使用机器学习预测粘合剂结合的单关节的爬行失效寿命
Faizullah Jan1, Marcin Kujawa2, Piotr Paczos3
1Gdańsk University of Technology, G. Narutowicza 11/12, 80-233, Gdańsk, Poland.
Scientific reports
|February 26, 2025
概括
预测粘合关节的故障至关重要. 机器学习,特别是随机森林模型,通过分析诸如爬行应变和拉伸强度等关键特征,准确预测爬行失效寿命.
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:
- 预测单粘合剂关节 (SLAJ) 的爬行失效寿命具有挑战性,需要大量的资源和时间.
- 确保结构完整性和防止粘合剂接头的过早故障需要准确的爬行故障寿命预测.
研究的目的:
- 利用机器学习 (ML) 来识别影响SLAJ在爬行条件下的耐用性的关键特征.
- 开发和验证一种ML模型,用于预测SLAJs的爬行失效寿命.
主要方法:
- 通过相关性分析和顺序性特征选择来识别特征.
- 应用多个ML算法来建模特征和爬虫失败寿命之间的复杂关系.
- 使用Python开发一个预测模型,通过实验测试验证.
主要成果:
- 影响爬行失效寿命的关键特征包括SLAJ爬行应变,粘合剂抗拉强度 (UTS),SLAJ爬行应力,粘合剂表面积 (A) 和Young的模量 (E).
- 随机森林 (RF) 模型在预测爬行失效寿命方面表现出最高的准确性.
- ML模型的预测经过实验验证,证实了它的可靠性.
结论:
- 机器学习有效地识别了预测接关节爬行失败的关键因素.
- 开发的射频模型提供了一种准确且经过实验验证的方法,用于预测SLAJs中的爬行失效寿命.
- 该研究提供了有价值的见解和工具,用于提高工程应用中粘合剂接头的耐用性和可靠性.
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