In-plane simple shear tests have become commonplace in the fracture characterization of automotive sheet metals but have received less attention for constitutive characterization. Unlike tensile tests, simple shear tests do not have any tensile instability and remain in a state of plane stress until fracture. From plastic work equivalence, an isotropic hardening model can be readily constructed from the tensile and shear test data without inverse finite-element analysis. The success of the methodology hinges upon the shear specimen geometry and how the local strains in the gage region are measured using digital image correlation (DIC). In this study, finite-element simulations of seven shear test geometries were evaluated for an isotropic material in a series of virtual experiments by varying the input hardening response. The data from the simulations was extracted from the surface as if DIC was employed and used to determine the hardening behavior in comparison with the exact solution. Shear geometries without a notch eccentricity in the gage region appear to be best suited for characterizing low hardening materials with an error of less than 1% in the stress response for an n-value of 0.02. Conversely, for higher hardening materials corresponding to an n-value of 0.20 or greater, the geometries with a notch eccentricity performed best.

The demand from the automotive industry for increasingly complex sheet metal components and higher throughput in progressive die operations has led to the increased integration of sensors and control-systems in the sheet metal forming process. However, current control-systems used in sheet metal forming are often limited to measuring the state of the tooling during the forming process, neglecting the dynamic effects of the strip during its transfer between tooling operations. Developing a control strategy that accounts for the strip dynamics requires knowledge of how various process parameters influence the strip behaviour during both the transfer and forming stages. FE element models can accurately model the behaviour of sheet metal, but by themselves cannot identify a robust control strategy. Machine learning can solve this issue by constructing a probabilistic representation for the data generated from FE simulations to be used to identify a control strategy for sheet metal forming. The goal of this work is to conduct a parametric study on a progressive die FE model and evaluate the influence of various input parameters. The data collected from this FEM study will be used to construct a neural network model that will inform a control strategy for a progressive die.

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Modern progressive dies are increasingly equipped with integrated sensors and actuators, thus enabling an improvement of the manufacturing process and an inline monitoring of the component quality. The design of the tooling is determined at an early stage and is largely based on the desired product and the specific production stages such as blanking, forming and punching. Nevertheless, malfunctions such as strip vibrations cannot always be foreseen and may occur during operation time, which may lead to reduced component quality and in the worst case to component collision or a damage of the tooling. Therefore, complex reworking or a reduction of the stroke rate and thus lower output quantity are often necessary for a reliable and stable process. Strip vibrations are often caused by the highly dynamic transportation of the strip in the tool and can have various causes. Among others, passive tooling components such as spring-loaded, hard stop limited strip lifter can be the cause of such vibrations. Strip lifter are always necessary when three-dimensional components are produced and have to be lifted out of the die for the feeding phase. The feeding phase takes place between two strokes of the stamping process. This work is aiming for a control strategy to suppress strip vibrations in various progressive die stamping processes based on closed-loop controlled active strip lifter. These strip lifter combine the spring-loaded passive standard strip lifter with an additional PID-controlled actuator. Taking the dynamics of the flexible strip during operation into account, a Finite Element Analysis (FEA) model of a progressive die tooling system is created. For the design of the control algorithm, the FEA model is connected to an environment for model-based design in a co-simulation. This approach allows modelling the influence of arbitrary control parameter settings on the movement curve of the strip, aiming for an increased stroke rate.