Abstract
The paper proposes an unsupervised classification method for peculiarities of flat finishing motion with an iron file, measured by a 3D stylus. The proposed method extract personal peculiarities based on trajectory of an iron file. The classified peculiarities are used to correct learner’s finishing motions effectively for skill training. In the case of such skill training, the number of classes of peculiarity is unknown. A torus type Self-Organizing Maps is effectively used to classify such unknown number of classes of peculiarity patterns.
Experimental results of the classification with measured data of an expert and sixteen learners show effectiveness of the proposed method.
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Teranishi, M., Matsumoto, S., Takeno, H. (2019). Peculiarity Classification of Flat Finishing Motion Based on Tool Trajectory by Using Self-organizing Maps. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_10
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DOI: https://doi.org/10.1007/978-3-319-94649-8_10
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Online ISBN: 978-3-319-94649-8
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