利用遗传算法（GA）和神经网络对木塑复合材料力学性能进行预测。首先利用神经网络构建木塑复合板材主要工艺参数热压时间（T）、马来酸酐（MA）和废旧塑料聚丙烯（PP）与材料力学性能内结合强度（IB）、静曲强度（MOR）、弹性模量（MOE）和吸收厚度膨胀率（TS）之间的关系模型,然后利用遗传算法对模型进行优化和训练;最后利用训练好模型对材料的力学性能进行预测以及模型验证。结合显示优化模型预测的板材的MOE的误差范围分别为2%～15.5%、9%～38%和4%～70%,远小于未优化模型的预测误差8%～1491%、2.8%～1950%和15%～128%;对IB、MOR和TS的预测也有相似的结果。
Genetic algorithm（GA） and artificial neural network（ANN） were employed to study on performance prediction of wood-plastics composite（WPC） material.Firstly,ANN was adopted to build the relationship between mechanical performance of WPC,which was internal bond strength（IB）,modulus of rupture（MOR）,modulus of elasticity（MOE） and thickness swelling（TS）,and main technological parameters,which were hot-pression time（T）,the recycled polypropylen（PP） and maleic anhydride（MA）.Secondly,the weight and threshold of ANN model were optimized by GA.Finnally,mechanical performance of WPC was predicted by the optimized ANN models;and the proof generalization test of ANN was done.The results show the errors of MOE predicted by GA optimized model are 2%~15.5%、9%~38% and 4%~70% respectively,which is less than those of non-optimized model.There is the same conclusion as for IB,MOR and TS.
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