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在ANN模型中使用特定Jominy距离的可能性,用于预测低合金钢的微观结构
Tea Marohnić1, Sunčana Smokvina Hanza1, Dario Iljkić1
1University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000 Rijeka, Croatia.
Materials (Basel, Switzerland)
|February 13, 2025
概括
这项研究使用人工神经网络 (ANN) 来预测钢铁微结构成分,如铁和马氏体,而不需要硬度数据. 特定的Jominy距离可以提高过程规划的预测准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 金工业是一种金工业.
- 计算材料科学科学 计算材料科学
背景情况:
- 准确预测钢铁微结构成分 (铁矿,珍珠矿,贝尼特,马石) 对于材料性能量身定制至关重要.
- 传统方法通常需要硬度数据,限制了早期的过程规划.
研究的目的:
- 开发和评估人工神经网络 (ANN),用于预测低合金钢中微结构成分的体积分数.
- 使用易于获得的参数来评估模型的性能,不包括灭后的硬度.
- 为了研究特定的Jominy距离作为输入变量的影响.
主要方法:
- 人工神经网络 (ANN) 用于预测建模.
- 测试了三种不同的输入配置,以优化预测性能.
- 合金成分,热处理参数和特定的Jominy距离被用作输入变量.
主要成果:
- 在没有依赖硬度数据的情况下,ANN模型成功预测了微结构成分.
- 包括特定的Jominy距离作为输入,显著提高了模型性能.
- 对铁矿,珍珠矿和马氏体的预测显示,与贝尼特相比,其准确性更高.
结论:
- 在热处理之前,ANN提供了一种实际方法来估计钢的微结构组成.
- 特定的Jominy距离可以成为工业应用中详细化学成分的可行替代方案.
- 该模型有助于过程优化,并减少钢铁制造中的试错.
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