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. We newly construct an FE spherical indentation model, which is suitable for sheet metal in consideration of actual compliance. To obtain a large dataset for training the ANN, the constructed FE model is utilized to simulate pure and alloyed engineering metals with one thousand elastoplastic parameter conditions. We suggest the specific variables of the residual indentation mark as input parameters, also with the indentation load–depth curve. The profile of the residual indentation, including the height and length in different orientations, are used to analyze the anisotropic properties of the material. Experimental validations have been conducted with three different sheet alloys, TRIP1180 steel, zinc alloy, and aluminum alloy 6063-T6, comparing the proposed ANN model and the uniaxial tensile test. In addition, machine vision was used to efficiently analyze the residual indentation marks and automatically measure the indentation profiles in different orientations. The proposed ANN model exhibits remarkable performance in the prediction of the flow curves and Lankford coefficient of different orientations.

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. In this study, the image of the entire edge was analyzed to provide a comprehensive evaluation of its quality. Statistical features were extracted from the edge images to represent the edge quality of DP780, DP980, and MART1500 steels. Combined with punching monitoring signals, a prediction model for hole expansion ratio was developed under punch conditions of varying clearance, punch angle, and punch edge radius. It was found that the features of grayscale variation are affected by the punching conditions and are related to the double burnish and uneven burr, which degrades the edge quality. Prediction of HER was possible based on only edge image and monitoring signals, with the same performance as the prediction based solely on punching parameters and material properties. The prediction performance improved when using all the features.

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. However, the acoustic emission analysis system has difficulty detecting the instant of crack appearance since the magnitude of the audio signal is drowned out by noise from the press, which interrupts the differentiation of crack configuration. To enhance the accuracy for determining the HER, an integrated analysis system that combines computer vision with punch load analysis, and improves on the shortcomings of each analysis system, is newly suggested.

Achieving geometric accuracy in weatherstrip co-extrusion is inherently challenging due to the complex nonlinear interactions among process variables and the significant time delay caused by the long vulcanization line. However, conventional approaches primarily focus on managing process parameters or fail to integrate dimensional inspection into closed-loop feedback systems. Therefore, an integrated control framework is required to regulate product dimensions while simultaneously accounting for complex process variables. This study proposes a data-driven control strategy comprising two models to manage both process initialization and steady-state production. For the startup phase, a Random Forest-based prediction model was developed to determine optimal initial screw speeds, effectively minimizing trial-and-error adjustments. For steady-state operation, a smart vision-based feedback framework utilizing an artificial neural network was implemented to proactively compensate for dimensional deviations despite transport delays. Field validation in a mass-production environment confirmed that the system successfully maintained dimensional errors within a strict tolerance range of - 0.1 to + 0.2 mm. These results demonstrate the practical feasibility of this integrated approach for autonomous quality management in industrial weatherstrip co-extrusion.

Accurate prediction of startup screw speeds in rubber weatherstrip co-extrusion is challenging due to batch-dependent process variation, material variability, and target-specific geometric constraints. This study proposes a hybrid fusion framework that integrates an autoregressive integrated moving average with exogenous variables (ARIMAX) model and a physics-guided feature-based artificial neural network (PG-ANN) to predict startup screw speeds for the Ø120, Ø90, and Ø70 extruders. The temporal model captures batch-to-batch process variations, while the PG-ANN uses input features constructed from material rheological properties and cross-sectional geometry. A process-change-driven branch-selection mechanism determines which model output is used for each production batch. The framework was evaluated using industrial co-extrusion data collected from a mass-production environment. In the hold-out test, the best-performing model differed across targets: ARIMAX showed the highest R² and lowest root mean square error (RMSE) for Ø120, the hybrid model achieved the best performance across all evaluated metrics for Ø90, and the PG-ANN performed best for Ø70. Model transferability was further assessed by applying the A1-trained framework to the A2 product condition without refitting; the Ø120 target showed poor transfer performance, consistent with the weaker batch-to-batch correlation observed in A2. The limited number of independent production batches and changes in batch-to-batch relationships across products constrain model generalization. The proposed framework provides a rule-based mechanism for selecting between complementary prediction branches using measurable process changes.

This study aimed to evaluate the efficacy and safety of a novel flow-disruptor (NFD), designed to function as both a flow diverter and disruptor, in a rabbit aneurysm model. Elastase-induced aneurysms were created in 21 rabbits and treated with the NFD. Animals were randomly assigned to follow-up evaluations at 1 month (n = 7), 2 months (n = 7), and 3 months (n = 7). Angiographic and histological analyses were performed to assess aneurysm occlusion rates and neointimal formation. Immediate angiography demonstrated near-complete or complete flow disruption in 52% of aneurysms. Follow-up angiography revealed favorable aneurysm occlusion rates, reaching 76% (16 of 21 cases). Histologically, successful neointimal formation across the aneurysm neck was observed when the device was appropriately implanted in the aneurysmal sac. However, intentional device tip protrusion into the parent artery resulted in variable occlusion outcomes. The NFD demonstrated promising aneurysm occlusion rates and safety in a rabbit aneurysm model when appropriate wall apposition and tip embedding were achieved. Nevertheless, the intended protrusion design feature produced inconsistent effects due to anatomical limitations of the rabbit elastase-induced aneurysm model. Further studies with refined delivery systems, advanced imaging, and diverse aneurysm models are warranted to validate and optimize the clinical potential of the NFD.