Abstract: Mathematical models of a suspension strut such as an aircraft landing gear are utilized by engineers in order to predict its dynamic response under different boundary conditions. The prediction of the dynamic response, for example the external loads, the stress and the strength as well as the maximum compression in the spring-damper component aids engineers in early decision making to ensure its structural reliability under various operational conditions. However, the prediction of the dynamic response is influenced by model uncertainty. As far as the model uncertainty is concerned, the prediction of the dynamic behavior via different mathematical models depends upon various factors such as the model's complexity in terms of the degrees of freedom, material and geometrical assumptions, their boundary conditions and the governing functional relations between the model input and output parameters. The latter can be linear or nonlinear, axiomatic or empiric, time variant or time-invariant. Hence, the uncertainty that arises in the prediction of the dynamic response of the resulting different mathematical models needs to be quantified with suitable validation metrics, especially when the system is under structural risk and failure assessment. In this contribution, the authors utilize the Bayesian interval hypothesis-based method to quantify the uncertainty in the mathematical models of the suspension strut.
Abstract: Efficient surrogate modelling of computer models (herein defined as simulators) becomes of increasing importance as more complex simulators and non-deterministic methods, such as Monte Carlo simulations, are utilised. This is especially true in large multidimensional design spaces. In order for these technologies to be feasible in an early design stage context, the surrogate model (oremulator) must create an accurate prediction of the simulator in the proposed design space. Gaussian Processes (GPs) are a powerful non-parametric Bayesian approach that can be used as emulators. The probabilistic framework means that predictive distributions are inferred, providing an understanding of the uncertainty introduced by replacing the simulator with an emulator, known as code uncertainty. An issue with GPs is that they have a computational complexity of O(N3) (where N is the number of data points), which can be reduced to O(NM2) by using various sparse approximations, calculated from a subset of inducing points (where M is the number of inducing points). This paper explores the use of sparse Gaussian process emulators as a computationally efficient method for creating surrogate models of structural dynamics simulators. Discussions on the performance of these methods are presented along with comments regarding key applications to the early design stage.
Abstract: The stiffness of metal formed products strongly affects the dynamic behavior of structures in which they are integrated. Forming processes underlie short and long-term variations which cause the stiffness to be uncertain.In the application of resonant shunted piezoelectric transducers for vibration attenuation, uncertain stiffness may cause significant reduction in the vibration attenuation performance due to imprecise tuning. In the past, large efforts were made to control one or more geometrical feature of products while weightier features that cause uncertainty have not been addressed.In this paper, a single point incremental forming process of a membrane-like spring element on a servo press with a 3 degrees of freedom drive system is investigated. This spring element is used in a beam support for lateral vibration attenuation with resonant shunted transducers as well as axial buckling stabilization.To reduce uncertainty caused by process variations, an offline closed-loop control of product stiffness is presented. Different product and forming criteria are integrated into a control approach based on an optimization routine. By making use of a model-based prediction of the product properties, the approach shows how to realize a multi-objective control.
Abstract: This paper shows how a databased approach towards production optimization was realized with the help of cloud-technologies. Several uncertainties, either in the manufacturing of the producing machines or in the production on these machines can be systematically reduced. In this way a significant improvement in production amount, but also in produced quality can be reached.
Abstract: Metal forming processes may induce internal damage in the form of voids in the workpiece under unfavorable deformation conditions. Controlling the amount of damage induced by metal forming operations may increase service performance of the produced parts. Damage is crucial in high-performance components of limited workability such as jet engine turbine blades. Recent developments have introduced forged titanium aluminides into commercial jet engines. Titanium aluminides are lightweight intermetallic compounds with excellent creep properties but very limited ductility. Their low workability requires isothermal forging at slow strain rates, which is typically kept constant in the process. This work explores the possibility of increasing the ram speed during the process so that the process time is reduced while the amount of damage introduced into the workpiece is controlled. The results show that a 25% reduction in process time seems viable without increase in damage by solving an optimal control problem, in which the ram speed profile is determined off-line by minimization.
Abstract: Feedback and process control of metalforming processes has received increasing attention the lastdecade. Basically there exist four control philosophies; control ofprocess parameters during the punch stroke, iterative learning control(based on historical data), a combination iterative learning andfeedback control and finally feed-forward control. The present work willpresent three different control schemes which all are based onfeedback philosophy i.e. control during the punch stroke or iterativelearning control, where process parameters are updated according toprocess history. The three control schemes are tested using a non-linear finite element model of a square deep-drawing and finallypros and cons are discussed based on the numerical results.
Abstract: Load-carrying systems often suffer from unexpected disruptions which can cause damages or system breakdowns if they were neglected during product development. In this context, unexpected disruptions summarize unpredictable load conditions, external disturbances or failures of system components and can be comprehended as uncertainties caused by nescience. While robust systems can cope with stochastic uncertainties, uncertainties caused by nescience can be controlled only by resilient load-carrying systems. This paper gives an overview of the characteristics of resilience as well as the time-dependent resilient behaviour of subsystems. Based on this, the adaptivity of subsystems is classified and can be distinguished between autonomous and externally induced adaption and the temporal horizon of adaption. The classification of adaptivity is explained using a simple example of a joint brake application.
Abstract: By combining the established development method according to VDI guideline 2206 and the new approach of resilience, resilient product development makes it possible to control uncertainty in the early development phases. Based on the uncertainty that can occur in a classical product development process, such as uncertainty due to (i) the transition from function to building structure, (ii) interaction of modules and (iii) planning uncertainty, we first discuss the limits of existing product development guidelines and introduce the concept of resilience. The basic idea is that a resilient process can control uncertainty through the four resilience functions (i) monitoring, (ii) responding, (iii) learning and (iv) anticipating. We apply this new approach to the product development of the actuators of the active airspring of the TU Darmstadt. The active air spring can be used to increase driving comfort in a vehicle or, for example, to minimize kinetosis during autonomous driving.
Abstract: Given industrial applications, the costs for the operation and maintenance of a pump system typically far exceed its purchase price. For finding an optimal pump configuration which minimizes not only investment, but life-cycle costs, methods like Technical Operations Research which is based on Mixed-Integer Programming can be applied. However, during the planning phase, the designer is often faced with uncertain input data, e.g. future load demands can only be estimated. In this work, we deal with this uncertainty by developing a chance-constrained two-stage (CCTS) stochastic program. The design and operation of a booster station working under uncertain load demand are optimized to minimize total cost including purchase price, operation cost incurred by energy consumption and penalty cost resulting from water shortage. We find optimized system layouts using a sample average approximation (SAA) algorithm, and analyze the results for different risk levels of water shortage. By adjusting the risk level, the costs and performance range of the system can be balanced, and thus the system's resilience can be engineered.