A Deep Learning-Based Machine Vision System for Online Monitoring and Quality Evaluation During Multi-Layer Multi-Pass Welding.

Multi-layer multi-pass welding plays an important role in manufacturing industries such as nuclear power plants, pressure vessel manufacturing, and ship building. However, distortion or welding defects are still challenges; therefore, welding monitoring and quality control are essential tasks for the dynamic adjustment of execution during welding. The aim was to propose a machine vision system for monitoring and surface quality evaluation during multi-pass welding using a line scanner and infrared camera sensors.

Experimental Investigation of Delayed Fracture Initiation in Advanced High-Strength Steel Under Accelerated Bending.

Predicting bending fractures in advanced high-strength steel (AHSS) is challenging due to complex microstructural behaviors and strain rate dependencies, particularly in industrial forming processes. Current models and standards primarily focus on quasi-static tension or slow bending speeds, leaving a gap in understanding the accelerated failure of AHSS without necking. In this study, direct bending experiments were conducted on dual-phase, complex-phase, and martensitic AHSS grades under varying bending speeds and radii.

1.5 GPa Grade High-Strength Steel Sheet Flattening by Roll Gap Adjustment Considering Pattern Roll Effects.

This study analyzes a three-stage roll flattening process to improve the flatness of 1.5 GPa grade AHSS sheets. Unlike conventional leveler rolls, which mainly relieve residual stress through longitudinal tension-compression, the second roll has a sloped pattern to induce transverse deformation and redistribute local residual stresses.

U-Net-Based Deep Learning Hybrid Model: Research and Evaluation for Precise Prediction of Spinal Bone Density on Abdominal Radiographs.

Osteoporosis is a metabolic bone disorder characterized by the progressive loss of bone mass, which significantly increases the risk of fractures. While dual-energy X-ray absorptiometry is the standard technique for assessing bone mineral density, its use is limited in high-risk female populations. Additionally, quantitative computed tomography offers three-dimensional evaluations of bone mineral density but is costly and prone to motion artifacts.

Automated Measurements of Tooth Size and Arch Widths on Cone-Beam Computerized Tomography and Scan Images of Plaster Dental Models.

Measurements of tooth size for estimating inter-arch tooth size discrepancies and inter-tooth distances, essential for orthodontic diagnosis and treatment, are primarily done using traditional methods involving plaster models and calipers. These methods are time-consuming and labor-intensive, requiring multiple steps. With advances in cone-beam computerized tomography (CBCT) and intraoral scanning technology, these processes can now be automated through computer analyses.

Development of an Equivalent Analysis Model of PVB Laminated Glass for TRAM Crash Safety Analysis.

This study focuses on an equivalent model of Polyvinyl Butyral (PVB) laminated glass to simulate the Head Injury Criterion (HIC) when a pedestrian collides with a TRAM. To simulate the collision behavior that occurs when a pedestrian's head collides with PVB laminated glass, a comparison was made between the results of the widely used PLC model for PVB laminated glass modeling and an actual dynamic head impact test. The material properties of the tempered glass and PVB film used in the PLC and equivalent models were obtained via four-point bending tests and tensile tests, respectively.

Microstructure, Mechanical Properties and Corrosion Performance of Laser-Welded NiTi Shape Memory Alloy in Simulated Body Fluid.

Laser-welding is a promising technique for welding NiTi shape memory alloys with acceptable tensile strength and comparable corrosion performance for biomedical applications. The microstructural characteristics and localized corrosion behavior of NiTi alloys in a simulated body fluid (SBF) environment are evaluated. A microstructural examination indicated the presence of fine and equiaxed grains with a B2 austenite phase in the base metal (BM), while the weld metal (WM) had a coarse dendritic microstructure with intermetallic precipitates including TiNi and NiTi.

Contagious infection-free medical interaction system with machine vision controlled by remote hand gesture during an operation.

Background And Objective: Medical image visualization is a requirement in many types of surgery such as orthopaedic, spinal, thoracic procedures or tumour resection to eliminate risk such as "wrong level surgery". However, direct contact with physical devices such as mice or touch screens to control images is a challenge because of the potential risk of infection. To prevent the spread of infection in sterile environments, a contagious infection-free medical interaction system has been developed for manipulating medical images.

Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading.

Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels.

Novel Procedure for Automatic Registration between Cone-Beam Computed Tomography and Intraoral Scan Data Supported with 3D Segmentation.

In contemporary practice, intraoral scans and cone-beam computed tomography (CBCT) are widely adopted techniques for tooth localization and the acquisition of comprehensive three-dimensional models. Despite their utility, each dataset presents inherent merits and limitations, prompting the pursuit of an amalgamated solution for optimization. Thus, this research introduces a novel 3D registration approach aimed at harmonizing these distinct datasets to offer a holistic perspective.

Machine Learning-Based Measurement of Regional and Global Spinal Parameters Using the Concept of Incidence Angle of Inflection Points.

This study delves into the application of convolutional neural networks (CNNs) in evaluating spinal sagittal alignment, introducing the innovative concept of incidence angles of inflection points (IAIPs) as intuitive parameters to capture the interplay between pelvic and spinal alignment. Pioneering the fusion of IAIPs with machine learning for sagittal alignment analysis, this research scrutinized whole-spine lateral radiographs from hundreds of patients who visited a single institution, utilizing high-quality images for parameter assessments. Noteworthy findings revealed robust success rates for certain parameters, including pelvic and C2 incidence angles, but comparatively lower rates for sacral slope and L1 incidence.

Prediction of Hole Expansion Ratio for Advanced High-Strength Steel with Image Feature Analysis of Sheared Edge.

The punching process of AHSS induces edge cracks in successive process, limiting the application of AHSS for vehicle bodies. Controlling and predicting edge quality is substantially difficult due to the large variation in edge quality, die wear induced by high strength, and the complex effect of phase distribution. To overcome this challenge, a quality prediction model that considers the variation of the entire edge should be developed.

Crash Performance of Strength Gradient Tube Induced by Selective Laser Patterning.

This paper presents an investigation of the performance of a 22 MnB5 tube after local heat treatment according to a patterning shape under dynamic crash test conditions to propose the patterning shape with the best energy absorption efficiency. Numerical simulations support experimental results to validate the deformation mode during dynamic crash test as well as the strain distribution of the specimen. The helical patterning not only demonstrates the highest axial loading force and energy absorbance in both static and dynamic crash tests, but also can be easily fabricated in a short time.

Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models.

Background And Objectives: Analyzing three-dimensional cone beam computed tomography (CBCT) images has become an indispensable procedure for diagnosis and treatment planning of orthodontic patients. Artificial intelligence, especially deep-learning techniques for analyzing image data, shows great potential for medical and dental image analysis and diagnosis. To explore the feasibility of automating measurement of 13 geometric parameters from three-dimensional cone beam computed tomography images taken in natural head position (NHP), this study proposed a smart system that combined a facial profile analysis algorithm with deep-learning models.

Direct Writing of Functional Layer by Selective Laser Sintering of Nanoparticles for Emerging Applications: A Review.

Selective laser sintering of nanoparticles enables the direct and rapid formation of a functional layer even on heat-sensitive flexible and stretchable substrates, and is rising as a pioneering fabrication technology for future-oriented applications. To date, laser sintering has been successfully applied to various target nanomaterials including a wide range of metal and metal-oxide nanoparticles, and extensive investigation of relevant experimental schemes have not only reduced the minimum feature size but also have further expanded the scalability of the process. In the beginning, the selective laser sintering process was regarded as an alternative method to conventional manufacturing processes, but recent studies have shown that the unique characteristics of the laser-sintered layer may improve device performance or even enable novel functionalities which were not achievable using conventional fabrication techniques.

Artificial Neural Networks for Predicting Plastic Anisotropy of Sheet Metals Based on Indentation Test.

