热镀铝锌机组产品力学性能预报技术
Prediction technology for mechanical properties of hot-dip aluminum zinc plating unit products
魏宝民 1王孝建 2白振华3
作者信息
- 1. 宝钢股份中央研究院梅钢技术中心,江苏南京 210039
- 2. 宝钢股份中央研究院梅钢技术中心,江苏南京 210039;燕山大学国家冷轧板带装备及工艺工程技术研究中心,河北秦皇岛 066004
- 3. 燕山大学国家冷轧板带装备及工艺工程技术研究中心,河北秦皇岛 066004;燕山大学亚稳材料制备技术与科学国家重点实验室,河北秦皇岛 066004
- 折叠
摘要
针对热镀铝锌机组产品力学性能超差的问题,在对产品力学性能影响参数进行重要度分析和数据样本聚类分析的基础上,建立热镀铝锌机组产品力学性能BP神经网络预报模型,实现带钢屈服强度、抗拉强度与断后伸长率等力学性能参数在统计学意义上的计算.基于平整轧制机理模型,以变形抗力为桥梁,根据平整轧制过程中实时轧制力数据模型,反算出带钢出口处变形抗力的波动情况,以此对由BP神经网络模型预报得到的屈服强度、抗拉强度和断后伸长率进行修正,进一步形成了一套神经网络模型与物理冶金模型相结合的热镀产品力学性能预报技术.将此技术应用到了某钢厂热镀铝锌机组生产现场,为该机组生产工艺的制定提供了理论依据.
Abstract
Aiming at the issue of poor mechanical properties of hot-dip aluminum zinc plating unit products,based on the importance analysis and data sample clustering analysis of influencing parameters for product mechanical proper-ties,a BP neural network prediction model for mechanical properties of the hot-dip aluminum zinc plating unit prod-uct was established.The calculation of mechanical properties parameters such as yield strength,tensile strength,and elongation after fracture of the strip steel was achieved in statistical sense.Based on the flat rolling mechanism model and using deformation resistance as a bridge,the fluctuation of deformation resistance at the exit of the strip steel was calculated based on the real-time rolling force data model during the flat rolling process.Thus the yield strength,tensile strength,and elongation after fracture predicted by the BP neural network model were modified,and a set of mechanical properties prediction technology of hot-dip products combining neural network model and physical metallurgy model was further formed.The application of this technology to the on-site production of hot-dip aluminum zinc plating unit in a steel plant provides theoretical basis for the formulation of the production process of the unit.
关键词
热镀铝锌机组/力学性能/神经网络/变形抗力/预报Key words
hot-dip aluminum zinc plating unit/mechanical property/neural network/deformation resistance/pre-diction引用本文复制引用
基金项目
河北省高等学校科学技术研究项目(CXY2023012)
河北省重大科技成果转化专项(22281001Z)
河北省科学技术研究与发展计划-科技支撑计划项目(23280101Z)
辽宁省教育厅高等学校基本项目(LJKZZ20220040)
中央引导地方科技发展资金项目(236Z1024G)
出版年
2024
NETL
NSTL
维普
万方数据