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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.
New structural sheet metal parts are developed in an iterative, time-consuming manner. To improve the reproducibility and speed up the iterative drawability assessment, we propose a novel low-dimensional multi-fidelity inspired machine learning architecture. The approach utilizes the results of low-fidelity and high-fidelity finite element deep drawing simulation schemes. It hereby relies not only on parameters, but also on additional features to improve the generalization ability and applicability of the drawability assessment compared to classical approaches. Using the machine learning approach on a generated data set for a wide range of different cross-die drawing configurations, a classifier is trained to distinguish between drawable and non-drawable setups. Furthermore, two regression models, one for drawable and one for non-drawable designs are developed that rank designs by drawability. At instantaneous evaluation time, classification scores of high accuracy as well as regression scores of high quality for both regressors are achieved. The presented models can substitute low-fidelity finite element models due to their low evaluation times while at the same time, their predictive quality is close to high-fidelity models. This approach may enable fast and efficient assessments of designs in early development phases at the accuracy of a later design phase in the future.
Heat staking is a joining technology by which thermoplastic pins are formed by force and temperature to create a form- and force-fitting connection between components. This paper examines the characteristics of 3D printed pins in comparison to conventionally turned pins for heat staking applications. The 3D printed pins are created using fused layer modeling, with variations in horizontal and vertical building directions, as well as different layer thicknesses. The study investigates the impact of significant factors on the heat staking process, including the forming force and temperature. Tensile tests, micrographs, and micro-CT measurements were conducted to determine the properties of the heat-staked joints. Additionally, a stage plan was developed to enhance the understanding of the forming process of both printed and conventionally turned materials. The findings suggest that, under specific process parameters, 3D printed pins exhibit comparable strength to conventionally manufactured pins. The research also demonstrates that the anisotropy resulting from the layer-by-layer construction of the pins significantly influences the strength of the connection. Furthermore, the study reveals that 3D printed pins exhibit good forming accuracy during the heat staking process, and the cavities formed during printing can be substantially reduced.
Appropriate climate change mitigation requires solutions for all actors of the energy system. The residential sector is a major part of the energy system and solutions for the implementation of a seasonal hydrogen storage system in residential houses has been increasingly discussed. A global analysis of prosumer systems including seasonal hydrogen storage with water electrolyser, hydrogen compressor, storage tank, and a fuel cell studying the role of such a seasonal household storage in the upcoming decades is not available. This study aims to close this research gap via the improved LUT-PROSUME model, which models a fully micro sector coupled residential photovoltaic prosumer system with linear optimisation for 145 regions globally. The modelling of the cost development of hydrogen storage components allows for the simulation of a residential system from 2020 until 2050 in 5-year steps in hourly resolution. The systems are cost-optimised for either on- or off-grid operation in eight scenarios including battery electric vehicles, which can act as an additional vehicle-to-home electricity storage for the system. Results show that implementation of seasonal hydrogen systems only occurs in least cost solutions in high latitude countries when the system is forced to run in off-grid mode. In general, a solar photovoltaic plus battery system including technologies that can cover the heat demand is the most economic choice and can even achieve lower cost than a full grid supply in off-grid operation for most regions until 2050. Additional parameters including the self-consumption ratio, the demand cover ratio, and the heat cover ratio can therefore not be improved by seasonal storage systems if economics is the main deciding factor for a respective system. Further research opportunities and possible limitations of the system are then identified.
Kombinationen aus kraft- und formschlüssigen Verbindungen erhöhen die Leistung bei im Hybridguss verbundenen Gussteilen mit Anbau- oder Einlegeteilen und machen separate Fügeprozesse überflüssig. Simulations-Methoden unterstützen das Design kraft- und formschlüssiger Verbindungen. Ergebnisse eines Ausdrückversuchs zeigen eine relativ gute Übereinstimmung zwischen gemessenen und mittels Simulation ermittelten Kraft-Weg-Kurven.
Mechanical surface treatments such as deep rolling or laser-shock peening can markedly affect the cyclic performance of light-weight alloys, especially if significant thick work hardened surface regions are induced. At room temperature, the cyclic deformation behaviour is strongly influenced by the nature of the induced near-surface microstructures provided that they remain stable during fatigue loading. At elevated temperatures, the stability of near-surface work hardening and local microstructures plays an even more important role since the process-induced residual compressive stresses are likely to anneal out partially or completely. This overview illustrates to what extent deep rolling can effect the cyclic performance of various wrought light alloys being fatigue loaded in stress control at ambient and elevated temperatures.
Bauteilfertigung
(2009)
Mechanical surface treatments, such as deep rolling, shot peening and laser shock peening, can significantly improve the fatigue behavior of highly stressed metallic components. Deep rolling is an especially attractive technique since it is possible to generate deep, near-surface compressive residual stresses and work hardening while retaining a relatively smooth surface finish. Indeed, this technique is best known for increasing the fatigue strength and lifetime of steel components such as crankshafts. Although most work on deep rolling has been on steels, recently it has also been applied with reasonable success to titanium alloys. Accordingly, in this investigation, we examine the effect of deep rolling on the high-cycle fatigue behavior of Ti-6Al-4V, with particular emphasis on the thermal and mechanical stability of the residual stress states and near-surface microstructures. Preliminary results on laser shock peened Ti-6Al-4V are also presented for comparison. In addition, we examine whether these surface treatments are effective in retaining fatigue strength at the higher temperatures of 300 to 450C. Based on the cyclic deformation and stress/life behavior, together with the X-ray and microstructural observations, it is found that deep rolling can be quite effective in retarding the initiation and initial propagation of fatigue cracks in Ti-6Al-4V at such higher temperatures, despite the almost complete relaxation of the residual stresses at the surface. This clearly implies that, in addition to residual stresses, near-surface microstructures, which in Ti-6Al-4V consist of ultrafine near-surface nanostructures, play a critical role in the enhancement of fatigue lifetimes by mechanical surface treatments.
In this paper, the high-temperature fatigue behavior and residual stress states of a Ti-6Al-4V alloy are investigated after mechanical surface treatment. In particular, the two surface treatments investigated, laser shock peening and deep rolling, were observed to result in significantly different residual stress states. The consequent isothermal fatigue behavior at elevated temperatures, characterized using cyclic deformation curves, are discussed in terms of the stability of the near-surface work hardening and compressive residual stresses. Despite pronounced relaxation of the residual stresses, both laser shock peening and deep rolling led to a significant improvement in the cyclic deformation behavior and, hence, increased fatigue lifetimes at elevated temperatures as compared to untreated materials states.
Dual front steering axles are quite common in multi-axled heavy duty trucks. In standard layouts of such axle combinations, the steer motions of the wheels depend not only on the rotation of the steering wheel but also on the movements of the axles. As a consequence, the model complexity of the steering system should match with the complexity of the suspension model. The development of new technologies like advanced driver assistance systems or autonomous driving can only be accomplished efficiently using extensive simulation methods. Such kind of applications demand for computationally efficient vehicle models. This paper presents a steering system model for dual front axles of heavy duty trucks which supplements the suspension model of the axles. The model takes the torsional compliance of the steering column as well as the stiffness of the tie rods and the coupling rod into account. A quasi-static solution provides a straight forward computation including the partial derivatives required for an efficient implicit solver. The steering system model matches perfectly with comparatively lean, but sufficiently accurate multibody suspension models.