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2021 | OriginalPaper | Buchkapitel

An Analysis of Convolutional Neural Network Models for Classifying Machine Tools

verfasst von : Leonid Koval, Daniel Pfaller, Mühenad Bilal, Markus Bregulla, Rafał Cupek

Erschienen in: Advances in Computational Collective Intelligence

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Integration of PSO Algorithm and Fuzzy Logic to Reduce Energy Consumption in IoT-Based Sensor Networks

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Titel: An Analysis of Convolutional Neural Network Models for Classifying Machine Tools
verfasst von: Leonid Koval
Daniel Pfaller
Mühenad Bilal
Markus Bregulla
Rafał Cupek
Verlag: Springer International Publishing
Buch: Advances in Computational Collective Intelligence
Print ISBN: 978-3-030-88112-2

Electronic ISBN: 978-3-030-88113-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-88113-9_37

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