Predicting the Impact Resistance of Epoxy Resin Composite Materials by Machine
Residual compressive strength(RCS)is an important indicator for evaluating the mechanical properties of composite materials after impact damages.The impact load of glass fiber reinforced epoxy resin composite materials was monitored online using acoustic emission(AE)technology.Four impact load parameters,including counts,counts to peak,signal strength,and root means square value were analyzed.The RCS of the specimen was predicted based on the impact load parameters using artificial neural network(ANN)and radial basis function network(RBF).The results show that the high impact energy causes the delamination of the specimen,glass fiber fracture,epoxy resin matrix cracking,and fiber debonding.When the impact energy is respectively 10,15,20 and 30 J,the impact energy reaches its maximum after 3 ms of impact,which is respectively 10.53,16.67,21.77 and 27.13 J.Then the impact energy continues to decrease.As the impact energy increases,the impact depth of the specimen increases from 0.18 mm to 3.35 mm,and RCS decreases from 56.87 MPa to 20.45 MPa.The optimal ANN model structure is 4-48-1,with a minimum mean square error(MSE)of 0.03 MPa for predicted and experimental RCS.The optimal RBF model structure is 4-21-1,with a minimum MSE of 0.01.The local response characteristics of the RBF model make it more robust to noise in the input data.The correlation coefficient(R2)between the predicted and experimental RCS data is 0.986 3,while the predicted result of ANN model is 0.951 4.
万方数据
维普
