Thermomechanics & Infrared Imaging, Inverse Problem Methodologies, Mechanics of Additive & Advanced Manufactured Materials, and Advancements in Optical Methods & Digital Image Correlation, Volume 4

This article describes the use of evanescent light fields to directly observe and detect the newly discovered coronavirus SARS-CoV-2 that causes COVID-19. The proposed technique provides a low-cost, fast, and highly accurate method of detection. This approach builds from previous work from the authors that enables microscopic observations of nano-objects with the accuracy of nanometers and sensitivities of the order of fraction of a nanometer.

Powders under compression form mesostructures of particle agglomerations in response to both inter- and intra-particle forces. The ability to computationally predict the resulting mesostructures with reasonable accuracy requires models that capture the distributions associated with particle size and shape, contact forces, and mechanical response during deformation and fracture. The following report presents experimental data obtained for the purpose of validating emerging mesostructures simulated by discrete element method and peridynamic approaches. A custom compression apparatus, suitable for integration with our micro-computed tomography (micro-CT) system, was used to collect 3-D scans of a bulk powder at discrete steps of increasing compression. Details of the apparatus and the microcrystalline cellulose particles, with a nearly spherical shape and mean particle size, are presented. Comparative simulations were performed with an initial arrangement of particles and particle shapes directly extracted from the validation experiment. The experimental volumetric reconstruction was segmented to extract the relative positions and shapes of individual particles in the ensemble, including internal voids in the case of the microcrystalline cellulose particles. These computationally determined particles were then compressed within the computational domain and the evolving mesostructures compared directly to those in the validation experiment. The ability of the computational models to simulate the experimental mesostructures and particle behavior at increasing compression is discussed.

This study investigates full-field, dynamic pressure reconstruction during shock-structure interactions using optical measurements and the virtual fields method (VFM). Shock wave impacts pose severe challenges to experimental measurement techniques due to the substantial, almost instantaneous pressure rises they induce. Their effects are typically measured pointwise using pressure transducers or as total force using load cells. Here, surface deformations were measured on the blind side of a flat steel plate in pure bending using a deflectometry setup. Pressure was reconstructed from the deformations induced by an impacting shock wave using a piecewise VFM approach. Different shock wave symmetries were used in order to investigate the capabilities of identifying spatial distributions reliably under the experimental conditions in the shock tube. Pointwise pressure transducer measurements allowed a validation of the results. It was found that different shapes of load distributions on the sample surface can be identified qualitatively, but that the comparability of both measurement techniques is limited due to filter and sampling capabilities.

It has already been showed that Thermoelastic Stress Analysis (TSA) may be employed as an extremely useful technique in structural integrity assessment applications. Recent developments in the microbolometer technology made possible the commercial offer of very low-cost infrared cameras, one of these being the FLIR Lepton 3.5. Use of such low-cost cameras associated with commercially available software or in-house developed algorithms makes possible to localize anomalies and to determine quantitative results on the stress distribution acting on nominal and hot-spot locations in loaded structures. A further step will widely disseminate IR temperature and TSA measurements and consequent analyses into a powerful low-cost health monitoring tool. The aim of the present work is to demonstrate the use of this experimental technique in the evaluation of the stress concentration caused by a U-notch in a plate under tension load using TSA and a Lepton camera. A MATLAB in-house algorithm was developed for post-processing the measured IR signal. The achieved stress-distribution and stress-concentration results are also compared with those generated by measurement systems that integrate a commercially available software coupled to the FLIR Lepton 3.5 and to a median-cost FLIR A655sc IR camera. Moreover, the paper shows that the low-cost camera can be used in the monitoring of fatigue crack growth as well as in the determination of stress intensity factors for cracks initiated and propagated from the U-notch.

This paper describes the evaluation of thermal deformation of the CFRP-aluminum fastening structure using stereo DIC (stereo digital image correlation). This structure is used at the FOB (Fixed Optical Bench) of the astronomical satellite “ASTRO-H.” The fastening structure is heated up with a self-made thermostat, and the in-plane displacements on the three-dimensional surface of it are measured using stereo DIC. Then, the strains are evaluated and the results are compared with those obtained by FEM. By comparing experimental results with those of finite element analysis, the finite element model is improved, then analysis of thermal deformation of the fastening structure is made more accurate and easily.

The strain-induced crystallization is generally considered to be responsible for the excellent properties of natural rubber, especially its remarkable crack growth resistance.

