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Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the ‘segmentation-stacking’ method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each image’s 90–99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99–1.00 [0.97–1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91–100%; middle cerebral arteries, 82–98%; anterior cerebral arteries, 88–100%; posterior cerebral arteries, 87–100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90–99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.

The material properties of heat resistant materials are known to be affected by thermal aging as operating time is accumulated at power plants. In this study, the influence of thermal aging on yield strength, ductility and J-Resistance behavior for Mod.9Cr-1Mo (ASME Grade 91) steel which is a material widely adopted for Generation IV nuclear energy system has been investigated. Service exposed Gr.91 steel materials sampled from a piping system of an ultrasupercritical (USC) plant in Korea with accumulated operation time of 73,716 hours were used for material testing. The test results of the service exposed material were compared with those of the virgin Gr.91 steel. Those test data were compared with the material properties of ASME code and RCC-MRx code that are international elevated temperature design codes. Conservatism of the material properties in design code has been quantified based on the comparison of virgin and service exposed materials.

Gas pipelines are exposed to a fatigue load related to both the ground and cars because of being buried at a depth of 1.5 m. Gas pipeline integrity is of considerable importance because earthquake-induced damage of gas pipelines can lead to gas leaks and fire, resulting in property damage and endangering lives. For these reasons, evaluation of the mechanical properties of gas pipelines must be performed. Therefore, in this study, the fatigue behavior of an API 5L X42 gas pipeline was estimated. ASTM standard specimens, curved plate specimens, and wall-thinning curved plate specimens were collected from the API 5L X42 gas pipeline, and fatigue testing was subsequently performed. Also, the S–N curves of each specimen were estimated, and then predicted by theoretical methods according to the depth of wall-thinning. Finally, it was verified that the predicted models were in good agreement with actual test data.

To evaluate the bending fatigue characteristics of contact wire, a stroke-based bending fatigue tester on contact wire has been developed by Kang et al. But this tester is too cumbersome and complicated. This paper is investigated with a new fatigue life estimation method based on Juvinall’s method for estimation of S-N curve, which is widely used in steel structure, in order to estimate the bending S-N curve of cold drawn contact wire. First, the ultimate tensile strength and the axial tension fatigue test was performed with standard specimens, and the bending fatigue life for the cold drawn contact wire structure was predicted by using an improved S-N curve prediction method. Finally, the tested bending fatigue life of the structure was compared with the fatigue life estimated by the new method. As the result, the predicted bending S-N curve of the contact wire agreed fairly well with the experimental bending fatigue lives using the S-N curve and ultimate tensile strength of cold drawn material.

Caulking has merits of easy manufacturing and assembly, and reduced product cost. However, though the design of the caulking process is very important in order to obtain satisfactory strength, research on caulking has been insufficient. Therefore, tensile and fatigue tests were conducted for the evaluation of the caulked part of a drum in a washing machine. The caulking process was simulated by finite element analysis, and the residual stress of the caulked part was evaluated. The residual stress of the caulked part was applied to the S-N curve of the fatigue test, and the notch effect of the caulked part was applied to the S-N curve of virgin material. As a result, the S-N curve of the caulked part can be predicted from the S-N curve of virgin materials.

Composite materials called CFRP (Carbon fiber reinforced plastics) are widely used in the aerospace industry and otherrelated industries like the automobile and in sports. Nowadays, CFRP composite materials are receiving much attention because of their superior mechanical properties compared to metals and their lightweight, which has contributed to the high fuel efficiency of structures. However, the compressive strength of composite materials is lower than its tensile strength. Structures like airplanes and automobile are assembled with components such as bolts, pins, etc. and the strength of these components, or notches, can degrade. In this study, the compressive strengths of notched flat specimens were compared with those of unnotched flat specimens through compression tests. Also, the compressive strengths of differently shaped structures were compared and verified with those of the specimens

Carbon fiber reinforced plastics (CFRPs), a composite material, are generally vulnerable to compressive load and impact damage in a direction perpendicular to the surface of the material. In particular, during the operation of an aircraft, impact damages caused by bird collisions or falling tools reduce the strength of the aircraft structure. In this study, after impact damages were applied to CFRP specimens with various impact energies and impactor diameters, the compressive residual strength of the impact-damaged specimen was evaluated by performing a compression test. Furthermore, a prediction model for the compressive residual strength is proposed according to the variation in the impact energy by comparing the test results.

