用人工智能和灰色关系分析对聚 spun 的速度框架参数进行多目标优化
Habib Amiri Savadroodbari1, Milad Razbin2, Mohsen Rezahasani3
1Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran. habib.amiri1375@gmail.com.
Scientific reports
|October 31, 2025
概括
与传统方法相比,人工智能 (AI) 模型显著改善了聚线生产优化. 基于人工智能的优化通过确定织制造的最佳速度框架参数来提高质量.
科学领域:
- 工业制造业 工业制造业 工业制造业
- 织工程 织工程 织工程
- 人工智能的人工智能
背景情况:
- 工艺优化对于工业效率至关重要,尤其是在织品行业.
- 像灰色关系分析 (GRA) 和响应表面方法 (RSM) 这样的传统方法在复杂的优化任务中存在局限性.
- 开发先进的技术是必要的,以提高线生产质量和运营效率.
研究的目的:
- 将基于AI的方法与GRA进行比较,以优化100%聚 spun yarn生产中的速度框架参数.
- 评估人工神经网络 (ANN) 模型与RSM的预测准确性.
- 评估基因算法 (GA) 在织制造业多标准优化方面的有效性.
主要方法:
- 响应表面方法 (RSM) 用于实验设计.
- 灰色关系分析 (GRA) 用于根据线均度 (CVm%) 和不完美指数 (IPI) 计算灰色关系等级 (GRG).
- 人工神经网络 (ANN) 用于预测建模和遗传算法 (GA) 用于优化.
主要成果:
- 与RSM模型 (R2 ≈ 0.65) 相比,ANN模型显示出更高的预测准确性 (R2 ≈ 1).
- 基于GA的优化结果比GRA更好,从而提高了线质量.
- 确定了最佳参数:扭转率 = 23 TPM,破裂 draft = 1.26,间距尺寸 = 5.1 mm,悬架 = 3.5 mm.
结论:
- 基于人工智能的方法,特别是ANN和GA,为优化织制造工艺提供了卓越的性能和可靠性.
- 这些发现表明,我们应该转向智能优化技术,以提高工业效率和产品质量.
- 这项研究证实了人工智能在通过数据驱动的流程改进来推动织行业的潜力.
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