为了提高不锈钢应力腐蚀开裂(SCC)敏感性预测的准确性与科学性,通过主成分分析(PCA)提取出不锈钢SCC行为的主要影响因素作为后续模型的输入,随后将机器学习不同流派的代表算法混合成Hybrid模型,并用粒子群优化(PSO)算法进行优化,提出不锈钢SCC敏感性的预测模型PSO-Hybrid。以某奥氏体不锈钢实测数据为例,对比预测值与实际值,以验证模型的可靠性与优劣性。结果表明:Hybrid思想有一定的可行性和科学性,且经PSO优化后,Hybrid模型的平均准确度与马修斯相关系数各提高了3.3%与8.3%,PSO-Hybrid模型的预测准确度高、稳定性好。

In order to improve the accuracy and scientificity of the prediction of stress corrosion cracking (SCC) sensitivity of stainless steel, the main influencing factors of SCC behavior of stainless steel were extracted by principal component analysis (PCA) as the input of the subsequent model, and then the representative algorithms of different schools of machine learning were mixed into Hybrid model, and optimized by particle swarm optimization (PSO) algorithm, and the prediction model PSO-Hybrid of SCC sensitivity of stainless steel was proposed. Taking the measured data of an austenitic stainless steel as an example, the predicted value and the actual value were compared to verify the reliability and superiority of the model. The results showed that the Hybrid idea was feasible and scientific, and after PSO optimization, the average accuracy of the Hybrid model and the Matthews correlation coefficient were increased by 3.3% and 8.3% respectively. The PSO-Hybrid model had high prediction accuracy and good stability.