Efficient inverse heat conduction modeling for parameter estimation and defect detection in multi-layer structures with internal heating

A fast inverse heat conduction model (IHCM) is developed for estimating unknown properties of multi-layer structures considering internal heating. The model leverages a closed-form analytical forward solution to enable efficient inverse computations. It requires only a single internal temperature measurement as input, with unknown parameters estimated by minimizing an objective function using an interior-point optimization algorithm. The IHCM accurately identifies thermal properties such as thermal conductivity, specific heat capacity, density, and heat transfer coefficient in a high-precision linear motor. It also detects internal geometric variations, including the location and severity of delamination caused by thermal expansion. These predictions are validated against finite element (FE) simulations. Furthermore, a sensorless strategy is proposed, enabling non-invasive parameter estimation based on electrically inferred temperature data. The feasibility, sensitivity, and limitations of the IHCM are assessed across various scenarios. Results demonstrate its strong potential for real-time diagnostics, online defect detection, and thermal performance monitoring in multi-layer composite systems with internal heat generation, such as electrical machines. • Fast inverse parameter estimation in multilayer composites with internal heating. • Diagnoses thermal aging and detects global and local delamination. • Flexible analytical forward framework for multiple non-homogeneities. • High computational speed enables real-time, lightweight thermal diagnosis. • Noninvasive, sensorless estimation through electrically inferred temperature.