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

Pulsed thermography (PT) is a widely used non-destructive testing (NDT) method for detecting defective regions in carbon fiber reinforced polymers (CFRP) structures. In order to improve the spatial and temporal resolution of thermographic data, thermographic signal reconstruction (TSR) is often adopted for data processing and analysis. However, TSR only performs data filtering along the time direction, while the spatial information is not exploited for noise reduction. In addition, TSR cannot handle the non-uniform backgrounds commonly existing in thermal images. To get around these problems, this paper extends the utilization of the penalized least squares methods to defect detection in CFRP structures. The experiment results show that, with the aid of penalized least squares, the defective regions in thermal images are characterized more clearly, while the signal-to-noise ratio (SNR) values are increased significantly.

Defect detection in CFRP structures using pulsed thermographic data enhanced by penalized least squares methods

Resin transfer molding (RTM) is a popular manufacturing method of composite materials, which have been widely used in many areas including aeronautic, automotive industries, etc. In RTM, permeability of fiber reinforcement varies with its geometric formation and affects the property of resin flow, which influences the final product quality. Therefore, effective estimation of permeability is crucial to achieving good process control and satisfactory quality product. In this article, a method of online estimating and monitoring local permeability is proposed. It can deal with variation in local permeability within preform caused by irregular arrangement of fibers among different regions. This study is divided into three stages. In the first stage, flow visualization was realized and all hardware was integrated to acquire real-time information in resin filling period. In the second stage, local pressure and flow front location were substituted into the Darcy's law, thus making online calculation of local permeability feasible. Then, in the third stage, the statistical process control charting technique was adopted to identify the changes in permeability. The proposed methods were used in trial RTM tests to compare their results to those from the reference method and to confirm their effectiveness. POLYM. COMPOS., 2014. © 2014 Society of Plastics Engineers

Online estimation and monitoring of local permeability in resin transfer molding

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.

Yu-Sung Chang

Papers

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

Defect detection in CFRP structures using pulsed thermographic data enhanced by penalized least squares methods

Online estimation and monitoring of local permeability in resin transfer molding

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

Thermographic clustering analysis for defect detection in CFRP structures