Abstract
The soldering techniques are one of the industrial techniques required in electronic device manufacturing plants. However, a framework for quantifying soldering techniques has not been established, therefore it hard to evaluate the training of trainees. Also, the safety is very important in the education of beginners. The haptics is able to transmit the power generated in the virtual space to the manipulator. Therefore, we perform soldering virtual training based on haptics and analyze the soldering motion based on time series analysis using training data in a virtual space. In this paper, we propose and evaluate a soldering education system based on haptics. The experimental results show that Long Short-Term Memory (LSTM) performs better than Recurrent Neural Network (RNN) in detecting dangerous movements.
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This work was supported by JSPS KAKENHI Grant Number JP20K19793.
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Toyoshima, K. et al. (2022). Experimental Results of a Haptics Based Soldering Education System: A Comparison Study of RNN and LSTM for Detection of Dangerous Movements. In: Barolli, L., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2022. Lecture Notes in Networks and Systems, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-031-14627-5_20
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DOI: https://doi.org/10.1007/978-3-031-14627-5_20
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