改进了CNN用于预测线路加热形成复杂船体曲板的变形
Shun Wang1, Haohao Jia1, Yuxuan Fu1
1Naval Architecture and Ocean Engineering College, Dalian Maritime University, Dalian 116026, China.
Materials (Basel, Switzerland)
|December 11, 2025
概括
这项研究介绍了一种智能变形预测模型,用于使用鱼迁移算法改进的卷积神经网络 (WMA-CNN) 建造船只的曲面板. 该WMA-CNN模型显著提高了自动船体板形成的预测准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 造船业依赖于手动线路加热,用于复杂的曲船体板.
- 曲板成型的自动化是造船行业的一个重大挑战.
研究的目的:
- 开发一个智能变形预测模型,用于自动化船体曲板形成.
- 通过使用增强算法来提高预测模型的准确性.
主要方法:
- 建立了基于应变直接边界的数值计算模型.
- 开发了一个卷积神经网络 (CNN) 用于变形预测.
- 引入了粒子群优化 (PSO) 和鱼迁移算法 (WMA) 以优化CNN重量,创建了PSO-CNN和WMA-CNN模型.
主要成果:
- 与CNN和PSO-CNN相比,WMA-CNN模型显示出更高的预测准确性.
- 收缩预测的平均绝对百分比误差 (MAPE) 减少了1.36%与WMA-CNN.
- 用于偏斜预测的MAPE与WMA-CNN.降低了4.05%
- 验证案例显示相对错误在5%以内.
结论:
- 该WMA-CNN模型是有效的智能预测曲线板形成.
- 这项研究为自动化船体曲板形成过程提供了宝贵的参考.
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