Soft Sensors for Property-Controlled Multi-Stage Press Hardening of 22MnB5

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

Softsensors: key component of property control in forming technology

The constantly increasing challenges of production technology for the economic and resource-saving production of metallic workpieces require, among other things, the optimisation of existing processes. Forming technology, which is confronted with new challenges regarding the quality of the workpieces, must also organise the individual processes more efficiently and at the same time more reliably in order to be able to guarantee good workpiece quality and at the same time to be able to produce economically. One way to meet these challenges is to carry out the forming processes in closed-loop control systems using softsensors. Despite the many potential applications of softsensors in the field of forming technology, there is still no definition of the term softsensor. This publication therefore proposes a definition of the softsensor based on the definition of a sensor and the distinction from the observer, which on the one hand is intended to stimulate scientific discourse and on the other hand is also intended to form the basis for further scientific work. Based on this definition, a wide variety of highly topical application examples of various softsensors in the field of forming technology are given

Monitoring the evolution of dimensional accuracy and product properties in property-controlled forming processes

As recent trends in manufacturing engineering disciplines show a clear development in the sustainable as well as economically efficient design of forming processes, monitoring techniques have been gaining in relevance. In terms of monitoring of product properties, most processes are currently open-loop controlled, entailing that the microstructure evolution, which determines the final product properties, is not considered. However, a closed-loop control that can adjust and manipulate the process actuators according to the required product properties of the component will lead to a considerable increase in efficiency of the processes regarding resources and will decrease postproduction of the component. For most forming processes, one set of component dimensions will result in a certain set of product properties. However, to successfully establish closed-loop property controls for the processes, a systematic understanding of the reciprocity of the dimensions after forming and final product properties must be established. This work investigates the evolution of dimensional accuracy as well as product properties for a series of forming processes that utilize different degrees of freedom for process control