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Sewing is the most widely used and preferred method for manufacturing clothing products for extreme weather conditions and other industrial insulation systems. Multiple layers of functional fabrics in combination with insulation materials are used to thermally insulate precious body heat from its surrounding cold environment. The sewing process fixes the insulation material between the fabric layers. During conventional sewing, the insulation material is compressed along the stitch line. With the compression of the insulation material, entrapped air is forced to leave the insulation material internal structure, and heat loss occurs along the entire length of the stitch line. It results in the deterioration of thermal properties of the end product along the stitch line.

The amount of air, which is a decisive factor for thermal properties of any insulation system, was investigated at the level of a unit stitch length of a lockstitch. Conventional microscopy methods are not suitable to study the compression along the stitch line. With the help of X-ray tomography, the three-dimensional data of a stitch was taken and studied to measure the volume of air. The samples were prepared with conventional lockstitch sewing and a newly developed innovative sewing method “Spacer Stitching.” The results are compared with each other in terms of the amount of air present in a unit stitch length.

Calculations based on X-ray tomography images of lockstitch and spacer stitch revealed that, in the case of lockstitch, a unit stitch has a 15% of its volume made up of material and 85% of its volume by air. In comparison, the spacer stitch with the same sewing and fabric parameters has a material volume of 4.6 % and an air volume of 95.4% in a single stitch.

The research can positively improve the thermal properties of sewn material made for insulating purposes of conventional clothing as well as of industrial insulations.

There is no literature available which investigates and calculates the amount of air and material present along with a stitch line.

Orange peel formation remains to be understood clearly because it is difficult to directly observe a laser-sintered process in a partcake. Therefore, this study aims to provide insight into the orange peel formation mechanism through the nondestructive observation of laser-sintered specimens and their surrounding powders.

This study observed polyamide 12 powder in the vicinity of a laser-sintered specimen via X-ray computed tomography (CT) scanning. The specimen for nondestructive observation was 3D modeled in a hollow box using 3D CAD software. The boxes built using a laser-sintering system contained unsintered surrounding powder and sintered specimens. The box contents were preserved even after the boxes were removed from the partcake. After X-ray CT scanning, the authors broke the boxes and evaluated the unevenness formed on the specimen surface (i.e. the orange peel evaluation).

Voids (not those in sintered parts) generated in the powder in the vicinity of the specimen triggered the orange peel formation. Voids were less likely to form in the build with a 178.5° powder bed than in the build with a 173.5° powder bed. Similarly, the increment in laser energy density effectively suppressed void formation, although there was a tradeoff with overmelting. Thin-walled parts avoided void growth and made the orange peel less noticeable.

To the best of the authors’ knowledge, this study is the first to observe and understand the relationship between voids generated in the powder in the vicinity of sintered parts and orange peel formation.

This paper aims to present a methodology for the detection and categorisation of metal powder particles that are partially attached to additively manufactured lattice structures. It proposes a software algorithm to process micro computed tomography (µCT) image data, thereby providing a systematic and formal basis for the design and certification of powder bed fusion lattice structures, as is required for the certification of medical implants.

This paper details the design and development of a software algorithm for the analysis of µCT image data. The algorithm was designed to allow statistical probability of results based on key independent variables. Three data sets with a single unique parameter were input through the algorithm to allow for characterisation and analysis of like data sets.

This paper demonstrates the application of the proposed algorithm with three data sets, presenting a detailed visual rendering derived from the input image data, with the partially attached particles highlighted. Histograms for various geometric attributes are output, and a continuous trend between the three different data sets is highlighted based on the single unique parameter.

This paper presents a novel methodology for non-destructive algorithmic detection and categorisation of partially attached metal powder particles, of which no formal methods exist. This material is available to download as a part of a provided GitHub repository.

This paper aims to develop a method to measure the length of cracks inside solder joints, which enables the validation of computed tomography (CT) crack length measurements.

Cracks were formed inside solder joints intentionally by aging solder joints of 0603 size resistors with thermal shock (TS) test (−40 to +140°C, 2,000 cycles), and CT images were captured about them with different rotational increment (1/4, 1/2 and 1°) of sample projection. The length of cracks was also measured with our method, which is based on capturing high-resolution radiography X-ray images about the cracks in two perpendicular projection planes. The radiography results were compared to the CT measurements. The percentage error for the different CT rotational increment settings was calculated, and the optimal CT settings have been determined.

The results have proven that reducing the rotational increment increases the sharpness of the captured images and the accuracy of crack length measurements. Nevertheless, the accuracy compared to high-resolution radiography measurements is only slightly better at 1/4° rotational increment than in the case of 1/2° rotational increment. It should be also noted that the 1/4° increment requires twice as much time for capturing the images as the 1/2° increment. So, the 1/2° rotational increment of sample projection is the optimal setting in our investigated case for measuring crack lengths.

