Additive manufacturing (AM) is gaining relevance for the freedom it gives to designers in experimenting topologically optimized components, especially those having lattice morphology. Indeed, these are of great interest in various application fields (automotive, biomedical, etc.) because, in addition to a significant mass reduction, lattice topology (micro-scale) can be tuned to provide the final product (macro-scale) with the specific properties it needs to exhibit. However, additive manufactured lattice structures are still to be fully investigated, given the mismatch between the designed and the manufactured final product. This paper presents some preliminary results from a multi-instrument approach, grounding on non-contact measurement techniques, to characterize lattice and trabecular structures in terms of dimensional accuracy, surface morphology, stress-strain distribution and modal behavior.

Water-level monitoring systems are fundamental for flood warnings, disaster risk assessment and the periodical analysis of the state of reservoirs. Many advantages can be obtained by performing such investigations without the need for field measurements. In this paper, a specific method for the evaluation of the water level was developed using photogrammetry that is derived from images that were recorded by unmanned aerial vehicles (UAVs). A dense point cloud was retrieved and the plane that better fits the river water surface was found by the use of the random sample consensus (RANSAC) method. A reference point of a known altitude within the image was then exploited in order to compute the distance between it and the fitted plane, in order to monitor the altitude of the free surface of the river. This paper further aims to perform a critical analysis of the sensitivity of these photogrammetric techniques for river water level determination, starting from the effects that are highlighted by the state of the art, such as random noise that is related to the image data quality, reflections and process parameters. In this work, the influences of the plane depth and number of iterations have been investigated, showing that in correspondence to the optimal plane depth (0.5 m) the error is not affected by the number of iterations.

Despite its capability of measuring out of plane displacement gradients in the sub-micrometer range, and the possibility of successfully detecting defects (delaminations, debondings, impacts) without compromising the investigated structure (which is very important in the aerospace field), Shearography usually remains a qualitative technique. This is due to several uncertainty sources and the difficulty of managing its output images in order to extract information about the defect. The proposed methodology aims to reduce uncertainty on shear estimation, calculating the local shearing amount over a defect, and to quantify the defect size and shape, scanning its unwrapped phase-map using a wavelet transform algorithm. A PVC specimen with known defects was used to estimate the accuracy of the methodology, and a carbon fiber composite panel for aeronautical use was exploited to test the real defect reconstruction.

Additive manufacturing (AM) is a production process able to create complex structures with trabecular morphology that are of great potential interest in various application areas, such as aerospace and biomedical. In addition to a significant mass reduction, structures with trabecular topology (micro-scale) can be optimized and functionalized depending on the specific properties that the final product (macro-scale) needs to exhibit. The actual structural response of this new class of materials is, however, little known. This paper presents some first results obtained within the framework of a research project of national interest (PRIN) started in 2017, which involves some leading Universities in Italy. The project aims to investigate additive manufactured trabecular structures by developing proper testing protocols based on different non-contact measurement techniques, using a multi-instrumental and multi-competence approach.

Speckle shearography is often used in the aerospace industry to detect defects in composite materials because it allows noncontact, full field, and fast measurements. However, despite this advantage, this nondestructive technique is still not widely established, due to its limitations in performing defect quantitative estimations. The main issue lies in the extrapolation of phase profile boundaries, which represent defect edges. In this paper, this was achieved by studying the multimodal distribution of a characteristic parameter, called structural intensity, which is the probability of finding a denser population of local maxima of wavelet coefficients at a given position and adding an adaptive threshold selection in the structural intensity distribution. Once the damage boundaries were detected, the defect dimensions were computed by subtracting the local shearing amount, which was defined by a circle detection code, from each coordinate in both Cartesian directions. The paper shows an improvement to the algorithms able to detect defect area and perform morphological estimation previously developed by the authors and an in-depth study of the parameters involved in these algorithms. This resulted in both a high accuracy (0.04%) revealed by the calibration process on a PVC test panel and a close morphological agreement with the ultrasound reconstruction of unknown defects in composite materials. Thanks to this improvement, shearography becomes a quantitative inspection technique that takes few minutes compared to ultrasound inspection of the same area, which may require up to an entire day.