基于SiCp/Al-7.75Fe-1.04V-1.95Si复合材料的人工神经网络的塑料可操作性和风湿性压力模型
Pinming Feng1, Shuang Chen2, Jie Tang1
1College of Materials Science and Engineering, Hunan University, Changsha 410082, China.
Materials (Basel, Switzerland)
|November 9, 2024
概括
一个人工神经网络 (ANN) 模型准确地预测了SiCp/Al-Fe-V-Si复合材料的复杂变形. 这些材料的最佳加工涉及特定的温度和变形率,以提高可加工性.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算机建模 计算建模
背景情况:
- SiCp/Al-Fe-V-Si复合材料由于SiC粒子和Al12(Fe,V) 3Si相而表现出复杂的变形.
- 了解这些行为对于优化它们在不同温度下的性能至关重要.
研究的目的:
- 为SiCp/Al-7.75Fe-1.04V-1.95Si复合材料开发和验证一个人工神经网络 (ANN) 构成模型.
- 为了研究塑料的可加工性,并确定最佳的加工参数.
主要方法:
- 单轴压缩测试用于收集实验数据.
- 人工神经网络 (ANN) 建模用于构成性行为预测.
- 微结构观测,有限元素分析 (FEA) 和处理地图用于可操作性分析.
主要成果:
- 在预测实验压力-应变曲线方面,ANN模型取得了高准确性 (R2 = 0.999).
- 使用嵌入式ANN模型的FEA模拟准确计算了塑料和机械反应.
- 一个热力学处理图确定了最佳参数:450-500°C和0.1-0.2s-1 .
结论:
- 开发的ANN模型有效地捕捉了SiCp/Al-Fe-V-Si复合材料的复杂变形行为.
- 该研究提供了一种可靠的方法来预测材料反应,并优化加工条件,以提高塑料加工能力.
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