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To make the results of the autologous fat transfer more satisfactory, all parameters of suction, preparation, and reapplication must be well understood. Since syringe-assisted liposuction is frequently used for small-volume procedures, we quantified the influence of the cannula on negative pressure.
For these experiments, we used a digital manometer to which a syringe and one of seven different cannulas were connected. Data was collected and analyzed using LabVIEW and Matlab. The volume of our measurement setup was calculated using Boyle-Mariotte's law to ease interpretation, which revealed a volume of 2.82 ± 0.03 mL.
We demonstrate that the volume of the measurement setup significantly affects the measurement results. Consequently, we accounted for this factor and calculated the theoretical values accordingly. Statistical analysis shows a significant influence on negative pressure across different cannulas, except for two pairs, which had minimal volume differences. Exemplary, for cannulas with volumes under 1.75 mL, a 2 mL syringe plunger pull was sufficient to create a - 0.5 bar (- 14.8 inHg) negative pressure.
The present data indicate that the size of the suction cannula must be considered in syringe-assisted suction to ensure a specific negative pressure is not exceeded. Other research groups have demonstrated that adipocyte vitality and the amount of stromal vascular fraction are reduced by excessively strong negative pressure.
Additively processed materials are increasingly used to manufacture customized parts, e.g. medical implants. Implant surfaces often require a smooth finish, which can be achieved by post-processing and well-defined process parameters. In this study, the effects of electropolishing of metal parts produced by laser powder bed fusion are investigated using Hull cell experiments and a three-electrode setup. Current density voltage curves were measured with the three-electrode setup to identify the regimes for electropolishing. Subsequently different constant-currents were applied and Hull cell experiments were conducted. The surface roughness (Sz, Sa) and the mass removal were analysed. Surface morphologies were assessed using laser scanning and scanning electron microscopy. A reduction of the initial surface roughness of more than 90% to Sa < 0.3 μm has been achieved. Considering the passed electrical charge during electropolishing, results from Hull cell experiments are systematically correlated with current-controlled electropolishing. This approach enables the precise tailoring of polishing parameters to achieve surfaces with defined roughness. Furthermore, the study demonstrates the suitability of Hull cells in determining electropolishing parameters for additive materials and highlights their contribution to post-processing in additive manufacturing.
Creating simulation models and running experiments requires expertise and significant effort. Modelling complex production systems by using Petri Nets, which have a simple formalism, leads to considerable modelling effort. To reduce the workload required, fundamental processes within a production system must be analyzed and represented as generic Petri Net structures to enable their automatic generation. These structures can serve as modular building blocks to construct arbitrarily complex systems. The broader objective is to enable the automatic generation of such, based on process descriptions within a simulation framework. Built on these findings, the necessary information for developing a specialized formal process description is derived, enabling the automated generation of Petri Net simulation models for production systems.
This paper presents the workflows of Digital Twins in practice, with reference to three case studies. The objective is to present the processing of data and information flow. The most essential requirements for the design of a Digital Twin are synthesized from the case studies and a layer model is presented, which was developed based on the three case studies. The model consists of four functional layers and an overarching data- and workflow-management-backbone. A front-end layer is proposed for communication with the end user.
This paper presents the architecture and functionality of a simulation-based Digital Twin designed to support decision-making in the control of internal transport systems. The developed Digital Twin leverages a wide range of data from operational information systems to predict future system loads. Specific methods for forecasting transport orders have been developed. An integrated simulation framework enables analysis of potential control decisions, such as resource allocation or the timing of order releases. The simulation results are used for multi-criteria evaluation of the available options for preventive action, thus, providing users with concrete recommendations to support their operational decision-making processes.
Simulation can support early-stage production system planning by enabling
efficient evaluation of concept variants. However, detailed models are often
impractical in concept phases due to limited data and high modelling effort. This paper presents a structured three-stage simulation approach for supporting the concept planning of water electrolyzer stack production systems . The approach leverages automatic model generation from formal process descriptions and gradually increases model detail across stages, carried out through a custom-developed simulation framework. This enables early validation of the throughput capability, resource planning, and the analysis of stochastically occurring technical malfunction. The method aims to reduce planning time while improving decision quality.
The early design phase of deep-drawn structural components involves time-consuming iterative development. Traditional drawability assessments rely on finite element simulations, which are computationally expensive and slow the design process. Alternative machine learning (ML) approaches show promise in accelerating this process but face challenges with existing methods. Existing low-dimensional ML models only provide global predictions without identifying specific geometric regions prone to failure. High-dimensional models provide local predictions but require significant amounts of training data. We propose a data-driven approach leveraging graph neural networks (GNNs) for face-wise drawability prediction of sheet metal components in their computer-aided design (CAD) representation. Our method aims to bridge the gap between the computational efficiency of ML and the spatial resolution of simulation by providing face-wise insight into potential failure regions. This study utilises a dataset of parametric U-channel geometries with variability in both geometry and topology. Ground-truth labels are generated using inverse analysis simulations. Geometric entities are represented through the use of UV parameterisations, whereby 3D surfaces are mapped into 2D space to facilitate geometric encoding. Concurrently, the topological relationships are captured using a face adjacency graph. To address data scarcity, we evaluate how different amounts of training data affect model performance and perform ablation studies to analyse the impact of different CAD representation features. Our results show that the proposed approach achieves high accuracy even with limited training data. In addition, the ablation studies provide insights into the most critical CAD features, guiding future research. These results highlight the potential of our GNN to predict face-wise drawability in the early design phase.
Objectives
The study evaluated the influence of digital design software on crown morphology, occlusal characteristics, fracture force, and marginal fit across varying preparation designs for an identical target tooth.
Methods
A resin-based tooth (tooth 36) was digitized, manufactured (
), individually prepared and re-digitized. Five design groups were established using conventional software proposals, technician designs, two AI-based software solutions, and natural tooth-based reference designs. All systems employed consistent parameters. Crown designs were digitally assessed using quantitative morphological and occlusal metrics in reference to the original tooth. Crowns were milled, marginal fit was measured via digital microscopy, and fracture resistance was determined after thermal cycling and mechanical loading.
Results
Morphological metrics revealed statistically significant deviations across groups, with the technician design achieving the best performance. Occlusal metrics showed high deviations in the positional accuracy of the contact points across all groups. Technician and AI-based designs exhibited comparable functional results. None of the design groups were able to achieve contact with all relevant antagonist teeth, due to high deviations in the mesiolingual cusp. Conventional software designs exhibited the lowest fracture forces. Significant improvements were achieved through technician intervention. Vertical marginal discrepancies remained comparable across groups.
Significance
Improved functional and morphological design combined with high fracture resistance can reduce the need for clinical adjustments, minimize wear, and enhance crown longevity. Digital design software significantly influences crown morphology, occlusal characteristics and fracture forces. Vertical marginal discrepancies remain similar. AI-driven approaches demonstrate comparability with technician designs in terms of fracture forces, functional performance, and marginal fit.