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

Katedra Systemów Rozproszonych i Urządzeń Informatyki (RAU/RAU8)

Technische Hochschule Ingolstadt

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

3 Cytowania z bazy Scopus

Abstrakt

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.

Język oryginału	angielski
Tytuł publikacji goszczącej	Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings
Redaktorzy	Krystian Wojtkiewicz, Jan Treur, Elias Pimenidis, Marcin Maleszka
Wydawca	Springer Science and Business Media Deutschland GmbH
Strony	461-473
Liczba stron	13
ISBN (drukowany)	9783030881122
Identyfikatory DOI	https://doi.org/10.1007/978-3-030-88113-9_37
Status publikacji	Opublikowano - 2021
Wydarzenie	13th International Conference on Computational Collective Intelligence, ICCCI 2021 - Virtual, Online Czas trwania: 29 wrz 2021 → 1 paź 2021

Seria publikacji

Nazwa	Communications in Computer and Information Science
Tom	1463
ISSN (drukowany)	1865-0929
ISSN (elektroniczny)	1865-0937

Konferencja

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

Cele SDG ONZ

Ten wynik przyczynia się do realizacji następujących celów zrównoważonego rozwoju

Cel 9 Przemysł, innowacje i infrastruktura

Obszary tematyczne ASJC Scopus

Informatyka ogólna
Matematyka ogólna

Uzyskaj dostęp do dokumentu

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

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

Koval, L., Pfaller, D., Bilal, M., Bregulla, M., & Cupek, R. (2021). An Analysis of Convolutional Neural Network Models for Classifying Machine Tools. W K. Wojtkiewicz, J. Treur, E. Pimenidis, & M. Maleszka (Red.), Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings (str. 461-473). (Communications in Computer and Information Science; Tom 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 i in. / An Analysis of Convolutional Neural Network Models for Classifying Machine Tools. Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings. redaktor / Krystian Wojtkiewicz ; Jan Treur ; Elias Pimenidis ; Marcin Maleszka. Springer Science and Business Media Deutschland GmbH, 2021. str. 461-473 (Communications in Computer and Information Science).

@inproceedings{ee14aedd57044f088d3c114ea42372b2,

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.",

keywords = "Confusion-matrix, Domain relevance, F1-score, Image classification, Machine tools",

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

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 13th International Conference on Computational Collective Intelligence, ICCCI 2021 ; Conference date: 29-09-2021 Through 01-10-2021",

year = "2021",

doi = "10.1007/978-3-030-88113-9\_37",

language = "English",

isbn = "9783030881122",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "461--473",

editor = "Krystian Wojtkiewicz and Jan Treur and Elias Pimenidis and Marcin Maleszka",

booktitle = "Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings",

address = "Germany",

}

Koval, L, Pfaller, D, Bilal, M, Bregulla, M & Cupek, R 2021, An Analysis of Convolutional Neural Network Models for Classifying Machine Tools. w K Wojtkiewicz, J Treur, E Pimenidis & M Maleszka (red), Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings. Communications in Computer and Information Science, tom 1463, Springer Science and Business Media Deutschland GmbH, str. 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 i in.
Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings. red. / Krystian Wojtkiewicz; Jan Treur; Elias Pimenidis; Marcin Maleszka. Springer Science and Business Media Deutschland GmbH, 2021. str. 461-473 (Communications in Computer and Information Science; Tom 1463).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

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.

KW - Confusion-matrix

KW - Domain relevance

KW - F1-score

KW - Image classification

KW - Machine tools

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U2 - 10.1007/978-3-030-88113-9_37

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

M3 - Conference contribution

AN - SCOPUS:85116866999

SN - 9783030881122

T3 - Communications in Computer and Information Science

SP - 461

EP - 473

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

A2 - Wojtkiewicz, Krystian

A2 - Treur, Jan

A2 - Pimenidis, Elias

A2 - Maleszka, Marcin

PB - Springer Science and Business Media Deutschland GmbH

T2 - 13th International Conference on Computational Collective Intelligence, ICCCI 2021

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. W Wojtkiewicz K, Treur J, Pimenidis E, Maleszka M, redaktorzy, Advances in Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. str. 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

Abstrakt

Seria publikacji

Konferencja

Cele SDG ONZ

Obszary tematyczne ASJC Scopus

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