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The present study investigates the interface between carbon steel and titanium samples annealed at different temperatures (𝜗1 = 800 ◦C and 𝜗2 = 1050 ◦C). In both cases, an observable layer forms at the interface, with its thickness increasing from t𝜗1= 2.75 ± 0.15 μm at 800 ◦C to t𝜗2= 8.86 ± 0.29 μm at 1050 ◦C. The layer’s composition and thickness evolve with temperature. Analysis reveals approximately 40 at.-% carbon concentration in the exterior region, indicating likely titanium carbide creation. X-ray diffraction identifies titanium carbide peaks, while microscopy and elemental mapping confirm compositional gradients at the interface.
Electron Backscatter Diffraction (EBSD) shows a gradient in grain size near the TiC surface, reflecting TiC nucleation rates. XRD data detect both titanium carbide and titanium phases, with TiC becoming more prominent at 1050 ◦C. Rietveld analysis further confirms TiC formation. Notably, distinct diffraction patterns on the contact and rear sides suggest a Ti(C, O, N) presence. Depth profiles exhibit varying surface and depth carbon concentrations, attributed to temperature effects. The study successfully demonstrates TiC coating fabrication through hot pressing, wherein Ti(C, O, N) coatings arise from titanium’s affinity for reacting with oxygen and nitrogen. This research contributes to the understanding of phase transformations and interfacial properties in titanium-carbon steel systems.
Test Setup for Investigating the Impact Behavior of Biaxially Prestressed Composite Laminates
(2024)
Instrumented impact testing and compression-after-impact testing are important to adequately qualify material behavior and safely design composite structures. However, the stresses to which fiber-reinforced plastic components are typically subjected in practice are not considered in the impact test methods recommended in guidelines or standards. In this paper, a test setup for investigating the impact behavior of composite specimens under plane uniaxial and biaxial preloading is presented. For this purpose, a special test setup consisting of a biaxial testing machine and a specially designed drop-weight tower was developed. The design decisions were derived from existing guidelines and standards with the aim of inducing barely visible impact damage in laminated carbon fiber-reinforced plastic specimens. Several measurement systems have been integrated into the setup to allow comprehensive observation of the impact event and specimen behavior. A feasibility test was performed with biaxially prestressed carbon fiber-reinforced plastic specimens in comparison with unstressed reference tests. The compressive-tensile prestressing resulted in lower maximum contact forces, higher maximum deflections, higher residual deflections and a different damage pattern, which was investigated by light microscopic analysis. Finally, the functionality of the experimental setup is discussed, and the results seem to indicate that the test setup and parameters were properly chosen to investigate the effect of prestresses on the impacts behavior of composite structures, in particular for barely visible subsequent damages.
The human nose serves as the primary gateway for air entering the respiratory system and plays a vital role in breathing. Nasal breathing difficulties are a significant health concern, leading to substantial healthcare costs for patients. Understanding nasal airflow dynamics is crucial for comprehending respiratory mechanisms. This article presents a detailed study using tomo-Particle Image Velocimetry (PIV) to investigate nasal airflow dynamics while addressing its accuracy. Embedded in the OpenNose project, the work described aims to provide a validation basis for different numerical approaches to upper airway flow. The study includes the manufacturing of a transparent silicone model based on a clinical CT scan, refractive index matching to minimize optical distortions, and precise flow rate adjustments based on physiological breathing cycles. This method allows for spatial high-resolution investigations in different regions of interest within the nasopharynx during various phases of the breathing cycle. The results demonstrate the accuracy of the investigations, enabling detailed analysis of flow structures and gradients. This spatial high-resolution tomo-PIV approach provides valuable insights into the complex flow phenomena occurring during the physiological breathing cycle in the nasopharynx. The study's findings contribute to advancements in non-free-of-sight experimental flow investigation of complex cavities under nearly realistic conditions. Furthermore, reliable and accurate experimental data is crucial for properly validating numerical approaches that compute this patient-specific flow for clinical purposes.
