疲劳失败预测过程的演变从实验到人工智能:一篇评论
Cornel Samoila1,2, Doru Ursutiu3,4, Iuliana Tudorache Nistor1
1Faculty of Material Science and Engineering, Transylvania University of Brasov, Bdul. Eroilor Nr. 29, 500036 Brasov, Romania.
Materials (Basel, Switzerland)
|March 13, 2025
概括
疲劳破裂预测正在发展,新的人工智能辅助方法缩短了开发时间. 目前的方法面临局限性,但将物理和数据驱动方法结合起来,有望提高疲劳分析的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 疲劳破裂预测方法随着时间的推移而演变,发育阶段逐渐变短.
- 传统方法面临的局限性是由于许多影响因素和缺乏实际考虑.
- 最近的进展侧重于将基于物理的模型与数据驱动的人工智能 (AI) 方法相结合,以提高预测准确度.
研究的目的:
- 介绍疲劳破裂预测方法的进化轨迹.
- 突出向混合方法的转变,将物理机制与人工智能结合起来.
- 分析这个主题的不同审查方法的有效性.
主要方法:
- 采用了半系统和综合性审查方法的组合.
- 这些方法之所以被选中,是因为它们在分析该主题时具有互补的优势.
- 该研究分析了疲劳预测的历史发展和当前趋势.
主要成果:
- 实验期代表了疲劳预测进化的最长发展阶段.
- 综合先进的数学方法和人工智能显著加快了进化和减少了实施时间.
- 尽管取得了进展,但所有疲劳破裂预测方法都有局限性.
结论:
- 疲劳破裂预测的演变的特点是发展阶段的减少,由强大的数学和人工智能方法驱动.
- 将基于物理的疲劳失败机制与数据驱动的AI相结合,为提高预测准确性提供了一个有希望的途径.
- 混合审查方法 (半系统和整合) 有效地补充了这个不断变化的领域的分析.
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