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Novel fractional-order convolutional neural network based chatter diagnosis approach in turning process with chaos error mapping

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Abstract

The chatter not only brings about poor surface quality of the workpiece but also causes the tool wear and then leads to the increase in production cost over time. For this reason, it would be imperative that the chatter signal should be checked in an accurate manner whenever required. Because the chatter belongs to the nonlinear vibration phenomenon during the machining process, varied chatter characteristics will be presented under the different material, cutting speed and depth cutting conditions. Therefore, the machining learning method is used by many research programs by combining the database in order to analyze the vigorously changed data. To the chatter signal, thousands and even tens of thousands lots of data should be collected for use as training data and it would be extremely difficult for ordinary manufacturers and laboratories. It is because that not only will the tool be consumed but massive materials and time will also be required as far as the data collection is concerned. In this research, the “chaotic error map” is employed to accelerate the data processing in that 94.8% accuracy and 99.62% precision can be achieved simply with 60 lots of data only. Through the attractor properties of the chaotic system in the 3D space the input signal will be allowed to move along the attractor. Through such process, it transfers the highly variated chatter data to the consistent output result between the same classes. Further, this research is also the first program that proposes the fractional-order (FO) convolutional neural network (hereafter briefed as FOCNN) for chatter detection. Through the computation of fractional-order, it reduces 42.3% of trainable parameters when compared with the CNN having approximate training conditions while enhancing 3.8% of accuracy. Accordingly, our technique is also practical in use for the machining process.

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Acknowlegments

This work was supported by the Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

Funding

This work was supported by the Ministry of Science and Technology Taiwan Under Grants MOST 110-2221-E-194-037, 111-2218-E-194-007, 111-2221-E-194-052, 111-2634-F-005-001 and 111-2218-E-002-033.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Chung Cheng University, Chiayi, 62102, Taiwan

    Ping-Huan Kuo, Yung-Ruen Tseng & Her-Terng Yau

  2. Advanced Institute of Manufacturing With High-Tech Innovations (AIM-HI), National Chung Cheng University, Chiayi, 62102, Taiwan

    Ping-Huan Kuo, Po-Chien Luan & Her-Terng Yau

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  1. Ping-Huan Kuo
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Kuo, PH., Tseng, YR., Luan, PC. et al. Novel fractional-order convolutional neural network based chatter diagnosis approach in turning process with chaos error mapping. Nonlinear Dyn 111, 7547–7564 (2023). https://doi.org/10.1007/s11071-023-08252-w

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