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Updated: Jan 10, 2026

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Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
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使用深度神经网络和灰色关系分析进行多目标优化,以优化CFRP结构布局
Min-Gi Kim1, Jae-Chang Ryu2, Chan-Joo Lee3
1Department of Nanomechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea.
Materials (Basel, Switzerland)
|November 27, 2025
概括
本研究引入了一种结合深度神经网络 (DNN) 和灰色关系分析 (GRA) 的新方法,用于优化碳纤维增强塑料 (CFRP) 组件. 该方法有效地提高了汽车零部件的结构强度和安全性.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 优化复合材料布局对于汽车零部件至关重要.
- 在碳纤维增强塑料 (CFRP) 件中平衡结构强度和故障安全性是一个多目标的挑战.
- 传统的优化方法可能是计算密集型的.
研究的目的:
- 提出和验证CFRP汽车部件的新型多目标优化框架.
- 集成深度神经网络 (DNN) 和灰色关系分析 (GRA) 以实现高效的布局配置优化.
- 同时提高CFRP组件的结构强度和故障安全性.
主要方法:
- 开发一个DNN替代模型,训练在许多布置序列的有限元模拟上.
- 利用DNN模型在所有可能的布局组合中确定帕雷托最佳解决方案.
- 应用GRA以根据设计者偏好选择最佳配置.
- 在曲试验中使用制造的CFRPB柱进行实验验证.
主要成果:
- 该DNN-GRA方法实现了对铺设配置的高预测准确度.
- 经过优化布置设计,结构强度和故障安全性均显著改善.
- 实验验证证证实了模拟结果的错误率低于5%.
- 与传统方法相比,这种方法显示了较少的计算力度.
结论:
- 拟议的DNN-GRA框架为CFRP组件的多目标优化提供了一种高效和灵活的方法.
- 这种方法有效地平衡了竞争的设计目标,如强度和安全性.
- 经过验证的框架为汽车应用中的复合材料设计提供了可靠的预测.
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