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Research Papers

Applying Semantic Web Technologies to Provide Feasibility Feedback in Early Design Phases

[+] Author and Article Information
Felix Ocker

Institute of Automation and Information Systems,
TUM Department of Mechanical Engineering,
Technical University of Munich,
85748 Garching b. München, Germany
e-mail: felix.ocker@tum.de

Birgit Vogel-Heuser

Professor
Institute of Automation and Information Systems,
TUM Department of Mechanical Engineering,
Technical University of Munich,
85748 Garching b. München, Germany
e-mail: vogel-heuser@tum.de

Christiaan J. J. Paredis

Professor
Fellow ASME
BMW Chair in Systems Engineering,
Department of Automotive Engineering,
Clemson University,
Greenville, SC 29607
e-mail: paredis@clemson.edu

1Corresponding author.

Manuscript received January 24, 2019; final manuscript received May 7, 2019; published online July 18, 2019. Assoc. Editor: Conrad Tucker.

J. Comput. Inf. Sci. Eng 19(4), 041016 (Jul 18, 2019) (12 pages) Paper No: JCISE-19-1022; doi: 10.1115/1.4043795 History: Received January 24, 2019; Accepted May 07, 2019

In the product development process, as it is currently practiced, production is still often neglected in the early design phases, leading to late and costly changes. Using the knowledge of product designers concerning production process design, this paper introduces an ontological framework that enables early feasibility analyses. In this way, the number of iterations between product and process design can almost certainly be reduced, which would accelerate the product development process. Additionally, the approach provides process engineers with possible production sequences that can be used for process planning. To provide feasibility feedback, the approach presented relies on semantic web technologies. An ontology was developed that supports designers to model the relations among products, processes, and resources in a way that allows the use of generic Sparql Protocol And RDF Query Language (SPARQL) queries. Future applicability of this approach is ensured by aligning it with the top-level ontology Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE). We also compare the ontology’s universals to fundamental classes of existing knowledge bases from the manufacturing and the batch processing domains. This comparison demonstrates the approach’s domain-independent applicability. Two proofs of concept are described, one in the manufacturing domain and one in the batch processing domain.

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Figures

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Fig. 1

Overview of the framework

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Fig. 2

Intermediate Engineering Ontology (IEO)

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Fig. 3

Cases of feasibility

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Fig. 4

Cases of feasibility–one-dimensional representation

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Fig. 5

FPD of the production process for yogurt

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Fig. 6

Value combinations for testing subsumption

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Fig. 7

Possible sequences and specified sequence for the yogurt production

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Fig. 8

Workflow proposed for query execution

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Fig. 9

Overview of the query execution times, generated on a machine with an i7-6500U CPU and 8GB of RAM

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