Experimental Verification of the Neural Network Optimization Algorithm for Identifying Frequency-Dependent Constitutive Parameters of Viscoelastic Materials

PurposeThe constitutive parameters of viscoelastic materials, such as storage modulus and loss factor, usually have frequency-dependent characteristics. The combination of polymers with different reinforcement and fillers usually exhibits various mechanical characteristics, which makes the identification of the material properties of viscoelastic materials a challenging task. The present study proposes an inverse identification technique based on a neural network optimization (NNO) algorithm to characterize the frequency-dependent material properties of a viscoelastic material.MethodsTo this end, a symmetric three-layered sandwich plate is considered having face layers of isotropic elastic material and a core layer of viscoelastic material. The experimental free vibration tests are performed using the impact hammer method to determine resonant frequencies and modal loss factors for various eigenmodes. In addition, a numerical model of the sandwich plate is developed to determine vibrational responses utilizing the finite element method. The vibration-based material parameter identification technique is implemented based on the NNO algorithm. The identified material parameters are then compared with the experimental dynamic mechanical analysis (DMA) test results. Furthermore, a numerical parametric study is performed considering the optimized viscoelastic material properties to investigate the influence of various geometrical and structural factors on the free vibration response of the sandwich plate.ResultsThe identified results are in excellent agreement with the experimental DMA test results affirming the robustness of the proposed inverse technique. The parametric study not only investigates the effect of various structural and geometric parameters on the dynamic response of the sandwich plate but also verifies that the calibrated properties are both realistic and physically meaningful.ConclusionsThe proposed algorithm is a useful and efficient tool for inverse identification of the constitutive properties, and this approach can be extended for the calibration of other parameters (constitutive or not) for a variety of viscoelastic materials in any field of application. This study is a critical step forward in understanding viscoelastic materials and their frequency-dependent behaviorur.

Automated summary

This study proposes an inverse identification technique based on a neural network optimization algorithm to characterize the frequency-dependent material properties of viscoelastic materials. The identified results are in excellent agreement with the experimental dynamic mechanical analysis (DMA) test results, affirming the robustness of the proposed inverse technique. The proposed algorithm is a useful and efficient tool for inverse identification of the constitutive properties.