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Real World Data Identification and Classification for Support of Virtual Confidence
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-7612-4470
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-9690-890X
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Informationssystem, Information Systems)ORCID iD: 0000-0002-9421-8566
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2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Shortening of the product development process time is one of the main approaches for all enterprises to offer their products to the market. Virtual manufacturing tools can help companies to reduce their time to market, by reduction of the engineering lead time. Extensive use of virtual engineering models results in a need for verification of the model’s accuracy. This virtual engineering usability and assessment have been named virtual confidence. The two main factors of the achievement of this confidence are the accuracy of the virtual models and the virtual engineering results.

For controlling of both above factors, a complete virtual model and related virtual model knowledge are needed. These knowledges can be tacit or explicit. For exploring explicit knowledge, a data and information collection from different disciplines in the organization is needed.

In this paper, a data map with focus on the manufacturing engineering scope will be presented. This data map is generated from different data sources at a manufacturing plant, and gives an overview of different data that exist at different data sources, in the area of manufacturing. Combining real world data from different sources with virtual engineering model data supports, amongst others, establishment of virtual confidence.

Place, publisher, year, edition, pages
2016.
Keywords [en]
Virtual confidence, manufacturing knowledge, data map, Product lifecycle management (PLM), explicit knowledge
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering; Information Systems
Identifiers
URN: urn:nbn:se:his:diva-13736OAI: oai:DiVA.org:his-13736DiVA, id: diva2:1110760
Conference
7th Swedish Production Symposium, Lund, Sweden, October 25-27, 2016
Projects
KDDS
Funder
Knowledge FoundationAvailable from: 2017-06-16 Created: 2017-06-16 Last updated: 2023-01-04Bibliographically approved
In thesis
1. Managing virtual factory artifacts in extended product lifecycle management systems
Open this publication in new window or tab >>Managing virtual factory artifacts in extended product lifecycle management systems
2021 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Reusing previously designed virtual models and the knowledge extracted from running them can reduce time and costs. Since these models are representations of physical artifacts, they have been built based on some criteria, assumptions, and limitations. Being aware of these criteria, assumptions, and limitations, as well as having information regarding things like the purpose of the virtual model, can help a user evaluate the model for reuse in another study. The knowledge gained by generating and using virtual models also offers a significant advantage when making decisions about whether to reuse them and how to reuse them in new studies and experiments. This historical information and knowledge can be derived from the engineering activities performed when creating and using those virtual models. The research presented in this dissertation deals with the management of virtual factory artifacts, including virtual models, their related data, and historical information and knowledge in product lifecycle management (PLM) platforms. Since PLM systems are developed to manage product- and production-related data, they are suitable for managing virtual models as well, but they are incapable of handling the knowledge generated without appropriate extension. Therefore this research focuses on extending PLM systems so that they can manage historical information related to virtual models, in addition to managing the data and knowledge generated, together with related engineering activities. This management will be based on various requirements, including saving, searching, and retrieving virtual factory artifacts in different projects, studies, and engineering activities. A new information model was developed taking into account four aspects of management, namely: (1) hierarchical structures in PLM systems and manufacturing data; (2) the lifecycle of manufacturing systems; (3) projects, studies, and experiments; and (4) engineering activities, provenance data, and knowledge management. In addition to managing knowledge extracted from virtual models and their utilization, ontology-based knowledge management for virtual models and engineering activities is also provided to build up an ontology in this domain. Ultimately, the developed information model was implemented in several application studies in the industry. These studies cover different types of virtual models from various levels of manufacturing systems. The applicability of the invented information model in these application studies has confirmed its capability to manage and link virtual factory artifacts in a PLM context.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2021. p. 154
Series
Dissertation Series ; 38
Keywords
Virtual models, product lifecycle management, information model, knowledge management, ontology, manufacturing data, provenance, manufacturing systems
National Category
Information Systems
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-19560 (URN)978-91-984919-2-0 (ISBN)
Public defence
2021-04-09, ASSAR, Skövde, 10:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2021-03-30 Created: 2021-03-30 Last updated: 2023-10-30Bibliographically approved

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Morshedzadeh, ImanOscarsson, JanNg, Amos H.C.Jeusfeld, Manfred A.

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