The present work introduces a different data processing strategy, proposed in order to improve sub-surface defect detection on industrial composites; in addition, a resume of thermal data processing with most common algorithms in literature is presented and applied with new data. A deep comparison between the common absolute contrast, DAC, PCT, TSR and derivative methods and a new proposed contrast mapping procedure is implemented. Thermographic inspection was done in reflection mode on a Glass Fiber Reinforced Plastic plate, with flat bottom hole defects. Thermal data computation method is found to be critical for simultaneous defect detection and automatic mapping, optimized to identify defect boundaries at specific depth, with help of accurate image processing, implemented in a Matlab GUI for a reliable and rapid characterization of internal damage. The new processing approach, the Local Boundary Contrast method, elaborates different contrast maps and facilitates recognition of damage extension. Tanimoto criterion and the signal-to-noise ratio method were applied as a criterion to assess defect detectability of various processing methods.

In this work, enhanced thermal data processing is developed with experimental procedures, improving visualization algorithm for sub-surface defect detection on industrial composites. These materials are prone to successful infrared nondestructive investigation analyses, since defects are easily characterized by temperature response under thermal pulses with reliable results. Better defect characterization is achieved analyzing data with refined processing and experimental procedures, providing detailed contrasts maps where defects are better distinguished. Thermal data are analyzed for different CFRP specimens with artificial defects and experimental procedures are verified on real structural aeronautical component with internal anomalies due to impact simulation. A better computation method is found to be useful for simultaneous defect detection by means of automatic mapping of absolute contrast, optimized to identify defect boundaries.

Composite sandwich structures are widely used for their mechanical properties combined to lightweight. However, damage area quantification caused by low velocity impacts represents generally a crucial task in sandwich composites. In the last years, recent advantages of thermographic devices offer new promising and different real-time industrial and engineering applications where lower computation time, accuracy of results and convenient cost are required. The present research deals with the comparison of standard or latest image-processing methods proposed for pulsed thermography regarding their suitability for determining the impact damage area in sandwich materials made of Aluminium core an a GFRP laminated skins. The Infra-Red processed results are compared with the advanced ultrasonic Phased array method commonly employed in the industrial Non-Destructive Testing. Specifically, the damage area quantification is performed by means of an appropriate MATLAB binarization algorithm for the post-processing of acquired thermal and ultrasonic maps. The data results verify the effectiveness of the image-processing thermographic technique combined to advanced processing approaches for the quantitative assessment of impact damage in sandwich component.

Additive manufacturing (AM) technologies enable the design and production of complex geometries, including structures engineered to achieve enhanced mechanical performance. Among these designs, there is growing interest in utilizing metal lattice structures, with specifically required properties, minimizing weight, for applications in both the mechanical and medical fields. The mechanical properties of such structures are strongly influenced by their shape and size. This study aims to investigate the influence of lattice topology, cell dimensions and beam diameter on the deformation and failure mechanisms of Ti6Al4V alloy lattice structures manufactured by EBM under compressive loading. A Design of Experiment (DOE) was adopted to define the unit cell geometry and dimensional parameters of the specimens. Sixteen topological configurations were obtained, and forty-eight lattice specimens (three replicas per configuration) were manufactured in random order to minimize process-related variability. Experimental results were analysed both in absolute terms and as a function of lattice density. In addition, a detailed optical microscopy analysis was performed to statistically evaluate beam diameters and actual cell dimensions. X-ray micro-computed tomography (micro-CT) was also employed to assess the presence of internal defects, such as porosity, which may affect the mechanical behavior.