The interconnection between engineering simulations, real-world experiments, and virtual reality remains underutilised in engineering. This study addresses this gap by implementing such interconnections, focusing on active thermography for a carbon fibre plate in the aerospace domain. Six scenarios based on three parameters were simulated using ComSol Multiphysics 6.2 and validated experimentally. The results were then integrated into a virtual reality serious game developed with Unreal Engine 5.3.2 and aimed at educating users on thermography principles and aiding rapid experimental condition analysis. Users are immersed in a 3D representation of the research laboratory, allowing interaction with the environment, understanding thermographic setups, accessing instructional videos, and analysing results as graphs or animations. This serious game helps users determine the optimal scenario for a given problem, enhance thermography principle comprehension, and achieve results more swiftly than through real-world experimentation. This innovative approach bridges the gap between simulations and practical experiments, providing a more engaging and efficient learning experience in engineering education. It highlights the potential of integrating simulations, experiments, and virtual reality to improve understanding and efficiency in engineering.

This paper deals with the numerical modelling of non-destructive testing of composite parts using active thermography. This method has emerged as a new approach for performing non-destructive testing (NDT) on continuous carbon fibre reinforced thermoplastic polymer (CCFRTP) components, particularly in view of detecting porosity or delamination. In this context, our numerical model has been developed around references containing internal defects of various shapes and sizes. The first novelty lies in the fact that the heat source used in the experimental setup is modelled exhaustively to accurately model the radiation emitted by the lamp, as well as the convection and conduction around the bulb. A second novelty concerns the modelling of the CCFRTP making up the benchmark used. Indeed, its thermal properties vary as a function of the sample temperature. Therefore, the actual thermal properties have been experimentally measured and were later used in our model. The latter then captures the material dependency on temperature. The results obtained by our model proved to be in close agreement with the experimental results on real reference points, paving the way for future use of the model to optimise experimental configurations and, in particular, the heating parameters.

Additive manufacturing of advanced materials has become widespread, encompassing a range of materials including thermoplastics, metals, and ceramics. For the ceramics, the complete production process typically involves indirect additive manufacturing, where the green ceramic part undergoes debinding and sintering to achieve its final mechanical and thermal properties. To avoid unnecessary energy-intensive steps, it is crucial to assess the internal integrity of the ceramic in its green stage. This study aims to investigate the use of active thermography for defect detection. The approach is to examine detectability using two benchmarks: the first focuses on the detectability threshold, and the second on typical defects encountered in 3D printing. For the first benchmark, reflection and transmission modes are tested with and without a camera angle to minimize reflection. The second benchmark will then be assessed using the most effective configurations identified. All defects larger than 1.2 mm were detectable across the benchmarks. The method can successfully detect defects, with transmission mode being more suitable since it does not require a camera angle adjustment to avoid reflections. However, the method struggles to detect typical 3D-printing defects because the minimum defect size is 0.6 mm, which is the size of the nozzle.

This paper proposes a simulation-driven data processing framework that transforms multiphysics simulation outputs into interactive representations suitable for immersive scientific visualization and analysis. The proposed workflow uses Comsol Multiphysics 6.1 software for numerical simulation, Houdini FX for procedural preprocessing and spatial reconstruction, and Unreal Engine for real-time visualization and interaction with standalone Meta Quest headsets. Houdini imports point-based numerical datasets, reconstructs their spatial structure, and performs interpolation and filtering operations. It then exports an optimized representation that is compatible with real-time visualization. Unreal Engine then provides dynamic visualization, temporal navigation, temperature-scale adjustment, graphical analysis tools, and spatial selection of regions of interest. Active thermography is employed as a case study because it generates spatially distributed, time-dependent thermal fields that require the preservation of spatial and temporal information throughout the processing workflow. The results demonstrate the technical feasibility of connecting multiphysics simulations, procedural data processing, and standalone, immersive visualization within a modular workflow. Although evaluated here through active thermography, the architecture is extensible and provides a basis for future investigations with other point-based scientific simulation and experimental datasets.