The developed method is applicable to find the optimal settings for CT crack length measurements, which provides faster analysation of large quantity samples used, e.g. at life-time tests.

There is a lack of information in the literature regarding the optimisation of CT measurement set-up, e.g. a slightly larger value of the sample rotational increment can provide acceptable resolution with much faster processing time. Thus, the authors developed a method and performed research about optimising CT measurement parameters.

This paper aims to investigate the effect of printing temperature on the thermal and the mechanical behaviour of polylactic acid (PLA)-polyhydroxyalkanoate (PHA) blend printed using fused deposition modelling (FDM).

Because of the use of an infra-red camera, thermal cycling during the laying down is quantified. In addition, X-ray micro-tomography is considered to reveal the microstructural arrangement within the three-dimensional printed material. Tensile loading conditions are used to derive Young’s modulus, tensile strength and fracture toughness, and relate these to the printing temperature. Finite element computation based on three-dimensional microstructure information is used to predict the role of defects on the tensile performance.

The results show a remarkable cohesive structure of PLA-PHA, particularly at 240°C. This cohesive structure is explained by the ability to ensure heat accumulation during laying down as evidenced by the nature of thermal cycling. The printing temperature is found to be a key factor for tuning the ductility of the printed PLA-PHA allowing full restoration of tensile strength at high printing temperature.

This study reports new results related to the thermo-mechanical behaviour of PLA-PHA that did not receive much attention in three-dimensional printing despite its potential as a candidate for pharmacological and medical applications. This study concludes by a wide range of possible printing temperatures for PLA-PHA and a remarkable low porosity generated by FDM.

This study aims to investigate the effect of the wall thickness, deposition orientation and two different post-processing methods on the local mechanical properties and microstructure of additively manufactured parts made of maraging steel. In order to examine the local properties of the build, miniaturized testing specimens were employed. Before application of small-sized specimens, their performance was verified.

The investigation was composed of two stages. As first, the part thickness, specimen size and orientation were studied on a laser-powder bed fusion (L-PBF) platform with deposited walls of various thicknesses made of maraging steel. Subsequently, the influence of different heat-treatment methods was investigated on the final product, i.e. impellers. The miniaturized and standard tensile tests were performed to investigate the local mechanical properties. The porosity, microstructures and fracture surfaces were analysed by X-ray-computed tomography, X-ray diffraction and scanning electron microscopy with electron backscatter diffraction.

The results revealed good agreement between the values provided by miniaturized and standard specimens. The thinnest parts produced had the largest pores and the highest scatter of elongation values. In these cases, also the sub-contour porosity was observed. Part thickness affected pores’ size and results repeatability but not total porosity. The two-step heat-treatment (solutionizing and age-hardening) exhibited the highest yield and ultimate tensile strength.

The microstructure and local mechanical properties were studied on L-PBF platform with deposited walls of various thicknesses. Subsequently, a detailed analysis was conducted on real components (impellers) made of maraging steel, commonly used in tooling, automotive and aerospace industries.

The broadly understood quality of manufactured parts is crucial for their reliable and long-lasting operation. The findings presented in the manuscript allow the readers better understanding of the connection between deposition parameters, post-processing, microstructure and mechanical performance of additive manufacturing-processed parts.

Gives a bibliographical review of the finite element methods (FEMs) applied in biomedicine from the theoretical as well as practical points of view. The bibliography at the end of the paper contains 748 references to papers, conference proceedings and theses/dissertations dealing with the finite element analyses and simulations in biomedicine that were published between 1985 and 1999.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.

The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

The research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis (FEA) model, it seeks to understand chemical and physical changes during aging processes. This research provides insights into nonlinear mechanical behavior, stress softening and microstructural alterations in SBR compounds, improving material performance and guiding future strategies.

This study combines experimental analyses, including cyclic tensile loading, attenuated total reflection (ATR), spectroscopy and energy-dispersive X-ray spectroscopy (EDS) line scans, to investigate the effects of thermo-mechanical aging (TMA) on carbon-black (CB) reinforced styrene-butadiene rubber (SBR). It employs a 3D FEA model using the Abaqus/Implicit code to comprehend the nonlinear behavior and stress softening response, offering a holistic understanding of aging processes and mechanical behavior under cyclic-loading.

This study reveals significant insights into SBR behavior during thermo-mechanical aging. Findings include surface roughness variations, chemical alterations and microstructural changes. Notably, a partial recovery of stiffness was observed as a function of CB volume fraction. The developed 3D FEA model accurately depicts nonlinear behavior, stress softening and strain fields around CB particles in unstressed states, predicting hysteresis and energy dissipation in aged SBRs.

This research offers novel insights by comprehensively investigating the impact of thermo-mechanical aging on CB-reinforced-SBR. The fusion of experimental techniques with FEA simulations reveals time-dependent mechanical behavior and microstructural changes in SBR materials. The model serves as a valuable tool for predicting material responses under various conditions, advancing the design and engineering of SBR-based products across industries.