This study used the finite element method(FEM) to investigate how pressure on the lumbar spine changes during dynamic movements in different postures: standing, erect sitting on a chair, slumped sitting on a chair, and sitting on the floor. Three load modes (flexion, lateral bending, and axial rotation) were applied to the FEM, simulating movements of the lumbar spine. Results showed no significant difference in pressure distribution on the annulus fiber and nucleus pulposus, representing intradiscal pressure, as well as on the cortical bone during movements between standing and erect sitting postures. However, both slumped sitting on a chair and sitting on the floor postures significantly increased pressure on the nucleus pulposus, annulus fibrosus, and cortical bone in all three movements when compared to standing or erect sitting on a chair. Notably, sitting on the floor resulted in even higher pressure on the nucleus pulposus and annulus fibers compared to slumped sitting on a chair. The decreased lumbar lordosis while sitting on the floor led to the highest increase in pressure on the annulus fiber and nucleus pulposus in the lumbar spine. In conclusion, maintaining an erect sitting position with increased lumbar lordosis during seated activities can effectively reduce intradiscal pressure and cortical bone stress associated with degenerative disc diseases and spinal deformities.

With the rise of electric vehicles, the use of battery modules, which are key units that drive vehicles, is increasing. The battery housing is the final form of a battery system mounted on electric vehicles, and is generally made of aluminum alloys, located at the bottom of the vehicle. The aluminum housing has a special shape to accommodate the battery module and is produced by welding extruded panels. This study applied friction stir welding (FSW) to weld 2.5 mm thin aluminum plates in order to improve the weldability and productivity. To increase productivity, we compared the mechanical properties after performing experiments under various FSW conditions. As a result, it was possible to derive speed-enabling welding conditions that can improve productivity without decreasing tensile strength. Deformation occurred in the structure during welding, causing gaps in the structure. Since these gaps have a significant influence on the degradation of mechanical properties after welding, the welding deformation at each step of welding must be calculated and reflected in the process. This study used the inherent strain method to calculate the deformation of each step of welding to apply automatic welding, and reduced the analysis time to 1/30 compared to the thermal elasto-plastic analysis method. Finally, this study verified the validity of the analysis method by comparing the experimental results with the numerical results using the inherent strain method.

The welding process, which accounts for about 60% of the shipbuilding process, inevitably involves weld deformation. Considering this, productivity can be significantly increased if weld deformation can be predicted during the design phase, taking into account the fabrication order. However, the conventional welding deformation prediction method using thermo-elasto-plastic analysis requires a long analysis time, and the welding deformation prediction method using equivalent load analysis has a disadvantage in that the welding residual stress cannot be considered. In this study, an inherent strain chart using a solid-spring model with two-dimensional constraints is proposed to predict the equivalent strain. In addition, the welding deformation prediction method proposed in this study, the equivalent strain method (ESM), was compared with the ship block experimental results (EXP), elasto-plastic analysis (EPA) results, and equivalent load analysis (ELM) results. Through this comparison, it was found that the application of the equivalent strain method made it possible to quickly and accurately predict weld deformation in consideration of the residual stress of the curved double-bottom block used in the shipyard.

Spinal–pelvic parameters are utilized in orthopedics for assessing patients’ curvature and body alignment in diagnosing, treating, and planning surgeries for spinal and pelvic disorders. Segmenting and autodetecting the whole spine from lateral radiographs is challenging. Recent efforts have employed deep learning techniques to automate the segmentation and analysis of whole-spine lateral radiographs. This study aims to develop an artificial intelligence (AI)-based deep learning approach for the automated segmentation, alignment, and measurement of spinal–pelvic parameters through whole-spine lateral radiographs. We conducted the study on 932 annotated images from various spinal pathologies. Using a deep learning (DL) model, anatomical landmarks of the cervical, thoracic, lumbar vertebrae, sacrum, and femoral head were automatically distinguished. The algorithm was designed to measure 13 radiographic alignment and spinal–pelvic parameters from the whole-spine lateral radiographs. Training data comprised 748 digital radiographic (DR) X-ray images, while 90 X-ray images were used for validation. Another set of 90 X-ray images served as the test set. Inter-rater reliability between orthopedic spine specialists, orthopedic residents, and the DL model was evaluated using the intraclass correlation coefficient (ICC). The segmentation accuracy for anatomical landmarks was within an acceptable range (median error: 1.7–4.1 mm). The inter-rater reliability between the proposed DL model and individual experts was fair to good for measurements of spinal curvature characteristics (all ICC values > 0.62). The developed DL model in this study demonstrated good levels of inter-rater reliability for predicting anatomical landmark positions and measuring radiographic alignment and spinal–pelvic parameters. Automated segmentation and analysis of whole-spine lateral radiographs using deep learning offers a promising tool to enhance accuracy and efficiency in orthopedic diagnostics and treatments.

The objective of this study was to develop an artificial neural network (ANN) model for predicting the tensile strength of friction stir welding (FSW) joints between dissimilar materials, with a particular focus on aluminum and copper, using cryogenic processes. The research addresses the challenges posed by differences in material properties and the complex nature of FSW, where traditional experimental methods are time-consuming and costly. FSW experiments were conducted under a variety of conditions, and the resulting temperature data were utilized as input for a heat transfer analysis. The maximum temperature and temperature gradient obtained from the analysis were employed as input variables for training the ANN. The ANN was optimized using the Hyperband tuner and validated against experimental results. The model successfully predicted tensile strength with an average error of 5.4%, demonstrating its potential for predicting mechanical properties under different welding conditions. This approach offers a more efficient and accurate method for optimizing FSW processes.

Intervertebral discs are fibrocartilage structures, which play a role in buffering the compression applied to the vertebral bodies evenly while permitting limited movements. According to several previous studies, degenerative changes in the intervertebral disc could be accelerated by factors, such as aging, the female sex, obesity, and smoking. As degenerative change progresses, the disc height could be reduced due to the dehydration of the nucleus pulposus. This study aimed to quantitatively analyze the pressure that each structure of the spine receives according to the change in the disc height and predict the physiological effect of disc height on the spine. We analyzed the biomechanical effect on spinal structures when the disc height was decreased using a finite-element method investigation of the lumbar spine. Using a 3D FE model, the degree and distribution of von-Mises stress according to the disc height change were measured by applying the load of four different motions to the lumbar spine. The height was changed by dividing the anterior and posterior parts of the disc, and analysis was performed in the following four motions: flexion, extension, lateral bending, and axial rotation. Except for a few circumstances, the stress applied to the structure generally increased as the disc height decreased. Such a phenomenon was more pronounced when the direction in which the force was concentrated coincided with the portion where the disc height decreased. This study demonstrated that the degree of stress applied to the spinal structure generally increases as the disc height decreases. The increase in stress was more prominent when the part where the disc height was decreased and the part where the moment was additionally applied coincided. Disc height reduction could accelerate degenerative changes in the spine. Therefore, eliminating the controllable risk factors that cause disc height reduction may be beneficial for spinal health.