- Title
- Smart virtual product development system
- Creator
- Ahmed, Muhammad Bilal
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2021
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- The aim of this research is to address issues related to the effective use of information, knowledge and experience in industry during the process of product development. In this thesis, we propose a novel approach to the support of design, manufacturing, and inspection planning at the early stages of product development. The system we have developed is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) techniques, and will henceforth be referred to as the Smart Virtual Product Development (SVPD) system. This system comprises three primary modules, each of which has been developed to cater to a need for digital knowledge capture for smart manufacturing in the areas of product design, production planning, and inspection planning. The individual modules related to each of these areas in turn will henceforth be referred to as the design knowledge management (DKM) module, the manufacturing capability analysis and process planning (MCAPP) module, and the product inspection planning (PIP) module respectively. Together these modules are fully capable of supporting the five phases of advanced product quality planning (APQP). The SVPD system is a system that can store experiential knowledge relating to previous projects, and makes that knowledge available to a user who presents a relevant query in the future. Formal decisional events or experiences can be comprehensively represented in SOEKS using a unique combination of Variables, Functions, Constraints and Rules. A query based on objectives relevant to one of the modules mentioned above and comprised of variables and functions particular to those objectives is fed into the system, which then provides a list of potential solutions based on the experiential knowledge stored in the system. The user selects the most appropriate solution from among those provided, and that is stored in the system as an answer to similar queries. In the event that the system cannot provide a solution, an expert will then be consulted, and that expert’s decision will be manually inputted into the system and stored. The system, therefore, either updates itself or is updated manually each time a new decision is made. Our experimental results show that the SVPD system is an expert decisional support system and can play a vital role in the establishment of Industry 4.0. The system will benefit manufacturing organizations through the facilitation of product design, manufacturing, and inspection planning.
- Subject
- product development; set of experience knowledge structure; decsional DNA; experience management; knowledge management; design knowledge management; manufacturing capability analysis and process planning; product inspection planning; cyber-physical systems; advanced product quality planning
- Identifier
- http://hdl.handle.net/1959.13/1420676
- Identifier
- uon:37628
- Rights
- Copyright 2021 Muhammad Bilal Ahmed
- Language
- eng
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Thesis | 2 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 424 KB | Adobe Acrobat PDF | View Details Download |