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Automatic Hitting-Duration Estimation of a Rechargeable Impact Wrench Using a Fuzzy Neural Network to Reach Target Toques

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Abstract

This paper proposes an automatic hitting-duration estimation method for a rechargeable impact wrench using a fuzzy neural network (FNN) to reach target torques. To manipulate a commercial rechargeable impact wrench to output a proper torque for a fastener and battery specification, an operator must determine the corresponding hitting duration based on experiences and experimental trials. Nevertheless, the determined hitting duration is fixed and cannot adapt to changes in battery voltage outputs affected by several factors, such as battery capacity and degradation, which leads to poor torque regulation. This paper addresses this problem by applying an FNN to estimate proper hitting durations to achieve target torques under different battery conditions. Without additional embedding of torque feedback sensors on the impact wrench, this paper feeds four easily available states as inputs to the FNN, including motor revolutions per minute, motor current, battery voltage, and target torque. Supervised input–output data are collected to train the FNN to estimate the optimal hitting durations for target torque regulation. The FNN-based hitting-duration estimation method is implemented in a microcontroller unit to control a real rechargeable impact wrench with different power sources. Experimental results show that proper hitting durations are estimated by the FNN under different battery conditions to control the rechargeable impact wrench to achieve different target torques.

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Acknowledgements

This work was supported by the Ministry of Science and Technology, Taiwan, under Grant MOST 110-2221-E-005-082-MY2.

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  1. Department of Electrical Engineering, National Chung Hsing University, Taichung, 402, Taiwan, ROC

    Chia-Feng Juang & Yi-Wei Chen

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  1. Chia-Feng Juang
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Correspondence to Chia-Feng Juang.

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Juang, CF., Chen, YW. Automatic Hitting-Duration Estimation of a Rechargeable Impact Wrench Using a Fuzzy Neural Network to Reach Target Toques. Int. J. Fuzzy Syst. 25, 29–41 (2023). https://doi.org/10.1007/s40815-022-01387-9

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  • DOI: https://doi.org/10.1007/s40815-022-01387-9

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