Christoph Hartmann

Mechanical load on a particle aggregate in mono-axial elongational flow

Abstract Subject of the present contribution is the numerical simulation of the effect of a elongational flow field on a suspended particle-aggregate. The particle-aggregate consists of seven rigid spherical particles and is suspended in the flow field at low Re-numbers (Re=0.01–0.1). The ratio of particle and fluid densities varies between 1 and 15. The velocity and pressure distribution is obtained from the numerical solution of the Navier–Stokes equation and the continuity equation based on finite volume methods. The particle motion is obtained from the Euler equation of motion for rigid bodies. A comparison of classical solutions with the result of the numerical simulation for a spherical particle shows a very good agreement. It can be shown, that the interaction of the aggregate with the fluid differs clearly from that of a spherical particle. Furthermore, it has been found that the magnitude of stresses on the aggregate surface is increasing with time monotonously. Shear stress is maximum on the outer parts of the aggregate. Normal stress takes on maximum values on the upstream and downstream oriented faces. The maximum pressure drop across the particle results in an extensional force which increases in time within the considered period.

An artificial neural network approach for tool path generation in incremental sheet metal free-forming

This research considers a specific incremental sheet metal free-forming process, which allows for individualized component manufacturing. However, for a reasonable application in practice, an automation of the manual process is mandatory. Unfortunately, up to now, no general tool path generation strategies are available when free-forming processes are to be utilized. On this account, for the investigated driving process, a holistic concept for deriving tool paths for the production of sheet metal parts directly from a digital component model is presented adopting an artificial neural network architecture. Consequently, for the very first time an automated part production is possible in incremental sheet metal free-forming applications. For this, a suitable network input and output structure is designed. Balanced sample data sets are generated for appropriate training. An associated network topology is determined and undergoes a training and testing phase. The influence of different training algorithms, network configurations, as well as training sets have been studied in relation to a feedforward network structure with backpropagation. Finally, the proposed computer integrated manufacturing system is subject to validation and verification by automated sheet part production, which is followed by concluding remarks on the capabilities and limits of the concept.

Determination of crystallographic young’s modulus for sheet metals by in situ neutron diffraction

Elastic recovery is an important issue in sheet metal forming, especially in the context of the upcoming use of high strength steels due to shifted relations between Youngs modulus and strength. One important factor when it comes to elastic recovery prediction is a deep understanding for the elasto-plastic characteristics of the material. Today in general simple elastic behavior with constant Youngs modulus and Poissons ratio is assumed. Macroscopic analysis in standard tests shows that these assumptions are insufficient for an appropriate prediction of elastic recovery in sheet metal forming, which is why different phenomenological correlation models are derived. An experimental setup and microscopic investigation to further prove these models and to verify the approaches on another scale for sheet metals is presented within this paper. In the study microscopic deformation behavior of loading and unloading of a HC260LA sheet metal is analysed using in-situ neutron diffraction. Based on the lattice plane strains an orientation specific crystallographic Youngs modulus for different rolling directions is determined.

Damages Recognition on Crates of Beverages by Artificial Neural Networks Trained with Data Obtained from Numerical Simulation

A new method to detect damages on crates of beverages is investigated. It is based on a pattern-recognition-system by an artificial neural network (ANN) with a feedforward multilayer-perceptron topology. The sorting criterion is obtained by mechanical vibration analysis which provides characteristic frequency spectra for all possible damage cases and crate models. To support the network training, a large number of numerical data-sets is calculated by finite-element-method (FEM). The combination of artificial neural networks with methods of numerical simulation is a powerful instrument to cover the broad range of possible damages. First results are discussed with respect to the influence of modelling inaccuracies of the finite-element-model and the support of ANN by training-data obtained from numerical simulation.