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Smart Virtual Product Development (SVPD) System to Support Product Inspection Planning in Industry 4.0
Abstract
This paper presents the idea of supporting product inspection planning process during the early stages of product life cycle for the experts working on product development. Aim of this research is to assist a collaborative product development process by using Smart Virtual Product Development (SVPD) system, which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The proposed system is developed to support three key aspects of industrial product development i.e. design, manufacturing, and product inspection. Therefore, it comprises of three main modules; design knowledge management (DKM), manufacturing capability and process planning (MCAPP), and product inspection planning (PIP). It collects, stores, and uses experiential knowledge from formal decisional events in the form of set of experience (SOE). This research enlightens the working mechanism of the PIP module, and shows how experiential knowledge related to product inspection can be used during the early stages of product development process. This experiential knowledge is extracted and stored from similar products having some common features and functions. First, the basic description and principles of the approach are introduced, then the prototype version of the system is developed and tested for product inspection planning (PIP) module for the case study, which verifies the feasibility of the proposed approach. The presented system successfully supports smart manufacturing and can play a vital role in Industry 4.0.
Citations
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5
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Authors (3)
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Muhammad Ahmed
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Cesar Sanin
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Procedia Computer Science
no. 176,
pages 2596 - 2604,
ISSN: 1877-0509 - Language:
- English
- Publication year:
- 2020
- Bibliographic description:
- Ahmed M., Sanin C., Szczerbicki E.: Smart Virtual Product Development (SVPD) System to Support Product Inspection Planning in Industry 4.0// Procedia Computer Science -Vol. 176, (2020), s.2596-2604
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.procs.2020.09.310
- Verified by:
- Gdańsk University of Technology
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