On direct weight inverse approach for identifying composite parameters based on two-way TrumpetNets

Abstract

Reliable composite material parameter identification is of practical engineering significance in view of the extensive applications of composite materials in high-tech industries, such as aerospace, automobile industry and medical fields, etc. Efficient and easy-to-use computational tools are needed, and a new theoretical framework of two-way TrumpetNets have been proposed recently. It can solve both the forward and inverse problems effectively. The striking beauty of this theory is that the solution of the inverse problem is expressed by direct weight inversion (DWI) approach in an explicit formula. This study extends and implements the explicit inverse solution using two-way TrumpetNets for identification of material parameters of composite laminates. We successfully inversely identify up to seven parameters for composite laminates, which is essentially a 7-Dimensional inverse problem. It is found that by application of the two-way TrumpetNets, regularization parameters are not required in the DWI formula due to the intrinsic regularization by the least square formulation. The influence of different network structures, i.e., the number of hidden layers and the number of each layer, on the inverse result is explored. For similar composite material parameter identification, the computational efficiency is significantly improved in view of that, for inverse identification of composite parameters, it takes only 20 min computational time by two-way TrumpetNets. While this computational time is one sixth of that by the traditional inverse neural network using the same desktop computer, the computational accuracy is not compromised.