一种机器学习方法用于模拟各种不钢的冷曲线
Julia Contreras-Fortes1,2, M Inmaculada Rodríguez-García3, David L Sales2
1Laboratory and Research Section, Technical Department Acerinox Europa S.A.U., 11379 Los Barrios, Spain.
Materials (Basel, Switzerland)
|January 11, 2024
概括
人工神经网络 (ANN) 准确地预测不钢的情况.
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.
- 计算材料科学科学 计算材料科学
背景情况:
- 不钢表现出冷加工硬化,影响其在成型过程中的机械行为.
- 预测应变硬化性能对于通过冷制造制造平板不钢产品至关重要.
研究的目的:
- 开发不钢机械性能的预测模型.
- 根据构成和冷减,预测拉伸强度,屈服强度,硬度和延长.
主要方法:
- 使用人工神经网络 (ANN) 和多重线性回归 (MLR).
- 在实验室尺度上冷各种不钢等级 (奥斯泰尼特,费里特,双).
- 通过拉力测试来确定机械性能,以创建一个预测数据库.
主要成果:
- 在预测机械性质方面,ANN模型表现出高准确度.
- 模型成功地将化学成分和冷厚度减小与机械结果相关联.
- 为机器学习应用建立了一个强大的数据库.
结论:
- 机器学习模型为设计新的不钢等级提供了宝贵的工具.
- 这些模型可以帮助优化不钢的冷成型工艺.
- 预测能力增强了材料工程和产品开发.
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