Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping. (1st October 2017)

• Record Type: Journal Article
• Authors: Chokshi, Prasun; Dashwood, Richard; Hughes, Darren J.
• Is Part Of: Computers & structures. Volume 190(2017)
• Page Start: 162
• Page End: 172
• Publication Date: 2017-10-01
• Subjects: Artificial Neural Network -- Tailored hot stamping -- Microstructure -- Nanoindentation -- 22MnB5 boron steel -- Modelling; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2017.05.015
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: ANN based phase prediction model for tailored hot stamping has been developed. The ANN model takes into account thermal & mechanical history for doing predictions. The performance of the model was an improvement over most of the existing models. Advanced statistical techniques were used to make the ANN model robust and reliable. The model can be used for optimizing critical automotive passenger safety component. Abstract: Because of demand for lower emissions and better crashworthiness, the use of hot stamped 22MnB5 boron steel has greatly increased in manufacturing of automobile components. However, for many applications it is required that only certain regions in hot stamped parts are fully hardened whereas other regions need be more ductile. The innovative process of tailored hot stamping does this by controlling the localized microstructures through tailored cooling rates by dividing the tooling into heated and cooled zones. A barrier to optimal application of this technique is the lack of reliable phase distribution prediction model for the process. We present a novel Artificial Neural Network (ANN) based phase distribution prediction model for tailored hot stamping. The model was developed and validated using data generated from extensive thermo-mechanical physical simulation experiments and instrumented nanoindentation based phase quantification method. Advanced statistical techniques were used for preventing overfitting, for making the optimal use of available experimental data and for quantification of prediction uncertainty. The final predictions made by the ANN model during its independent validation have shown good agreement with the experimentally generated data and have a RMS prediction error of just 7.7%, which is a significant improvement over the existing models.