缝合复合材料面内刚度和强度的神经网络预测

基于神经网络的BP算法,建立了预测缝合复合材料刚度和强度性能的模型。根据试验所得的缝合复合材料的性能参数,训练人工神经网络,拟合出输入参数 (各种缝纫参数与等效未缝纫层合板性能参数)与输出参数(缝合层合板性能参数) 之间的非线性关系,设计完成了缝合复合材料弹性性能与强度分析软件,并以此软件分析计算在新的缝纫参数和等效未缝纫层合板性能参数情况下的缝合复合材料性能。与实验结果对比,两者符合较好。为缝合复合材料刚度强度预测开辟了一条新的有效途径。 

NEURAL NETWORK PREDICTING OF STIFFNESS AND STRENGTH IN PLANE OF STITCHED COMPOSITES

Models estimating stiffness and strength of stitched composites were established based on BP algorithm of neural network, and the longitudinal stiffness and strength networks were trained according to experimental data. The nonlinear relationship between input parameters and output parameters was determined and the analysis software of stitched laminates was completed. The longitudinal stiffness and strength of stitched laminates with new stitching parameters and new equivalent unstitched laminate performance parameters were predicted using the software. The results of the prediction and the test data are in good agreement. A very effective approach to estimate the stiffness and strength of stitched composites has been found.