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Paddle shape optimization for hole-flanging by paddle forming through the use of a predefined strain path in finite element analysis

Besong Lemopi Isidore, Buhl Johannes, Bambach Markus

Journal of Machine Engineering

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2019

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Vol. 19, No. 2

83--98

EN

This research investigates a novel hole-flanging process by paddle forming through the use of finite element (FE) simulations. Paddles of different shapes rotating at high speeds were used to deform clamped sheets with predrilled holes at their centers. The results of the simulations show that the paddle shape determines the geometry and principal strains of the formed flanges. A convex-shaped paddle forms flanges with predominant strains in the left quadrant of the forming limit diagram (FLD). However, the convex paddle promotes unwanted bulge formation at the clamped end of the flange. A concave paddle forms flanges with no bulge but the principal strains of elements in the middle section of the flange are in the right quadrant of the FLD which indicates an increased probability for crack occurrence. An optimization of the paddle shape was conducted to prevent bulging at the clamped end while avoiding crack occurrence. The paddle shape was optimized by mapping the deformation of some elements along the flange length to a pre-defined strain path on the FLD while maintaining the bulge height within the desired geometric tolerance. The radii and lengths of the paddle edge were varied to obtain an optimum paddle shape.

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Modeling and convergence analysis of directed energy deposition simulations with hybrid implicit / explicit and implicit solutions

Buhl Johannes, Israr Rameez, Bambach Markus

Journal of Machine Engineering

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2019

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Vol. 19, No. 3

94--107

EN

Conventional metal manufacturing techniques are suitable for mass production. However, cheaper and faster alternatives are preferred for small batch sizes and individualized components. Directed energy deposition (DED) processes allow depositing metallic material in almost arbitrary shapes. They are characterized by cyclic heat input, hence heating and cooling every point in the workpiece several times. This temperature history leads to distribution of mechanical properties, distortions, residual stresses or even fatigue properties in the part. To avoid experimental trial-and-error optimization, different methods are available to simulate DED processes. Currently, the wire arc additive manufacturing (WAAM) is the most competitive DED process. In this work, a simulation method for the WAAM process is established and validated, which should be capable to calculate global effects (e.g. distortions, residual stresses) of real WAAM-processes with duration of hours and thousands of weld beads. The addition of beads and layers is simulated by the element birth and death technique. The elements are activated according to the movements of the heat source (arc). In this paper, the influence of the time step, the mesh size and the material properties of the inactive elements in hybrid implicit / explicit and fully implicit solutions are evaluated with respect to the computation time and stability. This investigation concludes several recommendations for AM-modelling. For example, a low Young’s modulus (100 N/mm2) for the inactive elements show nearly no influences on the welding simulation, but introduces numerical instabilities in case of multiple welding beads. The Young’s modulus should be increased to 1.000 N/mm2 for small mesh-sizes, small step-sizes and many beads, even when it introduces unwanted stresses.

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Using neural networks to predict the low curves and processing maps of TNM-B1

Stendal Johan Andreas, Emdadi Aliakbar, Sizova Irina, Bambach Markus

Computer Methods in Materials Science

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2018

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Vol. 18, No. 4

134--142

EN

The ability to predict the behavior of a material is vital in both science and engineering. Traditionally, this task has been carried out using physics-based mathematical modeling. However, material behavior is dependent on a wide range of interconnected phenomena, properties and conditions. During deformation processes, work hardening, softening, microstructure evolution and generation of heat all occur simultaneously, and can either cooperate or compete. In addition, they can vary with the deformation temperature, applied force and process speed. As the complete picture of material behavior from the macroscopic scale to the atomic scale is not yet fully understood, deformation processes such as hot forging can be difficult to handle using physics-based modeling. Usually, modeling the high temperature deformation behavior of metals consists of extracting characteristic points from the experimental flow curve data, and use them to fit the model equations through regression analysis. This is called phenomenological modeling, as it is based on the observations of a phenomena rather than being derived from fundamental theory. Alternatively, the data obtained from experiments could be used for a data-driven or machine learning (ML) approach to model the material behavior. An ML model would require no knowledge of the underlying physical phenomena governing a deformation process, as it can learn a mapping function which connects input to output based purely on the experimental data. In this work, the application of machine learning to modeling the flow curves of two different states of the titanium aluminide (TiAl) TNM-B1; hot isostatically pressed (HIPed) and heat treated, is investigated. Neural networks were used to learn a mapping function which predicted flow stress based on the inputs temperature, strain and strain rate. In addition, strain rate sensitivity maps and processing maps based on the experimental and the predicted data are analysed and compared. The results revealed that the neural networks were able to produce realistic and accurate flow curves, which fitted to the underlying behavior of the experimental data rather than the noise. The strain rate sensitivity and processing maps showed conflicting results. Good correlation was found for the HIPed material state between the ones based on experimental data and the ones based on predicted values, while there was a significant difference for the heat treated state.

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Numerical analysis of damage during hot forming

Szyndler Joanna, Muhammad Imran Muhammad, Muhammad Junaid Afzal Muhammad Junaid, Bambach Markus

Computer Methods in Materials Science

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2018

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Vol. 18, No. 3

107--114

EN

The main aim of the presented research is the analysis of damage evolution in 16MnCrS5 steel during hot forming based on results obtained from finite element modelling. Particular attention is put on the interaction between dynamic recrystallization (DRX) and damage initiation at the matrix-inclusion interface. Moreover, a modified Gurson-Tvergaard- Needleman (GTN) model is proposed with the nucleation criterion taken from an extended Horstemeyer model, which predicts damage nucleation based on material softening due to the DRX and stress state in the material.

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A simulation study on the closed-loop control of screw press forgings using the impact energy as control input

Bambach Markus, Benjamin Sydow Benjamin, Markus Hirtler Markus, Sviridov Alexander

Computer Methods in Materials Science

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2018

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Vol. 18, No. 3

98--106

EN

Screw presses are energy-restricted forming machines that use rotational energy stored in a flywheel for forming, which is converted into a linear movement by a threaded screw. Screw presses are widely used for forging steel, aluminum and brass. In a direct-driven electrical screw press, a reversible electric motor is mounted directly on the screw and on the press frame above the flywheel. With directly driven screw presses, the blow energy can be exactly dosed from one blow to the next. However, so far no prior work is known which uses the blow energy as a control input in a targeted manner to influence the properties of the forging. The purpose of the present work is to lay the foundations for property control through blow energy dosing during forging on screw presses. Process control becomes increasingly interesting due to ever increasing customer demands and needs for resource-efficient production. A major challenge is the variation of process parameters, e.g. temperature variations in the furnace, during transport or due to inherent uncertainty in the heat transfer to the dies and the environment. If the process conditions are changing the deviations from the planned process trajectory may lead to an insufficient die filling or undesired final properties. Forged parts require high precision considering the part geometry and material properties. During forming two mechanisms in terms of forming temperature take place: heat conduction due to contact with tools and heat dissipation due to plastic deformation. The heat transfer acts as disturbance, the impact energy can be used as control input. In this work, investigations into process control by impact energy dosing are put forward using FE (finite element) simulations.