相关实验视频
Updated: Jul 11, 2025
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Determining the Mechanical Strength of Ultra-Fine-Grained Metals
Published on: November 22, 2021
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使用神经网络模型来评估基于硬度的铁的机械和 tribological 属性
Gamal M A Mahran1, Abdel-Nasser Mohamed Omran2
1Mining Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Heliyon
|November 6, 2023
概括
这项研究表明,硬度测试可以准确预测铁的抗拉强度,磨损率和冲击能量. 人工神经网络提高了对含量和材料性能的预测准确度.
科学领域:
- 材料科学 材料科学 材料科学
- 金工业是金工业的一个方面.
- 机械工程 机械工程
背景情况:
- 像铁这样的金属的抗拉强度,耐磨性和冲击强度测试是复杂而昂贵的.
- 硬度测试为材料属性评估提供了一个更简单,更经济的替代方案.
研究的目的:
- 为了研究硬度 (HB) 和密集石墨铁 (CGI) 的关键机械性能 (抗拉强度 - TS,磨损率 - WR,冲击能量 - IE) 之间的相关性.
- 根据硬度测量,评估回归分析和人工神经网络 (ANN) 在预测这些特性和 (Mg) 含量的有效性.
主要方法:
- 用3.5%,4.0%和4.5%的不同碳等价物 (CE) 制备了三组CGI实验组,这些碳等价物用结节剂合金 (Fe-Si-Mg) 处理.
- 进行了硬度 (HB) 测试,结果与测量TS,WR和IE相关联.
- 使用回归分析和ANN来建模和预测Mg%,TS,WR和IE使用HB数据.
主要成果:
- 生产的CGI表现出硬度范围为191-226 HB,TS为402-455 MPa,WR为30.1-23.8 mg/cm2,IE为22-15 J. 这些硬度范围包括:
- 在CE为4.5%和Mg含量在0.0118%-0.0155%之间时,获得了最佳结果.
- 基于硬度的ANN模型对Mg%和CGI属性 (TS,WR,IE) 具有很高的预测准确性,CE=4的平均平方误差 (MSE) 很低 (例如,Mg%的3.7E-8).
结论:
- 硬度测试提供了一种具有成本效益的方法,用于近似CGI的拉伸强度,磨损率和冲击能量.
- 人工神经网络显著提高了从硬度数据中预测CGI特性和Mg%含量的精度.
- 该研究验证了ANN在铁生产中的质量控制和材料表征方面的潜力.
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