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<p>In the field of modern industrial production, the quality of cable winding is of utmost significance for the performance and reliability of cable products. Among the key factors influencing cable winding quality, reducing the gap distance during the cable winding process is particularly crucial. This research holds great importance as it initiates a novel approach to resolve this long-standing issue in the cable manufacturing industry. This paper presents a lagangle error compensation method based on the whale optimization back propagation neural network (WOA-BP). The primary objective of this method is to compensate for the lag-angle error, thereby reducing the gap distance between cables and enhancing the winding quality. To validate the proposed method, a meticulously designed lag-angle error compensation experiment was carried out, and a total of 300 sets of experimental data were obtained. By leveraging the whale-optimized BP neural network, with the lag-angle, gap distance, and theoretical compensation value as input parameters, a lag-angle error compensation model was established. This model effectively compensates for the lag-angle error using the gap distance. The experimental results demonstrate that the model exhibits an extremely high level of accuracy. The maximum error between the predicted value and the true value of the detection data is 0.0036°, and all prediction errors are less than 0.014°. Moreover, the BP neural network error-compensation model optimized by the whale optimization algorithm has significantly improved the qualified rate of cable coiling quality to 95.96 %, providing a new solution for the error compensation of the cable coiling system. The high degree of accuracy between the predicted results and the true values strongly attests to the effectiveness and superiority of the proposed method. This research offers a practical and efficient solution for the cable manufacturing industry and is bound to have a profound impact on promoting the development of related industries.</p>

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Lag angle error compensation based on WOA-BP neural network

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Shuang Zhang,
  • Dongyu Sun,
  • Jingang Gao,
  • Hua Wang,
  • Guokai Zhu,
  • Wei Zhang

摘要

In the field of modern industrial production, the quality of cable winding is of utmost significance for the performance and reliability of cable products. Among the key factors influencing cable winding quality, reducing the gap distance during the cable winding process is particularly crucial. This research holds great importance as it initiates a novel approach to resolve this long-standing issue in the cable manufacturing industry. This paper presents a lagangle error compensation method based on the whale optimization back propagation neural network (WOA-BP). The primary objective of this method is to compensate for the lag-angle error, thereby reducing the gap distance between cables and enhancing the winding quality. To validate the proposed method, a meticulously designed lag-angle error compensation experiment was carried out, and a total of 300 sets of experimental data were obtained. By leveraging the whale-optimized BP neural network, with the lag-angle, gap distance, and theoretical compensation value as input parameters, a lag-angle error compensation model was established. This model effectively compensates for the lag-angle error using the gap distance. The experimental results demonstrate that the model exhibits an extremely high level of accuracy. The maximum error between the predicted value and the true value of the detection data is 0.0036°, and all prediction errors are less than 0.014°. Moreover, the BP neural network error-compensation model optimized by the whale optimization algorithm has significantly improved the qualified rate of cable coiling quality to 95.96 %, providing a new solution for the error compensation of the cable coiling system. The high degree of accuracy between the predicted results and the true values strongly attests to the effectiveness and superiority of the proposed method. This research offers a practical and efficient solution for the cable manufacturing industry and is bound to have a profound impact on promoting the development of related industries.