Enriching Laser Powder Bed Fusion Part Data Using Category Theory

Yuchu Qin, Shubhavardhan Ramadurga Narasimharaju, Qunfen Qi, Shan Lou, Wenhan Zeng, Paul Scott, Jane Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

Laser powder bed fusion (LPBF) is a promising metal additive manufacturing technology for producing functional components. However, there are still a lot of obstacles to overcome before this technology is considered mature and trustworthy for wider industrial applications. One of the biggest obstacles is the difficulty in ensuring the repeatability of process and the reproducibility of products. To tackle this challenge, a prerequisite is to represent and communicate the data from the part realisation process in an unambiguous and rigorous manner. In this paper, a semantically enriched LPBF part data model is developed using a category theory-based modelling approach. Firstly, a set of objects and morphisms are created to construct categories for design, process planning, part build, post-processing, and qualification. Twenty functors are then established to communicate these categories. Finally, application of the developed model is illustrated via realisation of an LPBF truncheon.
Original languageEnglish
Article number130
Number of pages18
JournalJournal of Manufacturing and Materials Processing
Volume8
Issue number4
Early online date24 Jun 2024
DOIs
Publication statusE-pub ahead of print - 24 Jun 2024

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