摘要
高Co-Ni二次硬化钢是一种多元系合金,其力学性能受材料成分及热处理工艺等条件影响。传统的数学模型很难对材料力学性能进行预测。采用增量型极限学习机(I-ELM)建立高Co-Ni二次硬化钢材料模型,根据模型预测的结果研究微量元素Co和时效温度对高Co-Ni二次硬化钢材料力学性能的影响,并且在多项性能指标方面对高Co-Ni二次硬化钢数据的I-ELM模型与BP神经网络模型进行比较。实验结果表明,在对较少样本数据的模型训练时,高Co-Ni二次硬化钢的I-ELM模型预测结果与实验数据基本吻合,I-ELM模型的拟合精度和训练速度均优于BP神经网络模型,为今后高Co-Ni二次硬化钢的材料研究提供参考。
High Co-Ni secondary hardening steel is a muti-component alloy, whose mechanical properties are affected by material composition and heat treatment process. Traditional mathematical models are difficult to predict the mechanical properties of materials. This paper uses the incremental extreme learning machine(I-ELM) algorithm to establish a high Co-Ni secondary hardening steel material model, studies the effect of trace element Co and aging temperature on mechanical properties of high Co-Ni secondary hardening steel according to the model predicting results, and comparing the I-ELM model and BP neural network model of high Co-Ni secondary hardened steel data on multiple performance indicators. The experimental result in this paper shows that the I-ELM model predicting results of the high Co-Ni secondary hardening steel are basically consistent with the experimental data when training the model with less sample data, both the fitting accuracy and training speed of the I-ELM model are better than the BP neural network, thereby providing a reference for material research of high Co-Ni secondary hardening steel in future.
作者
吴迪
曹培智
张国英
焦兴强
WU Di;CAO Peizhi;ZHANG Guoying;JIAO Xingqiang(College of Physical Science and Technology, Shenyang Normal University, Shenyang 110034, China)
出处
《沈阳师范大学学报（自然科学版）》
CAS
2019年第3期223-227,共5页
Journal of Shenyang Normal University:Natural Science Edition
基金
国家自然科学基金资助项目(11804235)
关键词
二次硬化钢
I-ELM
力学性能
微量元素
secondary hardening steel
I-ELM
mechanical properties
trace element
