Date: Friday, April 19, 2024

Time: 10:40 a.m. Research Presentation
          11:20 a.m. Teaching Presentation

Location: Donovan Hall G172

Title: Explainable Artificial Intelligence Prediction of Defect Characterization in Anisotropic Materials

Abstract:
Non-destructive evaluation (NDE) techniques are integral across diverse applications for void detection within composites. Infrared (IR) thermography (IRT) is a prevalent NDE technique that utilizes reverse heat transfer principles to infer defect characteristics by analyzing temperature distribution. Although the forward heat transfer problem is well-posed, its inverse counterpart lacks uniqueness, posing non-unique solutions. The present study performs simulations using finite element analysis (FEA) in defective (a penny-shaped defect) composites through which the heat transfer flux is modeled. A total of 2100 simulations with various defect positioning and size (depth, size, and thickness) are executed, and the corresponding surface temperature vs. time and vs. distance diagrams are extracted. The FEA outputs provide ample input data for the developing model based on explainable artificial intelligence (XAI) to estimate the defect characteristics. A detailed feature engineering task is performed to select the representative information from the diagrams. Explainable decision tree-based machine learning (ML) models with transparent decision paths based on derived features are developed to predict the defect depth, size, and thickness. The results that emerged from the predictive ML models suggest superb accuracy (R2 = 0.92 to 0.99) across all three defect characteristics. Harnessing the power of Explainable Artificial Intelligence (XAI) helps achieve highly accurate and understandable prediction paths. This enhanced trust in the results facilitates their optimal deployment, making them suitable for integration in manufacturing and industrial-scale applications.