Framework for Knowledge Management: A Case Study,” ASME Paper No. DETC2011-48346.
44
McPherson, J. D. , Grosse, I. R. , and Krishnamurty, S. , 2013, “ Integrating Biological and Engineering Ontologies,” ASME Paper No. DETC2013-13527.
45
Hagedorn, T. J. , Grosse, I. R. , and Krishnamurty, S. , 2015, “ A Concept Ideation Framework for Medical Device Design,” J. Biomed. Inf., 55, pp. 218–230. [CrossRef]
46
Hagedorn, T. J. , Krishnamurty, S. , and Grosse, I. R. , 2016, “ An Information Model to Support User-Centered Design of Medical Devices,” J. Biomed. Inf., 62, pp. 181–194. [CrossRef]
47
Eddy, D. , Krishnamurty, S. , and Grosse, I. , 2015, “ Knowledge Management With an Intelligent Tool for Additive Manufacturing,” ASME Paper No. DETC2015-46615.
48
Lu, Y. , Choi, S. , and Witherell, P. , 2015, “ Towards an Integrated Data Schema Design for Additive Manufacturing: Conceptual Modeling,” ASME Paper No. DETC2015-47802.
49
W3C Owl Working Group, 2009, “ {OWL} 2 Web Ontology Language Document Overview,” W3C Owl Working Group, accessed Mar. 9, 2018, https://www.w3.org/TR/owl2-overview/
50
Arp, R. , Smith, B. , and Spear, A. D. , 2015, Building Ontologies with Basic Formal Ontology, MIT Press, Cambridge, MA. [CrossRef]
51
Smith, B. , 1995, “ Formal Ontology, Common Sense and Cognitive Science,” Int. J. Man-Mach. Stud., 43(5–6), pp. 641–667.
52
Smith, B. , Ceusters, W. , and Klagges, B. , 2005, “ Relations in Biomedical Ontologies,” Genome Biol., 6(5), p. R46. [CrossRef] [PubMed]
53
Smith, B. , and Ceusters, W. , 2015, “ Aboutness: Towards Foundations for the Information Artifact Ontology,” Sixth International Conference on Biomedical Ontology (ICBO), Lisbon, Portugal, July 27–30, pp. 1–5. http://ceur-ws.org/Vol-1515/regular10.pdf
54
Wong, J. Y. , and Pfahnl, A. C. , 2014, “ 3D Printing of Surgical Instruments for Long-Duration Space Missions,” Aviat., Space, Environ. Med., 85(7), pp. 758–763. [CrossRef]
55
Musen, M. A. , Gennari, J. H. , and Eriksson, H. , 1995, “ PROTEGE-II: Computer Support for Development of Intelligent Systems From Libraries of Components,” Medinfo, 8(Pt. 1), pp. 766–770. https://www.researchgate.net/publication/14609873_PROTEGE-II_Computer_support_for_development_of_intelligent_systems_from_libraries_of_components [PubMed]
56
O'connor, M. J. , Halaschek-Wiener, C. , and Musen, M. A. , 2010, “ Mapping Master: A Flexible Approach for Mapping Spreadsheets to OWL,” International Semantic Web Conference, Shanghai, China, Nov. 7–11, pp. 194–208. http://iswc2010.semanticweb.org/pdf/414.pdf
57
Parsia, B. , and Sirin, E. , 2004, “ Pellet: An Owl Dl Reasoner,” Description Logic Workshop (DL 2004), Whistler, BC, Canada, June 6–8, pp. 212–213. http://iswc2004.semanticweb.org/posters/PID-ZWSCSLQK-1090286232.pdf
58
Edmondson, B. J. , Bowen, L. A. , and Grames, C. L. , 2013, “ Oriceps: Origami-Inspired Forceps,” ASME Paper No. SMASIS2013-3299.
59
Zhou, Y. , Huang, W. M. , and Kang, S. F. , 2015, “ From 3D to 4D Printing: Approaches and Typical Applications,” J. Mech. Sci. Technol., 29(10), pp. 4281–4288. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Partial representation of BFO class structure. Arrows represent a subclass relation between boxes.

