基于实验的统计模型用于合成纤维绳索的拉力特征:一种机器学习方法
Yahia Halabi1, Hu Xu2, Zhixiang Yu1
1School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China.
Scientific reports
|October 18, 2023
概括
这项研究分析了合成纤维绳 (聚,聚烯,尼龙),开发了准确的人工神经网络 (ANN) 模型,以预测它们的拉力应力-应变行为,提高设计效率.
科学领域:
- 材料科学 材料科学 材料科学
- 聚合物工程 聚合物工程
- 机械工程 机械工程
背景情况:
- 合成纤维绳在各种行业中至关重要,但它们复杂的拉伸行为需要准确的预测模型.
- 了解纤维特性,绳子构造和整体拉伸反应之间的关系对于材料选择和设计至关重要.
研究的目的:
- 为了研究聚,聚烯和尼龙绳索的拉伸行为.
- 建立一套关于绳索拉力特性的全面实验数据库.
- 开发和验证使用人工神经网络 (ANN) 的简化,预测性压力-应变构成规律.
主要方法:
- 差分扫描热量计 (DSC) 用于纤维的热史分析.
- 单个纤维和线组件的拉伸试验.
- 在最佳条件下进行了196次绳索拉力测试.
- 在 MATLAB 中使用实验数据集开发和优化 ANN 模型.
主要成果:
- 创建了一个广泛的合成纤维绳索拉伸特性数据库.
- ANN模型准确地预测了绳索的三线性应力-张力配置文件.
- 开发的ANN模型在预测故障强度和应变时达到了约5%的误差.
结论:
- 这项研究为合成纤维绳索的工艺结构和属性关系提供了宝贵的见解.
- 一个ANN模型提供了一种具有成本效益和高效的方法来预测绳索的拉力特性.
- 这项研究支持在工业应用中对合成纤维绳索性能进行实际设计和预测.
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