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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 and Hohmann, Johannes and Knoll, Maximilian and Kubik, Christian and 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, Elsevier B.V, pp. 847-856, 34, ISSN 2351-9789,
DOI: 10.1016/j.promfg.2019.06.164,
[Online-Edition: https://www.sciencedirect.com/science/article/pii/S235197891...],
[Article]

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.

Item Type: Article
Erschienen: 2019
Creators: Hoppe, Florian and Hohmann, Johannes and Knoll, Maximilian and Kubik, Christian and Groche, Peter
Title: Feature-based Supervision of Shear Cutting Processes on the Basis of Force Measurements: Evaluation of Feature Engineering and Feature Extraction
Language: English
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.

Journal or Publication Title: Procedia Manufacturing
Volume: 34
Place of Publication: Penn State Behrend Erie, Pennsylvania
Publisher: Elsevier B.V
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institut für Produktionstechnik und Umformmaschinen (PtU)
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 805: Control of Uncertainty in Load-Carrying Structures in Mechanical Engineering
Event Title: Procedia Manufacturing
Date Deposited: 26 Jul 2019 07:35
DOI: 10.1016/j.promfg.2019.06.164
Official URL: https://www.sciencedirect.com/science/article/pii/S235197891...
Additional Information:

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

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