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Article

Enriching Laser Powder Bed Fusion Part Data Using Category Theory

by
Yuchu Qin
1,
Shubhavardhan Ramadurga Narasimharaju
2,
Qunfen Qi
3,*,
Shan Lou
1,
Wenhan Zeng
1,
Paul J. Scott
1 and
Xiangqian Jiang
1
1
EPSRC Future Advanced Metrology Hub, University of Huddersfield, Huddersfield HD1 3DH, UK
2
Additive Manufacturing Advancing South East Project, South East Technological University, Waterford X91 K0EK, Ireland
3
School of Electrical, Electronic and Mechanical Engineering, University of Bristol, Bristol BS8 1QU, UK
*
Author to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2024, 8(4), 130; https://doi.org/10.3390/jmmp8040130
Submission received: 28 May 2024 / Revised: 18 June 2024 / Accepted: 21 June 2024 / Published: 24 June 2024

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, an application of the developed model is illustrated via the realisation of an LPBF truncheon.
Keywords: additive manufacturing; laser powder bed fusion; part realisation process; data modelling; data semantics; category theory additive manufacturing; laser powder bed fusion; part realisation process; data modelling; data semantics; category theory

Share and Cite

MDPI and ACS Style

Qin, Y.; Narasimharaju, S.R.; Qi, Q.; Lou, S.; Zeng, W.; Scott, P.J.; Jiang, X. Enriching Laser Powder Bed Fusion Part Data Using Category Theory. J. Manuf. Mater. Process. 2024, 8, 130. https://doi.org/10.3390/jmmp8040130

AMA Style

Qin Y, Narasimharaju SR, Qi Q, Lou S, Zeng W, Scott PJ, Jiang X. Enriching Laser Powder Bed Fusion Part Data Using Category Theory. Journal of Manufacturing and Materials Processing. 2024; 8(4):130. https://doi.org/10.3390/jmmp8040130

Chicago/Turabian Style

Qin, Yuchu, Shubhavardhan Ramadurga Narasimharaju, Qunfen Qi, Shan Lou, Wenhan Zeng, Paul J. Scott, and Xiangqian Jiang. 2024. "Enriching Laser Powder Bed Fusion Part Data Using Category Theory" Journal of Manufacturing and Materials Processing 8, no. 4: 130. https://doi.org/10.3390/jmmp8040130

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