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

作　　者：Juri Martschin Malte Wrobel Joshua Grodotzki Thomas Meurer A.Erman Tekkaya 

机构地区：[1]Institute of Forming Technology and Lightweight Components,TU Dortmund University,Baroper Str.303,44227 Dortmund,Germany [2]Institute of Mechanical Process Engineering and Mechanics,Karlsruhe Institute of Technology(KIT),Hertzstr.16,76187 Karlsruhe,Germany

出　　处：《Automotive Innovation》2023年第3期352-363,共12页汽车创新工程（英文）

基　　金：support of project 424334660 in the Collaborative Research Centre SPP2183“Property-controlled forming processes”(German:Eigenschaftsgeregelte Umformprozesse).

摘　　要：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 vari-ables.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.

关 键 词：Press hardening Property control Soft sensor Artificial neuronal network Dynamic mode decomposition 

分 类 号：U46[机械工程—车辆工程]