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Feature-based Supervision of Shear Cutting Processes on the Basis of Force Measurements: Evaluation of Feature Engineering and Feature Extraction

Hoppe, Florian ; Hohmann, Johannes ; Knoll, Maximilian ; Kubik, Christian ; Groche, Peter (2019)
Feature-based Supervision of Shear Cutting Processes on the Basis of Force Measurements: Evaluation of Feature Engineering and Feature Extraction.
In: Procedia Manufacturing, 34
doi: 10.1016/j.promfg.2019.06.164
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

Facing the increasing amount of available data, supervision of processes is experiencing a vast upheaval. Especially time series recorded during high-speed manufacturing processes like shear-cutting challenge the interpretation of the data. This work shows how to extract features from shear cutting force data that help to explain process variations. The ability to predict the product quality based on these features, however, plays a decisive role. Here the classic approach of feature engineering, in which features are selected using domain-specific knowledge of the engineer, is compared to statistical feature extraction which only bases on the actual process data. The use of these features aims at identifying the process state and product properties using predictive models. Both feature extraction methods are applied on force data and evaluated empirically in three different shear cutting processes. It turns out that both methods perform similar but differ in the presence of measurement uncertainty. Although simple prediction models have been used in this study, the features provide an excellent basis for predicting process or product properties.

Typ des Eintrags: Artikel
Erschienen: 2019
Autor(en): Hoppe, Florian ; Hohmann, Johannes ; Knoll, Maximilian ; Kubik, Christian ; Groche, Peter
Art des Eintrags: Bibliographie
Titel: Feature-based Supervision of Shear Cutting Processes on the Basis of Force Measurements: Evaluation of Feature Engineering and Feature Extraction
Sprache: Englisch
Publikationsjahr: 2019
Ort: Penn State Behrend Erie, Pennsylvania
Verlag: Elsevier B.V
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Procedia Manufacturing
Jahrgang/Volume einer Zeitschrift: 34
Veranstaltungstitel: Procedia Manufacturing
DOI: 10.1016/j.promfg.2019.06.164
URL / URN: https://www.sciencedirect.com/science/article/pii/S235197891...
Kurzbeschreibung (Abstract):

Facing the increasing amount of available data, supervision of processes is experiencing a vast upheaval. Especially time series recorded during high-speed manufacturing processes like shear-cutting challenge the interpretation of the data. This work shows how to extract features from shear cutting force data that help to explain process variations. The ability to predict the product quality based on these features, however, plays a decisive role. Here the classic approach of feature engineering, in which features are selected using domain-specific knowledge of the engineer, is compared to statistical feature extraction which only bases on the actual process data. The use of these features aims at identifying the process state and product properties using predictive models. Both feature extraction methods are applied on force data and evaluated empirically in three different shear cutting processes. It turns out that both methods perform similar but differ in the presence of measurement uncertainty. Although simple prediction models have been used in this study, the features provide an excellent basis for predicting process or product properties.

Zusätzliche Informationen:

Part of special issue: 47th SME North American Manufacturing Research Conference, NAMRC 47, Pennsylvania, USA.

Fachbereich(e)/-gebiet(e): 16 Fachbereich Maschinenbau
16 Fachbereich Maschinenbau > Institut für Produktionstechnik und Umformmaschinen (PtU)
DFG-Sonderforschungsbereiche (inkl. Transregio)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 805: Beherrschung von Unsicherheit in lasttragenden Systemen des Maschinenbaus
Hinterlegungsdatum: 26 Jul 2019 07:35
Letzte Änderung: 26 Nov 2020 10:24
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