In multi-stage press hardening, the product properties are determined by the thermo-mechanical history during the sequence of heat treatment and forming steps. To measure these properties and finally to control them by feedback, two soft sensors are developed in this work. The press hardening of 22MnB5 sheet material in a progressive die, where the material is first rapidly austenitized, then pre-cooled, stretch-formed, and finally die bent, serves as the framework for the development of these sensors. To provide feedback on the temporal and spatial temperature distribution, a soft sensor based on a model derived from the Dynamic mode decomposition (DMD) is presented. The model is extended to a parametric DMD and combined with a Kalman filter to estimate the temperature (-distribution) as a function of all process-relevant control variables. The soft sensor can estimate the temperature distribution based on local thermocouple measurements with an error of less than 10 °C during the process-relevant time steps. For the online prediction of the final microstructure, an artificial neural network (ANN)-based microstructure soft sensor is developed. As part of this, a transferable framework for deriving input parameters for the ANN based on the process route in multi-stage press hardening is presented, along with a method for developing a training database using a 1-element model implemented with LS-Dyna and utilizing the material model Mat248 (PHS_BMW). The developed ANN-based microstructure soft sensor can predict the final microstructure for specific regions of the formed and hardened sheet in a time span of far less than 1 s with a maximum deviation of a phase fraction of 1.8 % to a reference simulation.

Robust and versatile production is enabled by a closed-loop control of product properties. This essentially relies on the characterization of the interaction between properties and available degrees of freedom to control the process. In particular, this work examines the setting of collar height, thinning, curvature, and hardness during hot hole-flanging of X46Cr13 sheet material with simultaneous heat treatment to identify approaches for a closed-loop property control in hot hole-flanging during multi-stage hot sheet metal forming. To scrutinize the adjustability of the hardness of X46Cr13 sheet material by heat treatment with rapid heating and short dwell times, quenching tests with austenitizing temperatures from 900 to 1100 °C and dwell times from 1 to 300 s were carried out. A hardness between 317 and 680 HV10 was measured. By analyzing the force-displacement curve and the contact situation between tools and blank during hot hole-flanging, an understanding for the process was established. To determine the adjustability of geometrical collar properties and the hardness of the collar, collars were formed at punch speeds between 5 and 100 mm/s and at different temperatures. Here, a dependency of the geometry of the collar on temperature and punch speed as well as setting of the hardness was demonstrated.

A new method and roll stand for the manufacturing of structural profiles with tailored dimensions and properties based on the combination of rolling and profile bending is developed. Through this merged process the outer contour of a semi-finished product is shaped by a set of driven flexible and rigid rollers. The profile inner side is supported by an adjustable mandrel, so that an arbitrary variation of the cross-section along the profile can be achieved. Additionally, a moveable guiding roll facilitates the bending of the product. Conventionally, the manufacturing of load-optimized bent profiles requires a sequence of operations, whereas the novel method enables the manufacturing of those components in one process step. This leads to a reduction in tooling cost and setup time. Hence, the production of lightweight structures in small batch sizes is facilitated. In this contribution, the new forming method and the design of the developed roll stand are presented alongside with an investigation of the effects of stress superposition on bending stresses during combined bending and rolling as well as the possible processes applications.

In multistage press hardening processes, where a sheet material undergoes rapid austenitization, tempering, stretch-forming (SF), and die bending (DB), the resulting product properties are influenced by the thermo-mechanical history. This work aims at controlling the product properties of the formed blanks by making use of the model-based estimation and control of the spatial-temporal temperature distribution in the sheet. A data-driven dynamical model is constructed using dynamic mode decomposition (DMD) based on finite element (FE) simulation data. This model is further extended by means of parametric DMD to accommodate changes in process parameters like stroke rate, blank holder force, and austenitization temperature. The approach is validated and the model accuracy is improved through experimental analysis. The dynamics of the available temperature sensors are identified, whereupon a Kalman filter is developed based on the parametric DMD model to estimate the spatial-temporal temperature distribution. A time-varying, stage-dependent output matrix is employed to account for different numbers and locations of thermocouples in the three stages. Additionally, an optimal control strategy is implemented to achieve desired temperature trajectories, allowing targeted manipulation of the blank’s geometry and properties. Experimental validation of this system design and control strategy is carried out under real-time constraints.