The aim of the study was to develop a new rib attachment structure for the Nuss implant, in which the design process was supported by numerical methods, namely generative design and topology optimization. The study utilized 316LVM surgical steel, for which the mechanical properties were determined through tensile testing. The Autodesk Fusion and SolidWorks 2022 computational environments were used for shape optimization. Multiple geometric variants were obtained within the framework of generative design; however, their complexity and their reliance on additive manufacturing limit their practical implementation. In contrast, topology optimization reduced the mass by 17–30% while maintaining mechanical strength and manufacturability through conventional milling. Design modifications introduced through consultations with surgeons improved the ergonomics of implant installation. The results indicate that integrating both methods provides an effective tool for designing lightweight, durable, and functional Nuss implant attachments, opening up new prospects for personalized thoracic surgery.
The paper presents preliminary investigations into a strategy for solving linear systems resulting from 3D finite element simulations, based on the algebraic multigrid (AMG) method, enhanced using artificial intelligence techniques. In particular, we adapt to 3D problems the algorithm presented in Luz et al. (2020) for using a graph neural network to create the prolongation and restriction operators in a way that will improve convergence. The process of training the network proceeds on the basis of a set of system matrices obtained for tasks much smaller in scale than the target problem to be solved. Learning is aimed at decreasing the spectral radius of the error propagation matrix after applying modified prolongation and restriction. We describe some implementation details of the solver developed based on the presented strategy and show several numerical examples of its application for medium-sized problems.
Bipolar plates are critical core components in proton exchange membrane fuel cells (PEMFCs). Titanium-based materials are highly favored due to their excellent corrosion resistance and high specific strength. However, the plates often experience severe local thinning and poor consistency in forming dimensions during the two-stage stamping process. Although traditional finite element method (FEM) optimization can mitigate these defects, it comes with high computational costs and time consumption. This study proposes a die design optimization framework based on the Sequential Physics-Informed Neural Network (S-PINN). Unlike traditional single-layer neural network models, S-PINN adopts a sequential architecture that effectively maps the two-stage forming process of the plates. This architecture can explicitly predict the evolution of forming quality from the pre-forming stage to the final stage. By embedding the core physical laws of plastic deformation into the network loss function, the S-PINN model effectively predicts the complex nonlinear relationship between mold geometry and forming quality, while ensuring physical consistency. Experimental and simulation results show that the S-PINN model’s prediction accuracy for dimensional consistency (DC) is 73.8% higher than that of the PINN model and 33.9% higher than that of the S-ANN model. Compared with traditional modeling methods, the S-PINN-optimized die design can reduce the thinning rate and improve channel dimensional consistency.
This study investigates the sensitivity of factors influencing the contact resistance and reliability assessment of bolted copper–aluminum electrical connectors. Through continuous energized dynamic testing, the temporal evolution of the contact resistance and temperature of the contact area were monitored throughout the experiment. In conjunction with observations of contact surface morphology and microstructural composition analysis, the intrinsic mechanisms underlying anomalous changes in contact resistance were explored in depth. The results indicated that, during continuous current flow, the contact resistance of aluminum busbar assemblies without plating exhibited an “initially low → rapid increase → gradual stabilization” pattern, whereas nickel-plated aluminum assemblies maintained stable resistance. Quantitatively, the contact resistance of the nickel-plated group remained at approximately 15 μΩ, while that of the bare aluminum group rose to 400–750 μΩ. The evolution of the contact interface was asymmetric, with the dynamic growth of the oxide layer on the aluminum surface being the dominant mechanism increasing resistance. Moreover, the aluminum coating significantly reduced contact resistance and enhanced connection reliability.
In this study, Artificial Neural Networks (ANN) were created to develop a Digital Twin (DT) for temperature prediction in the laser hardening process of NC10 steel. The ANN were trained to predict temperature on the top layer during the laser hardening process of NC10 steel samples with different thicknesses and with various laser power and laser scanning speeds. The prediction developed during the project work was based on a parametric numerical model of the laser hardening process for a sample of NC 10 steel, using the Finite Element Method (FEM) within the ADINA software. Numerical simulations enabled a detailed analysis of the temperature produced on the surface of each sample, as well as a visualization of the structural changes made to the sample according to the laser hardening process. It is crucial to create data that reflects reality as closely as possible to assess the best setting for each process. A well created DT allows to make automatically important changes along laser hardening process. To obtain a set of the most efficient parameters for the desired result, Genetic Algorithms (GA) were integrated with the developed ANN. As a result, the authors developed an effective and efficient tool to predict the temperature produced along the laser hardening process.