Microchips are intensively used in almost all nowadays electronic devices. With the continuous advancement of our technologies, they get smaller in size than ever before. They generate high-intensity heat loads that need to be transported effectively such that they may function properly. Heat pipes have proven to be very effective in transporting relatively large heat loads from miniature components. They are of seamless structure that involves a working fluid capable of evaporation and condensation at the working temperature of the electronic chips. The working fluid is derived to move from the condenser to the evaporator via multiple microgrooves using capillary forces. It is important that the condensate reaches the evaporator at a proper rate such that no dry-out or flooding occur. In this work, we are particularly interested in the case of capillary-driven flows in rectangular microchannels. A generalized model is developed that works for axe-symmetric rectangular channels of arbitrary, moderately varying width profiles. It also accounts for any contrast of viscosity between the liquid and the vapor under isothermal conditions. The model shows to reduce to the special case of imbibition in straight and uniform microchannels, for which comparisons with experimental and modeling works show an excellent match. Cases representing linearly and quadratically varying converging/diverging width profiles have been explored. It is found that the viscosity ratio has a significant influence on the rate at which the meniscus advances. The model also negates the common practice found in the literature of using the formula developed for imbibition rates in capillary tubes for rectangular microchannels by replacing the diameter of the tube with the hydraulic diameter. It is also found that the channel profile has an influential effect on the imbibition rates. For tapered microchannels, the capillary force increases along the channel length while it decreases for diverging ones. It is interestingly demonstrated that, for quadratically tapered microchannel, the speed of the meniscus increases towards the end of the microchannel compared with linearly varying microchannels. On the other hand, for diverging microchannels, the speed of the meniscus decreases due to the increase in the cross-sectional area. Computational fluid dynamics (CFD) analysis has been conducted to provide a framework for confirmation and verification for which very good match has been established, which builds confidence in the modeling approach.
Preliminary considerations on the form-finding of a tensegrity joint to be used in dynamic orthoses
(2024)
In the early-stage development of sheet metal parts, key design properties of new structures must be specified. As these decisions are made under significant uncertainty regarding drawing configuration changes, they sometimes result in the development of new parts that, at a later design stage, will not be drawable. As a result, there is a need to increase the certainty of experience-driven drawing configuration decisions.
Complementing this process with a global sensitivity analysis can provide insight into the impact of various changes in drawing configurations on drawability, unveiling cost-effective strategies to ensure the drawability of new parts. However, when quantitative global sensitivity approaches, such as Sobol's method, are utilized, the computational requirements for obtaining Sobol indices can become prohibitive even for small application problems. To circumvent computational limitations, we evaluate the applicability of different surrogate models engaged in computing global design variable sensitivities for the drawability assessment of a deep-drawn component.
Here, we show in an exemplary application problem, that both a standard kriging model and an ensemble model can provide commendable results at a fraction of the computational cost. Moreover, we compare our surrogate models to existing approaches in the field. Furthermore, we show that the error introduced by the surrogate models is of the same order of magnitude as that from the choice of drawability measure. In consequence, our surrogate models can improve the cost-effective development of a component in the early design phase.
Transient surrogate modeling of modally reduced structures with discontinuous loads and damping
(2024)
In this work, a surrogate model for structural, transient and discontinuously excited finite element method simulations is developed. This allows to reduce the computational effort of repeated calculations of identical models under different load cases. The architecture of the surrogate combines fully connected neural network layers with long short-term memory layers. For the reproduction of different damping ratios, a categorical variable is added to the continuous input data. Based on a recursive flow of the predicted data back to the input layer, long-term dependencies do not vanish due to short-input sequences. The system dimension is reduced by applying the model-order reduction technique for modal decomposition. The high accuracy of the surrogate and the reduction of computational costs are shown on an academic example of a cantilever beam and a real-world example of a robot. The advantages of our approach are illustrated in comparison with state-of-the-art surrogates for transient finite element analysis. By using the surrogate proposed in this study, oscillations due to discontinuous excitation of mechanical structures can be reproduced. For this purpose, only short-input sequences are necessary since the excitation of the oscillations does not have to be part of the input sequence during the whole duration of the oscillations. Due to the categorical variable for the damping ratio, the surrogate can account for the influence of different damping in parameter studies.
Generative deep learning approaches for the design of dental restorations: A narrative review
(2024)
Objectives:
This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.
Data/Sources:
PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.
Study selection:
The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.
Conclusions:
Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.
Clinical significance:
Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.
