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2019 | OriginalPaper | Buchkapitel

Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training

verfasst von : Masaru Teranishi, Shimpei Matsumoto, Hidetoshi Takeno

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing

Verlag: Springer International Publishing

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Abstract

The paper compares classification performances on Self-Organizing Maps (SOMs) by torus and spherical topologies in the case of peculiarities classification of flat finishing motion with an iron file measured by a 3D stylus. In case of manufacturing skill training, peculiarities of tool motion are useful information for learners. Classified peculiarities are also useful especially for trainers to grasp effectively the tendency of the learners’ peculiarities in their class. In the authors’ former studies, a torus SOM are considered to be powerful tools to classify and visualize peculiarities with its borderless topological feature map structure. In this paper, the authors compare the classification performance of two kind of borderless topological SOMs: torus SOM and spherical SOM by quality measurements.

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Titel: Comparative Research on SOM with Torus and Sphere Topologies for Peculiarity Classification of Flat Finishing Skill Training
verfasst von: Masaru Teranishi
Shimpei Matsumoto
Hidetoshi Takeno
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing
Print ISBN: 978-3-030-30507-9

Electronic ISBN: 978-3-030-30508-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-30508-6_41

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