Meiners M, Kuhn M, Franke J (2021)
Publication Type: Journal article
Publication year: 2021
Book Volume: 30
Pages Range: 15-18
DOI: 10.1016/j.mfglet.2021.09.006
Modern manufacturing plants generate large volumes of data from production processes to monitor and control them. Besides the volume, the complexity of data rises, and thus, new approaches like machine learning and deep learning move into focus to extract the desired information. In assembly, which is critical for final product quality, various processes use curves for quality monitoring. However, there is currently little research on extracting further information from those process curves. Therefore, this paper proposes a deep learning approach for process curve monitoring. The usability is demonstrated through six joining processes and benchmarked to automated machine learning.
APA:
Meiners, M., Kuhn, M., & Franke, J. (2021). Manufacturing process curve monitoring with deep learning. Manufacturing Letters, 30, 15-18. https://dx.doi.org/10.1016/j.mfglet.2021.09.006
MLA:
Meiners, Moritz, Marlene Kuhn, and Jörg Franke. "Manufacturing process curve monitoring with deep learning." Manufacturing Letters 30 (2021): 15-18.
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