The crystallinity of rubber is classically studied by using X-ray diffraction (XRD). The XRD technique gives access to the crystallinity but also information of paramount importance on the crystalline phase structure (Rajkumar, et al., Macromolecules 39:7004, 2006), chain orientation (Toki, et al., Rubber Chemistry and Technology 42:956-964, 2004), kinetics of crystallization (Trabelsi, Macromolecules 36:9093–9099, 2003), non-exhaustively. However, this method provides this information at one point.

Recently, a new method has been proposed in (Le Cam, Strain 5:54, 2003) for determining the crystallinity, from infrared thermography-based surface calorimetry. In the case of heterogeneous heat source field and large deformations, the method requires combining digital image correlation and infrared thermography. In the present study, this methodology is applied to measure full heat source field in a stretched rubber specimen. The crystallinity as well as its spatial distribution is characterized.

The artery vessel simulator mimics the pulse using a membrane pump to drive water flow through the PDMS phantom intermittently to simulate the blood flows in the artery vessel. The DIC method performs the evaluation, where the images used for DIC calculation are captured by a single-camera stereo-imaging system, which can eliminate the time synchronization issue. The fluorescent medicine FITC is used to generate the random patterns on the surface of a PDMS phantom, where the PDMS phantom is part of the artery vessel simulator. The DIC method determines the displacement and strain field introduced by the interaction between water and the PDMS phantom to know the mechanical behavior of the simulator. In this study, two different imaging modes are implemented to see the time various mechanical behavior of water-PDMS phantom interaction and the simulator’s stability. Some challenges for using FITC to the PDMS would be highlighted at the end of this paper.

Avoiding or eliminating thermal abnormalities in powder bed fusion (PBF) is critical since the abnormalities can lead to a higher failure rate of printing complex parts, a longer manufacturing lead time, and/or additional post-processing. Controlling the thermal evolution of the process can hinder or minimize some of the most frequently encountered thermal abnormalities. To achieve such an objective, the prediction and control of temperature distribution throughout an exposure layer is a crucial step. The generation of uniform temperature distribution throughout the printed layers and the avoidance of overheated zones are two primary sub-objectives for controlling the thermal evolution of the process. However, the complex and non-linear nature of the process has limited the ability to derive a universal analytical equation to correlate the process parameters with the thermal distribution of a printed layer. Laser specifications such as laser power and scanning speed are among the main process parameters that predominantly govern the temperature distribution throughout the layer. In this paper, we employ an artificial neural network (ANN) to correlate laser power with the temperature of the printed area around the melt pool in Inconel 718. In our first variant, we investigate the effectiveness of using the multilayer perceptron Radial Basis Neural Network (RBNN) to model the function for predicting the temperature distribution for various laser power. We use the Rosenthal equation to generate adequate inputs-outputs for training our function. We then compare the output with the simulation results for five different laser powers. The results show that the function was trained successfully with a low mean square root error of 9.7157 using 2000 samples, a wider gap exists between the trained function and the simulated data. In the second variant, we use a recurrent neural network (RNN), which enables temporal histories to be used for training. To fulfill such objective, we acquire real thermal data using a photon-counting IR camera for different printed layers. This step allows the training of a function to predict the temperature distribution precisely for different laser power and thermal history. As future work, we will employ the function to adjust the laser power to minimize the overheated zones and distribute the temperature uniformly throughout each exposure layer.

This work presents an experimental characterization of the mechanical and thermomechanical properties of a soft 3D printed thermoplastic elastomer. The tested tensile specimens were obtained by Fused Deposition Modeling with a modified commercial 3D printer. Three different deposit angles with respect to the tensile loading (deposit angle of 0°, 45°, and ± 45°) have been tested. The specimens were tested under several uniaxial tensile loadings in order to investigate both the effect of increasing strain levels from one cycle to another and the loading rates. For both tests, the full temperature field was measured by means of infrared thermography and the full kinematic field was determined with the Digital Image Correlation (DIC) technique. Results provide information on the importance of the printing strategy effect on the mechanical response, the thermal response, as well as the specimen failure.

Inconel 713LC is a Ni-based superalloy, which is known as a non-weldable alloy, subjected to severe solidification cracking during the LPBF process. In this study, a systematic optimization method was constructed to find the optimal parameter region of IN713LC in LPBF additive manufacturing. The optimization method combines the perspectives of reducing the micro-cracks and the pores in melt-pool; therefore, the optimal region of the processing map can provide workpieces with less crack and high density. The specimens were fabricated with various fabrication parameters, leading to different melt-pool sizes, shapes, and different crack density. The corresponding results shows a trend that larger the mushy zone would result in the higher susceptibility of micro-cracking. As a result, the crack density of the specimens in the optimal parameter region has the lowest crack and the high relative densiy. It is found that in the result of the tensile test, LPBF processed IN713LC specimen shows excellent mechanical properties than that in casting.

