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Methods for the capture of manufacture best practice in product lifecycle management
- Source :
- International Journal of Production Research. 48:5885-5904
- Publication Year :
- 2009
- Publisher :
- Informa UK Limited, 2009.
-
Abstract
- The capture of manufacturing best practice knowledge in product lifecycle management systems has significant potential to improve the quality of design decisions and minimise manufacturing problems during new product development. However, providing a reusable source of manufacturing best practice is difficult due to the complexity of the viewpoint relationships between products and the manufacturing processes and resources used to produce them. This paper discusses how best to organise manufacturing best practice knowledge, the relationships between elements of this knowledge plus their relationship to product information. The paper also explores the application of UML-2 as a system design tool which can model these relationships and hence support the reuse of system design models over time. The paper identifies a set of part family and feature libraries and, most significantly, the relationships between them, as a means of capturing best practice manufacturing knowledge and illustrates how these can be linked to manufacturing resource models and product information. Design for manufacture and machining best practice views are used in the paper to illustrate the concepts developed. An experimental knowledge based system has been developed and results generated using a power transmission shaft example.
- Subjects :
- Product design specification
0209 industrial biotechnology
Engineering
business.industry
Strategy and Management
Integrated Computer-Aided Manufacturing
02 engineering and technology
Management Science and Operations Research
Product engineering
Industrial and Manufacturing Engineering
Manufacturing engineering
020901 industrial engineering & automation
Computer-integrated manufacturing
Process development execution system
New product development
0202 electrical engineering, electronic engineering, information engineering
Systems engineering
Product management
020201 artificial intelligence & image processing
business
Manufacturing execution system
Subjects
Details
- ISSN :
- 1366588X and 00207543
- Volume :
- 48
- Database :
- OpenAIRE
- Journal :
- International Journal of Production Research
- Accession number :
- edsair.doi...........c19d18c76370a37a78b93ed128aba59a
- Full Text :
- https://doi.org/10.1080/00207540903104210