DP590冷轧高强钢力学性能微磁无损评价方法研究
Research on micromagnetic non-destructive evaluation method of mechanical properties of DP590 cold rolled high strength steel
张阳阳 1王林 2张栋 1李明远 1于洋 1王贤贤3
作者信息
- 1. 首钢集团有限公司技术研究院,北京 100043;绿色可循环钢铁流程北京市重点实验室,北京 100043
- 2. 首钢集团有限公司技术研究院,北京 100043;北京工业大学,北京 100000;绿色可循环钢铁流程北京市重点实验室,北京 100043
- 3. 北京工业大学,北京 100000
- 折叠
摘要
DP590是冷轧高强钢代表性产品之一,对其生产工艺要求极为严格.生产中由于存在带钢边部温降,其边部、中部力学性能波动较大,然而沿带钢宽向的全域力学性能评价是生产工艺参数调整的重要参考.目前采用的抽样、有损检测方法不能满足性能沿带钢宽向整体、全域的评价要求.微磁检测是一种无损、高效的性能评价方法,基于微磁原理,采集得到的多种磁特征与相应位置的力学性能相关.选用首钢顺义冷轧公司生产的DP590高强钢为研究对象,分析了多种微磁特征与其屈服强度、抗拉强度及断后伸长率(A80)之间的相关性,利用神经网络方法建立了相应的定量预测模型,模型预测精度在93％以上,可用于实际生产.
Abstract
DP590 is one of the representative products of cold rolled high strength steel,and its production process requirements are extremely strict.Due to the temperature drop at the edge of the strip in production,the mechanical properties of the edge and middle of the strip fluctuate greatly,but the evaluation of the mechanical properties of the whole area along the width of the strip is an important reference for the adjustment of production process parameters.At present,the sampling and destructive testing methods used cannot meet the evaluation requirements of the whole and the whole area of the properties along the width of the strip.Micromagnetic detection is a non-destructive and efficient property evaluation method,based on the principle of micromag-netism,and a variety of magnetic features collected are related to the mechanical properties of the corresponding position.DP590 high strength steel produced by Shougang Shunyi Cold Rolling Company was selected as the research object,and the correlation between various micromagnetic characteristics and their yield strength,tensile strength and elongation after break(A80)were an-alyzed,and the corresponding quantitative prediction model was established by neural network method,and the prediction accu-racy of the model was more than 93％,which can be used for actual production.
关键词
冷轧高强钢/力学性能/微磁无损检测/定量预测/神经网络Key words
cold rolled high strength steel/mechanical properties/micromagnetic non-destructive detection/quantitative forecas-ting/neural networks引用本文复制引用
出版年
2024
NETL
NSTL
万方数据