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An extension of PARAFAC to analyze multi-group three-way data
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark.ORCID iD: 0000-0001-5263-1937
Department of Biosystems, MeBioS division, KU Leuven, Leuven, Belgium.
Department of Biosystems, MeBioS division, KU Leuven, Leuven, Belgium.
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark.ORCID iD: 0000-0003-4222-9631
2024 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 246, article id 105089Article in journal (Refereed) Published
Abstract [en]

This paper introduces a novel methodology for analyzing three-way array data with a multi-group structure. Three-way arrays are commonly observed in various domains, including image analysis, chemometrics, and real-world applications. In this paper, we use a practical case study of process modeling in additive manufacturing, where batches are structured according to multiple groups. Vast volumes of data for multiple variables and process stages are recorded by sensors installed on the production line for each batch. For these three-way arrays, the link between the final product and the observations creates a grouping structure in the observations. This grouping may hamper gaining insight into the process if only some of the groups dominate the controlled variability of the products. In this study, we develop an extension of the PARAFAC model that takes into account the grouping structure of three-way data sets. With this extension, it is possible to estimate a model that is representative of all the groups simultaneously by finding their common structure. The proposed model has been applied to three simulation data sets and a real manufacturing case study. The capability to find the common structure of the groups is compared to PARAFAC and the insights into the importance of variables delivered by the models are discussed.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 246, article id 105089
Keywords [en]
Additive manufacturing, Factor analysis, Multi-group data set, PARAFAC
National Category
Probability Theory and Statistics
Research subject
Quality Technology and Logistics
Identifiers
URN: urn:nbn:se:ltu:diva-104469DOI: 10.1016/j.chemolab.2024.105089Scopus ID: 2-s2.0-85185833599OAI: oai:DiVA.org:ltu-104469DiVA, id: diva2:1842831
Note

Validerad;2024;Nivå 2;2024-03-06 (hanlid);

Full text license: CC BY

Available from: 2024-03-06 Created: 2024-03-06 Last updated: 2024-03-06Bibliographically approved

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Kulahci, Murat

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