Recently, the use of Carbon Fiber Reinforced Plastic (CFRP) composite, instead of metal materials, has increased in fields that have high demand for both lightweight and high specific strength materials, such as aircraft and automobile. Composites show different characteristics according to temperature and fiber array direction. Under the operating condition, the fuselage of an aircraft is often exposed to impacts from birds, hail etc. Therefore, Charpy impact fracture characteristics of the CFRP composite according temperature and fiber array direction need to be estimated to ensure aircraft safety. In this paper, after the Charpy impact tests, fractured surfaces were analyzed to estimate the impact fracture characteristics of the CFRP composite by SEM.

Carbon fiber reinforced plastic (CFRP) composites have high specific stiffness and high specific strength. Therefore, they are increasingly being use, instead of conventional metallic materials in the aviation and automobile industries, where there is a strong demand for lightweight materials. In aircraft, the fuselage is exposed to severe conditions of high temperatures and high humidity. Therefore, it is necessary to estimate the strength of CFRP composites under real conditions from the viewpoint of aircraft safety. In this study, CFRP specimens were immersed in distilled water at 75°C for a long time. Then, tensile tests were performed on these specimens, and the fracture characteristics of the fractured surfaces were analyzed using SEM. A fatigue test was performed on specimens immersed for 300 days with R=0.1, and it was confirmed that the fatigue life deteriorated in immersed specimens compared to specimens that were not immersed.

The effect of thermo-mechanical treatment (TMT) process on the strength and precipitation behavior of an 11%Cr-0.3Mo-1.6W steel during long term aging was investigated. The major precipitates in as-tempered specimen were identified as M23C6 (M=Fe,Cr) and MX (M=Ta,V;X=C,N). The M23C6 precipitate in TMT sample was more finely distributed due to the increased heterogeneous nucleation sites at dislocations. The tensile strength of TMT sample is higher than that of the conventional heat treatment (CHT) sample in as-tempered condition. However, Laves phase starts to precipitate additionally in the aged condition. The growth rate of Laves phase in TMT sample is much faster than that in CHT sample. Therefore, the difference in tensile strength between CHT and TMT sample decreases as the aging time increases.

Ferritic steels have been candidate structural materials for first wall and blanket structures of fusion power plant since the late 1970’s, when the fast-reactor irradiation showed them to be more swelling resistant than austenitic stainless steels. In this investigation, the coarsening of MX nitrides during aging was studied for Cr-Mo-N-X(X=V, Nb, Ti) ferritic steels. During the aging, (V, Nb, Ti)nitrides were precipitated out. From TEM observation, particle size distribution was confirmed and size distribution follows a typical log-normal distribution. The coarsening rate of MX nitrides was correlated with the Oswald ripening equation.

The segregation and diffusion of boron during heat treatments were studied. The influence of boron contents, aging time and applied stress on FeMo 2 B 2 formation was also studied. Finally, the effects of boron contents and FeMo 2 B 2 formation on the high temperature strength were studied. Boron atoms were segregated to prior austenite grain boundary during normalizing treatment. And these boron atoms were slowly diffused into the grain interior during tempering and aging at 700 °C. The FeMo 2 B 2 phase was only formed after 1,000 h aging at 700 °C in alloy containing 196 ppm boron. The formation of FeMo 2 B 2 phase is accelerated by the applied stress. It was expected that the formation of FeMo 2 B 2 is closely related to the redistribution of boron atoms. The tensile strengths at 700 °C are increased with the increase of boron contents. However, the formation of FeMo 2 B 2 phase results in lower tensile strength.