An Analysis of Convolutional Neural Network Models for Classifying Machine Tools

Leonid Koval; Daniel Pfaller; Mühenad Bilal; Markus Bregulla; Rafał Cupek

doi:10.1007/978-3-030-88113-9_37

An Analysis of Convolutional Neural Network Models for Classifying Machine Tools

Leonid Koval
, Daniel Pfaller
, Mühenad Bilal
, Markus Bregulla
, Rafał Cupek

Department of Distributed Systems and Informatic Devices (RAU/RAU8)

Technische Hochschule Ingolstadt

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

This paper analyzes the use of different Neural Network architectures on two different sets of machine tool images. The sets are either composed of images that were taken with a low-quality camera or catalog photos. The task was to classify of the different types of cutting tools, which is the first step in initiating automatic support for computer-based sharpening. The performance of different Neural Network models was evaluated using a confusion matrix and the Fl-Score. For better understanding, the ROC and PR curves were used. A final check using trained Convolutional Neural Networks was done reciprocally on each of the respective test-set. The main contribution is dedicated for the research in the Industry 4.0. Especially the application of machine learning methods. The main goal of this paper is to present an analysis of the different Deep Neural Network Models that are used to classify machine tools. Furthermore, the factor domain relevance is also briefly discussed.

Original language	English
Title of host publication	Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings
Editors	Krystian Wojtkiewicz, Jan Treur, Elias Pimenidis, Marcin Maleszka
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	461-473
Number of pages	13
ISBN (Print)	9783030881122
DOIs	https://doi.org/10.1007/978-3-030-88113-9_37
Publication status	Published - 2021
Event	13th International Conference on Computational Collective Intelligence, ICCCI 2021 - Virtual, Online Duration: 29 Sept 2021 → 1 Oct 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1463
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	13th International Conference on Computational Collective Intelligence, ICCCI 2021
City	Virtual, Online
Period	29/09/21 → 1/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Confusion-matrix
Domain relevance
F1-score
Image classification
Machine tools

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-030-88113-9_37

Cite this

Koval, L., Pfaller, D., Bilal, M., Bregulla, M., & Cupek, R. (2021). An Analysis of Convolutional Neural Network Models for Classifying Machine Tools. In K. Wojtkiewicz, J. Treur, E. Pimenidis, & M. Maleszka (Eds.), Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings (pp. 461-473). (Communications in Computer and Information Science; Vol. 1463). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-88113-9_37

Koval, Leonid ; Pfaller, Daniel ; Bilal, Mühenad et al. / An Analysis of Convolutional Neural Network Models for Classifying Machine Tools. Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings. editor / Krystian Wojtkiewicz ; Jan Treur ; Elias Pimenidis ; Marcin Maleszka. Springer Science and Business Media Deutschland GmbH, 2021. pp. 461-473 (Communications in Computer and Information Science).

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title = "An Analysis of Convolutional Neural Network Models for Classifying Machine Tools",

abstract = "This paper analyzes the use of different Neural Network architectures on two different sets of machine tool images. The sets are either composed of images that were taken with a low-quality camera or catalog photos. The task was to classify of the different types of cutting tools, which is the first step in initiating automatic support for computer-based sharpening. The performance of different Neural Network models was evaluated using a confusion matrix and the Fl-Score. For better understanding, the ROC and PR curves were used. A final check using trained Convolutional Neural Networks was done reciprocally on each of the respective test-set. The main contribution is dedicated for the research in the Industry 4.0. Especially the application of machine learning methods. The main goal of this paper is to present an analysis of the different Deep Neural Network Models that are used to classify machine tools. Furthermore, the factor domain relevance is also briefly discussed.",

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author = "Leonid Koval and Daniel Pfaller and M{\"u}henad Bilal and Markus Bregulla and Rafa{\l} Cupek",

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Koval, L, Pfaller, D, Bilal, M, Bregulla, M & Cupek, R 2021, An Analysis of Convolutional Neural Network Models for Classifying Machine Tools. in K Wojtkiewicz, J Treur, E Pimenidis & M Maleszka (eds), Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings. Communications in Computer and Information Science, vol. 1463, Springer Science and Business Media Deutschland GmbH, pp. 461-473, 13th International Conference on Computational Collective Intelligence, ICCCI 2021, Virtual, Online, 29/09/21. https://doi.org/10.1007/978-3-030-88113-9_37

An Analysis of Convolutional Neural Network Models for Classifying Machine Tools. / Koval, Leonid; Pfaller, Daniel; Bilal, Mühenad et al.
Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings. ed. / Krystian Wojtkiewicz; Jan Treur; Elias Pimenidis; Marcin Maleszka. Springer Science and Business Media Deutschland GmbH, 2021. p. 461-473 (Communications in Computer and Information Science; Vol. 1463).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Analysis of Convolutional Neural Network Models for Classifying Machine Tools

AU - Koval, Leonid

AU - Pfaller, Daniel

AU - Bilal, Mühenad

AU - Bregulla, Markus

AU - Cupek, Rafał

PY - 2021

Y1 - 2021

N2 - This paper analyzes the use of different Neural Network architectures on two different sets of machine tool images. The sets are either composed of images that were taken with a low-quality camera or catalog photos. The task was to classify of the different types of cutting tools, which is the first step in initiating automatic support for computer-based sharpening. The performance of different Neural Network models was evaluated using a confusion matrix and the Fl-Score. For better understanding, the ROC and PR curves were used. A final check using trained Convolutional Neural Networks was done reciprocally on each of the respective test-set. The main contribution is dedicated for the research in the Industry 4.0. Especially the application of machine learning methods. The main goal of this paper is to present an analysis of the different Deep Neural Network Models that are used to classify machine tools. Furthermore, the factor domain relevance is also briefly discussed.

AB - This paper analyzes the use of different Neural Network architectures on two different sets of machine tool images. The sets are either composed of images that were taken with a low-quality camera or catalog photos. The task was to classify of the different types of cutting tools, which is the first step in initiating automatic support for computer-based sharpening. The performance of different Neural Network models was evaluated using a confusion matrix and the Fl-Score. For better understanding, the ROC and PR curves were used. A final check using trained Convolutional Neural Networks was done reciprocally on each of the respective test-set. The main contribution is dedicated for the research in the Industry 4.0. Especially the application of machine learning methods. The main goal of this paper is to present an analysis of the different Deep Neural Network Models that are used to classify machine tools. Furthermore, the factor domain relevance is also briefly discussed.

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KW - Domain relevance

KW - F1-score

KW - Image classification

KW - Machine tools

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M3 - Conference contribution

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BT - Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings

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A2 - Pimenidis, Elias

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PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 29 September 2021 through 1 October 2021

ER -

Koval L, Pfaller D, Bilal M, Bregulla M, Cupek R. An Analysis of Convolutional Neural Network Models for Classifying Machine Tools. In Wojtkiewicz K, Treur J, Pimenidis E, Maleszka M, editors, Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 461-473. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-88113-9_37

An Analysis of Convolutional Neural Network Models for Classifying Machine Tools

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this