The present paper takes a novel approach to production of fibre-reinforced thermoplastic tubes. The method begins with the raw materials, reinforcing fibre and thermoplastic granulate which are processed to tapes through a newly developed direct impregnation process. It is followed by consolidation of fibre-reinforced thermoplastic tubes using infrared (IR) emitters in the filament winding process. This process employs various angles and utilizes a rotatable consolidation axis. The winding process operates at a constant speed, addressing the challenge of bending the fibre-reinforced tapes in the angle reversal areas near the tube ends. Experiments have confirmed that the process can run at speeds reaching approximately 470 mm/min. The design of the impregnation line takes into account the properties of the thermoplastic and the roving, allowing for a speed of up to 1 m/s.
Background and objectiveDue to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.MethodsA Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.ResultsThe reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.ConclusionsThis article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.
AbstractRegularized static friction models have been used successfully for many years. However, they are unable to maintain static friction in detail. For this reason, dynamic friction models have been developed and published in the literature. However, commercial multibody simulation packages such as Adams, RecurDyn, and Simpack have developed their own specific stick-slip models instead of adopting one of the public domain approaches. This article introduces the fundamentals of these commercial models and their behavior from a practical point of view. The stick-slip models were applied to a simple test model and a more sophisticated model of a festoon cable system using their standard parameters.
Im Betrieb von Förderbändern auftretende Phänomene sind das Bandwandern in Achsrichtung der Walzen und die Bandverformung in der Bandebene [1, S. 142]. Das Bandwandern ist bisher detailliert untersucht [2–4], während die Bandverformung Gegenstand weniger Veröffentlichungen ist. Deshalb wird in diesem Artikel eine Methode zur Berechnung der sich im dynamischen Betrieb einstellenden Verformung eines elastischen Förderbandes vorgestellt. Dazu wird ein Mehrkörpersimulations-Modell basierend auf der Lumped-Mass-Modellierung verwendet. Als Untersuchungsbeispiel dient ein Zwei-Walzensystem mit flexiblen und zylindrischen Walzen, sowie ein flexibles Förderband. Es zeigt sich, dass mit der Lumped-Mass-Modellierung eine dynamische Bandverformung, die von der gewählten Diskretisierung abhängt, berechnet werden kann. Aufgrund dieser Abhängigkeit ist es notwendig eine Konvergenzanalyse durchzuführen. Zusätzlich ist darauf zu achten, dass die künstliche Anregung aufgrund der Lumped-Mass-Modellierung nicht mit einer Eigenfrequenz des Modells zusammenfällt.
Due to the complexity and the number of factors involved in factory layout planning, computers were identified as an efficient tool to support the process. However, so far no method for computer-aided layout planning has gained wide acceptance in practical application. One reason for this is that in present approaches either the user or the computer designs the layout, neglecting either the qualitative or the quantitative goals. To bridge this gap, this article introduces a concept for human-computer-integration based on evaluative feedback and inverse reinforcement learning. A key element of the concept is the interactive planning process in which user and computer alternately design and improve the layout until a satisfactory layout is found. The user evaluates the layouts according to qualitative criteria, adjusts them intuitively and specifies objectives and restrictions in an explorative way. The computer on the other hand – in form of a reinforcement algorithm – generates possible layouts and incorporates the user’s feedback into its policy. This synergy is expected to generate better results than an expert or an algorithm alone could. Furthermore, in the context of learning factories, it encourages critical thinking and allows students to develop a deeper understanding of the factors that contribute to efficient manufacturing processes. Both an architecture for the implementation is proposed and the requirements for the user interface are specified.
Internal transport systems are an essential part of intralogistics in production and distribution facilities. These are characterized by a variety of technologies as well as a multitude of interactions with other processes, such as warehouse, picking, and production processes. Therefore, resource planning and control of these systems is complex, especially for discontinuous conveyors. In this task, users can be supported by Digital Twins for decision-making, as they are suitable for investigating both future system states and possible actions. However, relevant use cases that are generally applicable across sectors as well as a generic system architecture for Digital Twins for resource planning and process control of in-plant transport systems have not yet been sufficiently investigated. In this paper, use cases are presented, relevant functions defined, and, finally, a generic functional and a logical reference architecture described. This is conducted with the design science in information systems research method together with a Systems Engineering approach. The use cases are determined at industrial partners of the research project TwInTraSys, which explores Digital Twins for the planning and control of internal transport systems. They are generalized and, thus, also applicable to other production and distribution facilities in different sectors. Further, the reference architecture can provide a basis for the successful implementation of the Digital Twin.