In the last few decades, the shape memory polymers have gained growing interest and relevance in many application fields, which include textiles, aerospace, biomedical devices, etc. SMP are materials with stimuli-sensitive switches that are able to change their geometry from a primary shape to a secondary shape—and vice versa—in response to external stimuli. This phenomenon is called shape memory effect and, generally, is triggered by heat.

The heat stimulation, in fact, alters the internal structure of the polymer: by exceeding the glass transition temperature T_g, it is possible to program the shape of the component. Then, by cooling the material below T_g and imposing a fixed deformation, the polymer can reach its temporary shape. The original shape can be recovered by heating again the material above T_g without any constraints.

Recent research works are mostly focused on thermomechanical uniaxial characterization of these materials, aimed to obtain the material main memory effect parameters (namely the Young modulus above/under glass transition temperature and shape fixed/recovery ratio). In this work the authors propose a non-conventional characterization approach for the investigation of SMP behaviour under equi-biaxial stress state. In particular, the main idea is to carry out the hydraulic bulge test with a thermomechanical cycle on thin sheets of thermoplastic polyurethane shape memory polymer (TPU-SMP).

Full-field measurements on the specimen surface are used for retrieving the material shape, this latter by employing the Digital Image Correlation (DIC) technique. The proposed configuration makes the test suitable for determining the biaxial-stress strain curve and the thermomechanical cycle, also providing data for inverse calibration methods such as the Virtual Fields Method (VFM) and the Finite Element Model Updating (FEMU).

Additive manufacturing (AM) is undoubtedly the fastest-growing technology in the field of component productions. In particular, metal AM is rapidly emerging thanks to its enormous potentiality in manufacturing components with complex shapes and high structural performances. Although the precision and the rapidity of the production process is continuously improving, the performance of the final components in terms of crashworthiness and mechanical properties is still under evaluation. In this work, several specimens were manufactured using metal AM, in particular the Selective Laser Melting (SLM) method was employed. The used material is steel. All specimens have the same dimensions, but they were created using different paths of the laser source with respect to the metal powder layers during the AM process. Afterwards, the specimens were subjected to tensile test and the deformation field was measured using Digital image Correlation. The post-necking behavior as well as the anisotropy were evaluated using the Virtual Fields Method. It turned out that the laser paths used during the forming process have an impact on the plastic flow at large deformation up to the final fracture. The variance of the mechanical properties and the experimental uncertainties are discussed thoroughly.

Fatigue damage is associated with heat release leading to material self-heating. The present study deals with the measurement of mechanical dissipation from temperature measurements by infrared (IR) thermography during a fatigue test with varying stress amplitude. The experiment was performed in two steps on an additively manufactured steel specimen. First, specific acquisition conditions of the thermal response were used to remove the cyclic fluctuation due to thermoelastic coupling. Second, heat source reconstruction was applied to evaluate the calorific origin of the self-heating during the test. A simplified version of the heat diffusion equation was used assuming a uniform distribution of mechanical dissipation within the specimen. The relationship between stress amplitude and mechanical dissipation was identified without the need for a steady thermal regime at constant load amplitude.

Infrared thermography was used to study the thermal signature of a rubber-like granular material subjected to cyclic confined compression. The discrete medium consisted of cylinders placed in parallel. A fluctuation of the temperature at the same frequency as the mechanical loading was observed, as well as a global self-heating as the cycles progressed. This can be associated with the thermoelastic coupling effect and the mechanical dissipation effect, respectively. The thermoelastic coupling effect is visible in all contact zones between the particles, which can be explained by the stress concentrations that occur there. A strong mechanical dissipation effect occurs at specific contacts, which can be explained by the high friction between certain particles.

The first objective of this presentation is to show that it is possible to explain the cause of the pattern-induced bias (PIB) observed in displacement fields obtained by local DIC. A model is presented for this purpose. It gathers the different errors made when retrieving this displacement by minimizing the optical residual over subsets. It is shown that PIB predicted with this model and its counterpart observed with displacement fields obtained with DIC are in good agreement. When DIC is applied on periodic patterns like checkerboards instead of random speckles, it is observed that PIB becomes negligible. Such regular patterns are however not well suited for DIC. Hence it is recalled how to process such images by minimizing the optical residual in the Fourier domain instead of the spatial one. PIB is assessed in this case, and it is also observed that PIB is negligible in displacement maps obtained with such regular patterns processed by minimizing the optical residual in the Fourier domain.