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https://hdl.handle.net/1959.11/61790
Title: | Smart Virtual Product Development (SVPD) System to Support Product Inspection Planning in Industry 4.0 |
Contributor(s): | Ahmed, Muhammad Bilal (author); Majeed, Farhat (author); Sanin, Cesar (author) ; Szczerbicki, Edward (author) |
Publication Date: | 2020 |
Early Online Version: | 2020-10-02 |
Open Access: | Yes |
DOI: | 10.1016/j.procs.2020.09.310 |
Handle Link: | https://hdl.handle.net/1959.11/61790 |
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.
Publication Type: | Conference Publication |
Source of Publication: | Procedia Computer Science, v.176, p. 2596-2604 |
Publisher: | Elsevier BV |
Place of Publication: | The Netherlands |
ISSN: | 1877-0509 |
Fields of Research (FoR) 2020: | 4602 Artificial intelligence |
Peer Reviewed: | Yes |
HERDC Category Description: | E1 Refereed Scholarly Conference Publication |
Appears in Collections: | Journal Article School of Science and Technology
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