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A data preparation and migration framework for implementing modular product structures in PLM

A data preparation and migration framework for implementing modular product structures in PLM

Prasad Giddaluru, Muni and Gao, James ORCID: 0000-0001-5625-3654 (2019) A data preparation and migration framework for implementing modular product structures in PLM. In: International Conference on Product Lifecycle Management (PLM2019). Springer. (In Press)

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Abstract

This paper reports the research on the complex process of implementing modular product structures in a Product Lifecycle Management (PLM) system. There are many challenges in implementing the system. One main challenge is organising or mapping existing product data and migrating it to the new PLM system. Companies often use a PLM tool for management of CAD files, documents and drawings, but they do not take advantage of the full potential of the PLM system to support the development activities of modular products. Product data management tools are used mainly for product CAD data management and PLM systems support by automating and managing some of the operational complexity of modular product design. The aim of this research is to propose a data model that can be used for implementing modular product structures in a PLM system and a tool that can formalise the existing data so as to migrate it into the PLM system.

Item Type: Conference Proceedings
Title of Proceedings: International Conference on Product Lifecycle Management (PLM2019)
Uncontrolled Keywords: Product life cycle management, data migration, modular product structures, product data management
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Design, Manufacturing and Innovative Products Research Theme
Related URLs:
Last Modified: 03 Oct 2019 10:47
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/24647

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