机器学习辅助2000 MPa级弹簧钢成分和热处理工艺开发

杨累, 赵帆, 姜磊, 谢建新

Development of Composition and Heat Treatment Process of 2000 MPa Grade Spring Steels Assisted by Machine Learning

YANG Lei, ZHAO Fan, JIANG Lei, XIE Jianxin

表1 MLDS预测结果和实验验证结果的比较

Table 1 Comparisons of predicted results by MLDS and experimental results

Steel Chemical composition (mass fraction / %) Heat treatment parameter Mechanical property C Si Mn Cr Ni Mo V Nb TQ tQ TT tT Rm A Rp0.2 Z oC min oC min MPa % MPa % 1# predicted 0.50 1.63 0.73 1.20 0.21 0.27 0.14 0.020 907 31 423 89 2075 12.0 - - 1# actual 0.55 1.76 0.70 1.10 0.21 0.20 0.14 0.016 910 30 420 90 2046 11.9 1644 38.0 2# predicted 0.57 1.70 0.70 1.17 0.18 0.22 0.34 0.010 914 33 420 93 2096 10.9 - - 2# actual 0.54 1.75 0.64 1.18 0.20 0.20 0.37 0.003 910 30 420 90 2044 10.0 1695 33.9