Weiderer, Peter and Tome, Ana Maria and Lang, Elmar W. (2020) A NMF-based extraction of physically meaningful components from sensory data of metal casting processes. JOURNAL OF MANUFACTURING SYSTEMS, 54. pp. 62-73. ISSN 0278-6125, 1878-6642
Full text not available from this repository.Abstract
The paper introduces a novel approach for the extraction of physically meaningful thermal component time series during the manufacturing of casting parts. We treat their extraction as Blind Source Separation (BSS) problem by exploiting process-related prior knowledge. Our proposed method arranges temperature time series into a data matrix, which is then decomposed by Non-negative Matrix Factorization (NMF). The latter is guided by a knowledge-based strategy, which initializes the NMF component matrix with time curves designed according to basic physical processes. The temperature time series encompass exclusively non-negative data. Hence NMF lends itself a natural choice as it does not impose mathematical constraints that lack any immediate physical interpretation. We show how to extract components linked to physical phenomena that typically occur during production and cannot be monitored directly. We apply our method to real world data, collected in a foundry during the series production of casting parts for the automobile industry and demonstrate its efficiency.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | NONNEGATIVE MATRIX; FACTORIZATION; SEPARATION; MODEL; Blind source separation; Nonnegative matrix factorization; Temperature time series; Metal casting; Mold casting |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 07 Apr 2021 08:54 |
| Last Modified: | 07 Apr 2021 08:54 |
| URI: | https://pred.uni-regensburg.de/id/eprint/45493 |
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