A COMBINED FINITE ELEMENT AND MACHINE LEARNING APPROACH TO ACCELERATE CALIBRATION AND VALIDATION OF NUMERICAL MODELS FOR PREDICTION OF FAILURE IN AEROSPACE COMPOSITE PARTS

The need for a large number of costly experimental tests is a major challenge in the certification of composite parts in the aerospace industry. While computational simulation, specifically the finite element method (FEM), can accelerate this process through analysis supported by testing, the time-consuming calibration of composite failure models remains a drawback. This often requires an engineer with years of experience to perform trial-and-error simulations. To address this, an automated machine learning framework has been proposed to simulate and calibrate the failure models of composites in an accelerated and low-cost manner. In this framework, FEM parameters are calibrated using probabilistic machine learning in a step-by-step and targeted approach. The single-edge notch tension (SENT) test was chosen as a case study and simulated explicitly with predefined initial parameters. The result was compared with experiments to calculate the calibration error. Then, Gaussian process regression (GPR) was used in an inhouse developed program to automatically select new failure parameters based on initial values and the calibration error in the previous step. The program continues this process until the error becomes negligible. For validation, the calibrated model was used to predict the behavior of a center-notch tension (CNT) test. The presented automated framework provides a robust and reliable calibrated model in a significantly faster manner. It also reduces a substantial amount of computational costs, eliminates the need for high expertise, and saves valuable engineering time.