Feature extraction based on scalable hypothesis tests from photodiode data in laser welding processes

Will T, Schwarzkopf K, Hölbling C, Müller L, Schmidt M (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Procedia CIRP Curran

Publisher: Elsevier B.V.

Book Volume: 111

Pages Range: 527-531

Conference Proceedings Title: Procedia CIRP

Event location: Erlangen DE

DOI: 10.1016/j.procir.2022.08.084

Abstract

Inline monitoring technologies gain increasing importance in industrial laser welding applications. Multiple sensing technologies can be implemented for classification of welding defects. Feature extraction from sensing data is a time-consuming process due to various algorithm possibilities for the identification of relevant features. Feature extraction based on scalable hypothesis tests (FRESH) allows for feature extraction with a combination of various time series characterization methods. FRESHs feature selection promises a quick extraction of relevant features from sensing data with an automatically configured hypothesis test. In the example of classification of spatter events, we show that FRESH can be used for the extraction of relevant features with photodiode sensing data in laser welding of copper.

Authors with CRIS profile

Thomas Will Lehrstuhl für Photonische Technologien Karen Schwarzkopf Lehrstuhl für Photonische Technologien Michael Schmidt Lehrstuhl für Photonische Technologien

Involved external institutions

Vitesco Technologies GmbH DE Germany (DE)

How to cite

APA:

Will, T., Schwarzkopf, K., Hölbling, C., Müller, L., & Schmidt, M. (2022). Feature extraction based on scalable hypothesis tests from photodiode data in laser welding processes. In M. Schmidt, F. Vollertsen, B.M. Colosimo (Eds.), Procedia CIRP (pp. 527-531). Erlangen, DE: Elsevier B.V..

MLA:

Will, Thomas, et al. "Feature extraction based on scalable hypothesis tests from photodiode data in laser welding processes." Proceedings of the 12th CIRP Conference on Photonic Technologies, LANE 2022, Erlangen Ed. M. Schmidt, F. Vollertsen, B.M. Colosimo, Elsevier B.V., 2022. 527-531.

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