Active infrared thermography is a fast and accurate non-destructive evaluation technique that is of particular relevance to the aerospace industry for the inspection of aircraft and helicopters’ primary and secondary structures, aero-engine parts, spacecraft components and its subsystems. This review provides an exhaustive summary of most recent active thermographic methods used for aerospace applications according to their physical principle and thermal excitation sources. Besides traditional optically stimulated thermography, which uses external optical radiation such as flashes, heaters and laser systems, novel hybrid thermographic techniques are also investigated. These include ultrasonic stimulated thermography, which uses ultrasonic waves and the local damage resonance effect to enhance the reliability and sensitivity to micro-cracks, eddy current stimulated thermography, which uses cost-effective eddy current excitation to generate induction heating, and microwave thermography, which uses electromagnetic radiation at the microwave frequency bands to provide rapid detection of cracks and delamination. All these techniques are here analysed and numerous examples are provided for different damage scenarios and aerospace components in order to identify the strength and limitations of each thermographic technique. Moreover, alternative strategies to current external thermal excitation sources, here named as material-based thermography methods, are examined in this paper. These novel thermographic techniques rely on thermoresistive internal heating and offer a fast, low power, accurate and reliable assessment of damage in aerospace composites.

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Recent Advances in Active Infrared Thermography for Non-Destructive Testing of Aerospace Components.

Optically and non-optically excited thermography for composites: A review

Introduction Motivation and Contents Preliminaries Polymer Matrices for Composites Fibers Classification General Approach to Modeling Organization of the Book Overview of Manufacturing Processes Background Classification Based on Dominant Flow Process Short Fiber Suspension Manufacturing Methods Advanced Thermoplastic Manufacturing Methods Advanced Thermoset Composite Manufacturing Methods Transport Equations for Composite Processing Introduction to Process Models Conservation of Mass (Continuity Equation) Conservation of Momentum (Equation of Motion) Stress-Strain Rate Relationship Examples to Solve Viscous Flow Problems Conservation of Energy Constitutive Laws and Their Characterization Resin Viscosity Viscosity of Aligned Fiber Thermoplastic Laminates Suspension Viscosity Reaction Kinetics Thermoplastic Reactive Processing Crystallization Kinetics Permeability Fiber Stress Model Simplifications and Solution Formulation of Models Model and Geometry Simplifications Dimensionless Analysis and Dimensionless Numbers Customary Assumptions in Polymer Composite Processing Boundary Conditions for Flow Analysis Convection of Variables Process Models from Simplified Geometries Mathematical Tools for Simplification Solution Methods Numerical Methods Validation Short Fiber Composites Compression Molding Extrusion Injection Molding Exercises Advanced Thermoplastic Manufacturing Processes Composite Sheet Forming Processes Pultrusion Thermal Model Online Consolidation of Thermoplastics Processing Advanced Thermoset Fiber Composites Autoclave Molding Liquid Composite Molding Filament Winding of Thermosetting Matrix Composites Summary and Outlook MATLAB Files Solution to Example 8.13 using FDM Additional Examples with LIMS to Model Liquid Mold Filling Bibliography Index

Process Modeling in Composites Manufacturing

An overview of corrosion defect characterization using active infrared thermography

Most common types of defects for composite are debond and delamination. It is difficult to detect the inner defects on a complex shaped specimen by using conventional optical thermography nondestructive testing (NDT) methods. In this paper, a hybrid of spatial and temporal deep learning architecture for automatic thermography defects detection is proposed. The integration of cross network learning strategy has the capability to significantly minimize the uneven illumination and enhance the detection rate. The probability of detection (POD) has been derived to measure the detection results and this is coupled with comparison studies to verify the efficacy of the proposed method. The results show that visual geometry group-Unet (VGG-Unet) cross learning structure can significantly improve the contrast between the defective and non-defective regions. In addition, investigation of different feature extraction methods in which embedded in deep learning is conducted to optimize the learning structure. To investigate the efficacy and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped carbon fiber reinforced polymer (CFRP) specimens.

https://northumbria-test.eprints-hosting.org/id/document/270070

Temporal and spatial deep learning network for infrared thermal defect detection

Improved non-destructive testing of carbon fiber reinforced polymer (CFRP) composites using pulsed thermograph

Carbon fiber reinforced polymer (CFRP) composites have been widely used in aerospace, automotive and other industries, owing to their high strength and stiffness combined with low density. Thereby, there is an increased demand for defect detection in CFRP. Due to the high cost of carbon fiber, the non-destructive testing (NDT) techniques for assessment of CFPR structures have become an important research field. Pulsed thermography (PT) is a popular NDT technique for the sake of its convenient deployment and rapid detection ability, which visualizes the defects inside the CFRP specimens with thermal images. However, the thermographic data are often contaminated by measurement noise and non-uniform backgrounds, making it difficult to accurately identify defect regions. In this paper, a nonparametric signal decomposition method named multi-dimensional ensemble empirical mode decomposition (MEEMD) is utilized to decompose each thermal image into three parts, i.e. the high-frequency noise, the low-frequency backgrounds, and the signals informative for defect detection. In doing so, the noise and the non-uniform backgrounds can be removed from the thermographic data at one time, improving the detection results. The effectiveness of the proposed method is illustrated with experiments through the comparison with the existing methods.

Non-destructive testing of CFRP using pulsed thermography and multi-dimensional ensemble empirical mode decomposition

Thermographic clustering analysis for defect detection in CFRP structures

Resin transfer molding (RTM) is a popular manufacturing technique that produces fiber reinforced polymer (FRP) composites. In this paper, a model-assisted flow front control system is developed based on real-time estimation of permeability/porosity ratio using the information acquired by a visualization system. In the proposed control system, a radial basis function (RBF) network meta-model is utilized to predict the position of the future flow front by inputting the injection pressure, the current position of flow front, and the estimated ratio. By conducting optimization based on the meta-model, the value of injection pressure to be implemented at each step is obtained. Moreover, a cascade control structure is established to further improve the control performance. Experiments show that the developed system successfully enhances the performance of flow front control in RTM. Especially, the cascade structure makes the control system robust to model mismatch.

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Kai-Hong Wang

Papers

Improved non-destructive testing of carbon fiber reinforced polymer (CFRP) composites using pulsed thermograph

Non-destructive testing of CFRP using pulsed thermography and multi-dimensional ensemble empirical mode decomposition

Thermographic clustering analysis for defect detection in CFRP structures

Model-Assisted Control of Flow Front in Resin Transfer Molding Based on Real-Time Estimation of Permeability/Porosity Ratio

Feature-selective clustering for ultrasonic-based automatic defect detection in FRP structures