修改的泰勒冲击测试用配置铜圆柱体:实验和优化失位可塑性模型的实验
Egor S Rodionov1, Victor V Pogorelko1, Victor G Lupanov1
1Department of General and Theoretical Physics, Chelyabinsk State University, 454001 Chelyabinsk, Russia.
Materials (Basel, Switzerland)
|August 26, 2023
概括
一种新的实验数值方法优化了金属的动态可塑性模型,使用了配置投射器和机器学习. 这种方法提高了工程应用的材料表征和模型精度.
科学领域:
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
- 固体力学 固体力学是什么
背景情况:
- 先进的数值模拟和机器学习使复杂的负载条件能够用于塑性模型参数识别.
- 这扩大了检查的变形状态的范围,使模型与工程实践更加紧密地对齐.
- 金属的动态可塑性需要准确的模型,用于涉及高延展率的应用.
研究的目的:
- 在动态可塑性模型中开发和应用综合实验-数值方法来识别参数.
- 用新型实验技术研究冷OFHC铜的动态可塑性.
- 使用先进的计算和统计方法优化排位可塑性模型.
主要方法:
- 在泰勒撞击测试中使用了成型弹头 (缩小的圆柱体/圆) 来进行材料特征测试,在延伸速度高达10^5s^-1.1时,实现了很大的塑性变形 (真正的应变率高达1.3).
- 实现了一个3D脱位可塑性模型与光滑粒子水力学 (SPH) 数值方案.
- 采用贝叶斯统计方法与人工神经网络 (ANN) 结合,作为SPH模拟器来进行参数优化.
主要成果:
- 与经典的泰勒气相比,配置弹头可以优化可塑性模型参数.
- 结合不同的投射器形状和增加实验数据显著提高了优化质量.
- 优化的模型成功地与飞行员板实验中的冲击波形状进行了验证.
- 该模型被扩展到估计谷物精炼和减弱区域的体积分数.
结论:
- 为优化动态可塑性模型提出了一种具有成本效益,简单和高效的方法.
- 开发的方法提高了可塑性模型与工程实践的一致性.
- 这项研究证明了配置弹头和机器学习在推进动态事件材料建模方面的有效性.
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