+Partial representation of BFO class structure. Arrows represent a subclass relation between boxes.
View Large  |  Save Figure  |  Download Slide (.ppt)
Partial representation of BFO class structure. Arrows represent a subclass relation between boxes.
Grahic Jump Location
Fig. 2

High-level schematic of the implementation of the ICAM ontology

+High-level schematic of the implementation of the ICAM ontology
View Large  |  Save Figure  |  Download Slide (.ppt)
High-level schematic of the implementation of the ICAM ontology
Grahic Jump Location
Fig. 3

Dependency relations among modular ontologies

+Dependency relations among modular ontologies
View Large  |  Save Figure  |  Download Slide (.ppt)
Dependency relations among modular ontologies
Grahic Jump Location
Fig. 4

Partial representation of the information model implemented in the proposed BEM

+Partial representation of the information model implemented in the proposed BEM
View Large  |  Save Figure  |  Download Slide (.ppt)
Partial representation of the information model implemented in the proposed BEM
Grahic Jump Location
Fig. 5

Additive manufacturing machinery role in value delivery model in the BEM ontology

+Additive manufacturing machinery role in value delivery model in the BEM ontology
View Large  |  Save Figure  |  Download Slide (.ppt)
Additive manufacturing machinery role in value delivery model in the BEM ontology
Grahic Jump Location
Fig. 6

Partial class hierarchy depicting AM capabilities identified during the literature review

+Partial class hierarchy depicting AM capabilities identified during the literature review
View Large  |  Save Figure  |  Download Slide (.ppt)
Partial class hierarchy depicting AM capabilities identified during the literature review
Grahic Jump Location
Fig. 7

Proposed process for design ideation using ICAM

+Proposed process for design ideation using ICAM
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Proposed process for design ideation using ICAM
Grahic Jump Location
Fig. 8

Representation of folding surgical tool model in ICAM. The tool is inserted in a folded configuration, then unfolds in vivo. Model based on device reported in Ref. [58].

+Representation of folding surgical tool model in ICAM. The tool is inserted in a folded configuration, then unfolds in vivo. Model based on device reported in Ref. [58].
View Large  |  Save Figure  |  Download Slide (.ppt)
Representation of folding surgical tool model in ICAM. The tool is inserted in a folded configuration, then unfolds in vivo. Model based on device reported in Ref. [58].
Grahic Jump Location
Fig. 9

Representation of case in ICAM. The tool undergoes significant deformation during introduction. However, it is made of a shape memory material, which changes shape in vivo to block a vessel. From Ref. [59].

+Representation of case in ICAM. The tool undergoes significant deformation during introduction. However, it is made of a shape memory material, which changes shape in vivo to block a vessel. From Ref. [59].
View Large  |  Save Figure  |  Download Slide (.ppt)
Representation of case in ICAM. The tool undergoes significant deformation during introduction. However, it is made of a shape memory material, which changes shape in vivo to block a vessel. From Ref. [59].
Grahic Jump Location
Fig. 10

Concept generated from queries of ICAM: (a) folding box structure that uses a hinge to fold and (b) base unit of endocutter stapling surface. Black rectangles represent wells containing staples. The individual segments fold over on another as the box itself folds, advancing the center rows.

+Concept generated from queries of ICAM: (a) folding box structure that uses a hinge to fold and (b) base unit of endocutter stapling surface. Black rectangles represent wells containing staples. The individual segments fold over on another as the box itself folds, advancing the center rows.
View Large  |  Save Figure  |  Download Slide (.ppt)
Concept generated from queries of ICAM: (a) folding box structure that uses a hinge to fold and (b) base unit of endocutter stapling surface. Black rectangles represent wells containing staples. The individual segments fold over on another as the box itself folds, advancing the center rows.

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