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关键词： Design for manufacturing   Manufacturability taxonomy   Sheet metal bending   Synthetic CAD dataset   Geometric deep learning  

摘要： Predicting the manufacturability of CAD designs early, in terms of both feasibility and required effort, is a key goal of Design for Manufacturing (DFM). Despite advances in deep learning for CAD and its widespread use in manufacturing process selection, learning-based approaches for predicting manufacturability within a specific process remain limited. Two key challenges limit progress: inconsistency across prior work in how manufacturability is defined and consequently in the associated learning targets, and a scarcity of suitable datasets. Existing labels vary significantly: they may reflect intrinsic design constraints or depend on specific manufacturing capabilities (such as available tools), and they range from discrete feasibility checks to continuous complexity measures. Furthermore, industrial datasets typically contain only manufacturable parts, offering little signal for infeasible cases, while existing synthetic datasets focus on simple geometries and subtractive processes. To address these gaps, we propose a taxonomy of manufacturability metrics along the axes of configuration dependence and measurement type, allowing clearer scoping of generalizability and learning objectives. Next, we introduce BenDFM, the first synthetic dataset for manufacturability assessment in sheet metal bending. BenDFM contains 20,000 parts, both manufacturable and unmanufacturable, generated with process-aware bending simulations, providing both folded and unfolded geometries and multiple manufacturability labels across the taxonomy, enabling systematic study of previously unexplored learning-based DFM challenges. We benchmark two state-of-the-art 3D learning architectures on BenDFM, showing that graph-based representations that capture relationships between part surfaces achieve better accuracy, and that predicting metrics that depend on specific manufacturing setups remains more challenging. Together, the taxonomy and BenDFM dataset provide a significant step toward deep-lea

关键词： adhesive interfaces   CAD/CAM inlays   deep margin elevation   marginal fit   resin composites   SEM   thermomechanical aging  

摘要： Objective This in vitro study evaluated the effects of composite material type, margin design (deep margin elevation vs. control), and thermomechanical aging on the marginal gap of CAD/CAM inlay restorations. Materials and Methods Standardized mesio-occluso-distal (MOD) inlay cavities were prepared in extracted human molars, with mesial margins located at the cemento-enamel junction (control) and distal margins extended 2 mm apical to the CEJ and elevated using three resin composite materials (bulk-fill, packable, and flowable). CAD/CAM inlays were fabricated and luted. Marginal gap values were measured using scanning electron microscopy at baseline and after thermomechanical aging simulating 1 year of clinical service. Data were analyzed using mixed repeated-measures ANOVA to evaluate the effects of material type, margin design, aging condition, and their interactions (alpha = 0.05). Results Thermomechanical aging significantly increased marginal gap values (p < 0.001). Margin design showed a significant effect (p = 0.015), whereas the main effect of material type was not statistically significant (p = 0.205). Significant interactions were observed between material type and margin design (p = 0.008) and among material type, margin design, and aging condition (p < 0.001). Conclusions Within the limitations of this current study, thermomechanical aging significantly increased marginal gap values of CAD/CAM inlays. Material type alone did not show a significant main effect on marginal gap. However, significant interaction effects were observed between material type and deep margin elevation, as well as between material type and aging condition.

关键词： Design management   engineering design   design workflow efficiency   optimized CAD-based manufacturing   training interventions   CAD formats' interoperability  

