Abstract
In modern manufacturing industry featured with automation and flexibility, the intelligent machine tools management is essential for the workshop. In this work, we proposed a novel machine tools recognition system for classifying 3D models. A common and standard 3D tool database is constructed. The hybrid networks of Convolutional Neural Networks (CNNs) and Extreme Learning Machine (ELM) are developed for multiple view based 3D shape recognition. This framework utilizes the composited advantages of deep CNN architecture with the robust ELM auto-encoder feature representation, as well as the fast ELM classifier. The experimental results shows that it outperforms other methods which are using the manually specified 3D feature descriptors.
This work is supported in part by the Science and Technology Development Fund of Macao S.A.R (FDCT) under MoST-FDCT Joint Grant 015/2015/AMJ and grant FDCT/121/2016/A3, in part by University of Macau under grant MYRG2016-00160-FST, and MYRG2018-00248-FST.
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Zhang, K., Tang, LL., Yang, ZX., Luo, LQ. (2020). Intelligent Machine Tools Recognition Based on Hybrid CNNs and ELMs Networks. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_27
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DOI: https://doi.org/10.1007/978-3-030-23307-5_27
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