Influence of carbon content on the formation of TiC at diffusion bonded titanium-steel interface
(2023)
Hot pressing of pure Ti and various carbon steels in a temperature range of 950 – 1050 °C creates an up to 9 μm thick compound layer of TiC at the Ti/ steel interface. The calculation of the activation energy for layer formation is 126.5 - 136.7 kJ/mol, independent of the steels carbon content. As the carbon content of the steel increases, the layer thickness also increases, which provides enormous potential for the surface modification of Ti/ Ti-alloys.
The movement of a meniscus inside a capillary tube has been extensively studied in the context of displacing one fluid with another immiscible one. This phenomenon exists in many applications including pharmaceutical, oil production, filtration and separation processes, and others. When one of the phases is entrapped inside a capillary tube, it forms what is called a ganglion with two menisci between the two fluids. In a straight uniform capillary tube, a stagnant entrapped ganglion is symmetric. The situation is different if the capillary tube is tapered in which case the two menisci assume different curvatures. Such inhomogeneity of the capillary pressure self-propels the ganglion to move. The fate of the ganglion inside the tapered tube depends on whether it is wetting or nonwetting to the tube wall. That is, after the initial movement, a wetting ganglion accelerates towards the tapered end of the tube while a nonwetting one decelerates towards the wider end before reaching a terminal configuration. Such fates are linked to the variations of the capillary pressure, which continuously increases for a wetting ganglion and decreases for the nonwetting one. In this work, a generalized model is developed that not only describes capillary-driven dynamics over a wide range of viscosity and density contrasts but also pressure-driven scenarios with/without gravity. The model, however, neglects the inertial effect of the two fluids on account of the fact that it is confined to the very early time of the movement process. A first-order nonlinear ordinary differential equation is developed that describes the dynamic behavior of both the wetting and nonwetting ganglions. A fourth-order Runge-Kutta algorithm is developed to solve the model equations. Furthermore, a computational fluid dynamics (CFD) analysis was used to provide a comparison and verification framework.
Additive Manufacturing (AM) is a future-oriented manufacturing technology that is experiencing an enormous boom in the times of Industry 4.0. As a result, various AM technologies and printer models from different manufacturers are entering the market over a short time span. With the advancing establishment of this manufacturing technology for series applications, the expectations and requirements of the fabricated components are also increasing. However, a major challenge is the application-specific selection of the most suitable AM process due to a lack of comparable data. Furthermore, there needs to be more know-how regarding the geometrical and mechanical characteristics of AM parts. This paper addresses this problem by comparing the three most common plasticbased AM processes in the areas of surface quality, dimensional accuracy, and mechanical properties. Roughness measurements, evaluation of a benchmark artifact, tensile tests, and load increase tests are carried out. Based on the results, the individual possibilities and limitations of the compared AM processes can be detected.
Developing new deep-drawn sheet metal parts is a challenging task due to conflicting demands for low costs, durability, and crash properties. Ensuring manufacturability throughout geometrical changes adds to the complexity, leading engineers to rely on experience-driven iterative design changes that compromise requirements and lack reproducibility. Finite Element (FE) simulation models are employed to ensure manufacturability, albeit at the expense of high computational costs and delays in part development.
To improve efficiency, a Machine learning (ML)-centered approach was proposed to ensure manufacturability. However, the limited availability of data raises uncertainty about whether a low- or high-dimensional ML approach is most suitable for drawability assessment.
This work compares the accuracy of a low-dimensional, feature-based Linear Support Vector surrogate and an adapted high-dimensional PointNet model under different dataset sizes. The dataset is composed of parametrically generated, U-shaped structural sheet metal parts.
We use a one-step simulation scheme and evaluate results with a Forming Limit Diagram (FLD) to label drawability. Results show the point of transition to be at about 500 training samples, from which onwards Deep learning is advantageous.
Moreover, the generalizability of these models is tested on a second dataset with topologically similar components. This is to assess the potential for a geometrically more comprehensive evaluation. We discuss several influences on model performances and outline future potentials.