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使用结合标准和人工神经网络优化摩擦点接过程.
Deok Sang Jo1, Parviz Kahhal1,2,3, Ji Hoon Kim1
1School of Mechanical Engineering, Pusan National University, Geumjeong-gu, Busan 46241, Republic of Korea.
Materials (Basel, Switzerland)
|May 27, 2023
概括
这项研究优化了使用人工神经网络和有限元素分析 (FEA) 的摩擦点接 (FSSW) 参数. 压力-时间流量标准被证明是最合适的,导致对粘合强度和硬度的准确预测.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算建模 计算建模
背景情况:
- 固态连接过程需要强大的粘合标准来确保接完整性.
- 摩擦点接 (FSSW) 是一种具有复杂工艺动态的有前途的固态连接技术.
- 传统的债券标准可能无法完全捕捉到FSSW的复杂性.
研究的目的:
- 通过有限元分析 (FEA) 来分析和验证FSSW的结合标准.
- 使用人工神经网络 (ANN) 确定FSSW最佳过程参数,以提高接质量.
- 为了验证,将预测结果与实验结果进行比较.
主要方法:
- 使用ABAQUS-3D的有限元素分析 (FEA) 显式与合的欧勒尔-拉格朗式定制用于大变形.
- 将压力-时间和压力-时间流量结合标准应用于FEA结果.
- 通过基于粘合标准的ANN优化工艺参数 (包括工具旋转速度).
- 对粘合强度和硬度的优化参数进行实验验证.
主要成果:
- 与压力-时间流量标准相比,压力-时间流量标准被认为更适合FSSW.
- 人工神经网络成功优化了过程参数,预测了粘合强度和硬度.
- 工具旋转速度被发现是对粘合强度和硬度最有影响的参数.
- 实验验证显示了高精度,粘合强度误差为3.675%,硬度误差为3.197%.
结论:
- 由FEA驱动的方法与ANN相结合,为优化FSSW参数提供了一种有效的方法.
- 压力-时间流量标准是评估FSSW接头质量的可靠指标.
- 优化的参数显著提高了接区的硬度和粘合强度,通过实验数据验证.
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