This paper mainly proposes two kinds of artificial neural network (ANN) models for predicting the plastic anisotropy properties of sheet metal using spherical indentation test, which minimizes measurement time, costs, and simplifies the process of obtaining the anisotropy properties than the conventional tensile test. The proposed ANN models for predicting anisotropic properties can replace the traditional complex dimensionless analysis. Moreover, this paper is not limited to the prediction of yield strength anisotropy but also further accurately predicts the Lankford coefficient in different orientations.

Numerical Simulation of the Effects of Scanning Strategies on the Aluminum Evaporation of Titanium Alloy in the Electron Beam Cold Hearth Melting Process.

In the production of titanium alloy, the electron beam cold hearth melting (EBCHM) process is commonly used due to its effectiveness and the high quality of the end product. However, its main drawback is the significant loss of elements such as aluminum (Al) due to evaporation under the vacuum environment. Numerical coupled thermal-flow models were here developed to investigate the effects of scanning strategies on Al loss in a titanium alloy during EBCHM.

Intelligent Evaluation of Global Spinal Alignment by a Decentralized Convolutional Neural Network.

Degenerative changes of the spine can cause spinal misalignment, with part of the spine arching beyond normal limits or moving in an incorrect direction, potentially resulting in back pain and significantly limiting a person's mobility. The most important parameters related to spinal misalignment include pelvic incidence, pelvic tilt, lumbar lordosis, thoracic kyphosis, and cervical lordosis. As a general rule, alignment of the spine for diagnosis and surgical treatment is estimated based on geometrical parameters measured manually by experienced doctors.

Integrated Machine Vision System for Evaluating Hole Expansion Ratio of Advanced High-Strength Steels.

In this paper, we propose a new method to estimate the hole expansion ratio (HER) using an integrated analysis system. To precisely measure the HER, three kinds of analysis methods (computer vision, punch load, and acoustic emission) were utilized to detect edge cracks during a hole expansion test. Cracks can be recognized by employing both computer vision and a punch load analysis system to determine the moment of crack initiation.

A Contemporary Review of Aluminium MMC Developed through Stir-Casting Route.

The growing demand for composite materials with improved properties is attracting a lot of attention from industries such as automotive, aerospace, military, aviation, and other manufacturing. Aluminium metal matrix composites (AMMCs), with various reinforcements such as continuous/discontinuous fibers, whiskers, and particulates, have captured the attention due to their superior tribological, mechanical, and microstructural characteristics as compared to bare Al alloy. AMMCs have undergone extensive research and development with different reinforcements in order to obtain the materials with the desired characteristics.

Dissimilar Materials Welding with a Standoff-Free Vaporizing Foil Actuator between TRIP 1180 Steel Sheets and AA5052 Alloy.

This paper mainly demonstrates an advanced type of the vaporizing foil actuator welding (VFAW) process between GPa-grade steel (TRIP1180) and aluminum alloy (AA5052-H32) without applying standoff. To secure a flying distance during the VFAW process, the preformed target sheet shaped like a circular indentation has been utilized. It is necessary to optimize process parameters integrated with geometrical design of the preform since the welding strength can be decreased beyond the optimum input energy in the standoff-free VFAW process.

Detailed Inspection of -ray, Fast and Thermal Neutrons Shielding Competence of Calcium Oxide or Strontium Oxide Comprising Bismuth Borate Glasses.

For both the BO-BiO-CaO and BO-BiO-SrO glass systems, -ray and neutron attenuation qualities were evaluated. Utilizing the Phy-X/PSD program, within the 0.015-15 MeV energy range, linear attenuation coefficients (µ) and mass attenuation coefficients (μ/ρ) were calculated, and the attained μ/ρ quantities match well with respective simulation results computed by MCNPX, Geant4, and Penelope codes.

A novel approach for evaluating bone mineral density of hips based on Sobel gradient-based map of radiographs utilizing convolutional neural network.

Osteoporosis, which is a common disorder associated with low bone mineral density (BMD), is one of the primary reasons for hip fracture. It not only limits mobility, but also makes the patient suffer from pain. Unlike traditional methods, which require both expensive equipment and long scanning times, this study aims to develop a novel technique employing a convolutional neural network (CNN) directly on radiographs of the hips to evaluate BMD.