AlON-TiN复相材料合成工艺参数的支持向量回归分析

根据在不同热压烧结工艺参数（包括TiN的含量、烧结温度和保温时间）下合成的AlON-TiN复相材料的抗弯强度实测数据集,应用基于粒子群算法寻优的支持向量回归（SVR）方法,建立了AlON-TiN复相材料在不同热压烧结工艺参数下抗弯强度的SVR预测模型,并与基于人工神经网络（ANN）模型的预测结果进行了比较.利用SVR预测模型并结合粒子群算法对AlON-TiN合成工艺参数进行了寻优和多因素分析.结果显示:对于相同的训练样本和检验样本,AlON-TiN复相材料抗弯强度的SVR模型比ANN模型具有更小的预测误差,表明SVR模型比ANN模型具有更强的预测能力.工艺参数寻优结果表明,当TiN质量分数为13.5%、烧结温度为1863.5 ℃和保温时间为5.8 h时, 可获得抗弯强度为555.452 MPa的AlON-TiN复相材料. 研究结果表明，该方法对于研发理想抗弯强度的AlON-TiN复相材料具有重要的理论指导意义和实用价值. 关键词： AlON-TiN 抗弯强度 支持向量回归 回归分析

Study on the influence of process parameters on the synthesis of AlON-TiN composites by using support vector regression

According to the experimental dataset on the bending strength of AlON-TiN composite synthesized by hot pressing sintering approach under different processing parameters, i.e., mass fraction of TiN, sintering temperature and soaking time, the support vector regression （SVR） approach combined with particle swarm optimization for its parameter optimization, is proposed to simulate the relationship between the bending strength and hot pressing sintering synthesis parameters of AlON-TiN composites. The optimization of process parameters and the multi-factor analysis are also carried out. The prediction result demonstrates that the estimation error of the SVR model is less than that of the artificial neural network（ANN） model under the identical training and test samples and reveales that the generalization ability of SVR model surpasses that achieved by the ANN model. The optimal synthesis parameters are obtained numerically under TiN content 13.5%, sintering temperature 1863.5 ℃ and soaking time 5.8 h. The maximum bending strength is estimated to be 555.452 MPa while the AlON-TiN composite is synthesized at the optimal synthesis parameters. These results suggest that SVR can provide an important theoretical and practical guidance to the research and development of AlON-TiN composite possessing ideal bending strength.