摘要： Effective CAD data management is crucial for engineering design productivity, yet challenges in file organization, interoperability, and workflow efficiency persist. While prior studies addressed technical aspects of PDM systems and file formats, the interaction between technical solutions and human factors remains underexplored. This study bridges this gap by examining the combined impact of structured training and file format standardization on CAD workflow performance, evaluating 120 professionals in control and experimental groups and assessing improvements in archiving, project categorization, security practices, and employing a QFD analysis to assess 35 CAD formats through expert testing, establishing performance hierarchies for manufacturing, AEC, and media applications. Key findings revealed a significant improvement after training, with large effect sizes across variables, reflecting meaningful enhancements in consistency, file retrieval, storage management, and security practices. Furthermore, based on expert ratings and normalized weighting, this study revealed a Pareto distribution where dwg, dxf, IGES, STEP, STL, and SVG collectively accounted for the majority of the weighted TIR, guiding format selection for specific industry applications. Manufacturing workflows benefited most from IGES and STEP formats, whereas AEC collaboration prioritized DWG and DXF. Media and entertainment pipelines relied on SVG, OBJ, and mb/.ma formats for texture and animation efficiency. These results show that human competency and technical interoperability in CAD data management must be developed together. By highlighting practical improvements in human-technical interactions and providing data-driven format hierarchies, the study offers actionable insights for workflow optimization, cross-platform collaboration, and adoption of interoperable CAD formats in professional settings, enhancing efficiency and standardized data management.

关键词： antithrombotic therapy   atrial fibrillation   coronary artery disease   edoxaban  

摘要： Background Paroxysmal atrial fibrillation (AF) is associated with a risk of stroke or systemic embolism similar to nonparoxysmal (persistent or permanent) AF. However, whether the optimal antithrombotic strategy in patients with AF and stable coronary artery disease differs by AF pattern remains *** This prespecified substudy of the EPIC-CAD (Edoxaban Antithrombotic Therapy for Atrial Fibrillation and Stable Coronary Artery Disease) trial compared edoxaban monotherapy with dual antithrombotic therapy (edoxaban plus a single antiplatelet agent) in patients with AF and stable coronary artery disease. Stratified analyses were performed by AF pattern. The primary outcome was net adverse clinical events, defined as a composite of all-cause death, myocardial infarction, stroke, systemic embolism, unplanned urgent revascularization, major bleeding, or clinically relevant nonmajor bleeding within 12 *** Among 1040 patients, 575 (55.3%) had paroxysmal and 465 (44.7%) had nonparoxysmal AF. The 12-month primary outcome rate was similar between paroxysmal and nonparoxysmal AF (10.3% versus 12.2%;adjusted hazard ratio [HR], 1.15 [95% CI, 0.79-1.68]). Edoxaban monotherapy was associated with a significantly lower risk of the primary outcome than dual antithrombotic therapy in both paroxysmal AF (4.6% versus 16.2%;adjusted HR, 0.23 [95% CI, 0.12-0.42]) and nonparoxysmal AF (9.3% versus 15.0%;adjusted HR, 0.56 [95% CI, 0.32-0.87]). No significant interaction was observed between antithrombotic strategy and AF pattern (P for interaction=0.10).Conclusions In patients with AF and stable coronary artery disease, edoxaban monotherapy significantly reduced net adverse clinical events at 12 months compared with dual antithrombotic therapy, irrespective of AF *** URL: https://***;Unique identifier: NCT03718559.

关键词： Adhesive dentistry   Clinical study   Dental bonding   Follow-up studies   Survival analysis  

摘要： Purpose: This study retrospectively investigated the clinical outcomes of computer-aided design/computer-aided manufacturing (CAD-CAM) resin composite crowns for molars and analyzed the morphological factors contributing to crown failure using three-dimensional (3D) digital data. Methods: The clinical outcomes of 117 crowns in 101 patients treated at Osaka University Dental Hospital were analyzed. 3D digital data were evaluated to identify factors influencing crown debonding, focusing on luting methods, abutment morphology, and crown parameters. Survival analyses, multivariate analyses, and regression modeling were performed. Results: During an observation period of up to 1281 days, the cumulative success and survival rates were 83.3% and 95.5%, respectively. Debonding (12.0%) was the most frequent complication and was significantly associated with the choice of luting materials (P < 0.001). In addition, 3D analysis identified greater buccolingual taper, insufficient occlusal thickness, smaller abutment surface area, and reduced abutment height as predictors of debonding. Nonlinear regression analysis revealed significant differences in the abutment parameters between SA Luting and PANAVIA V5 at the lower percentiles (P = 0.02). Conclusions: Combining clinical outcomes with 3D data highlights the importance of precise abutment preparation and material selection to reduce the risk of crown debonding.