Phase transformations in a simulated hot stamping process of the boron bearing steel

Nikravesh, M.; Naderi, M.; Akbari, G. H.; Bleck, Wolfgang

doi:10.1016/j.matdes.2015.06.108

Cited by 53 publications

(41 citation statements)

References 28 publications

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“…In order to develop this ANN based model, extensive thermomechanical experiments were performed to physically simulate the thermal and mechanical conditions which the different regions of a tailor hot stamped component undergo during their processing [15,20,[31][32][33]. The phase distributions in the final microstructures of different thermo-mechanical test samples were quantified using cutting edge scanned surface instrumented nanoindentation technique [34][35][36][37].…”

Section: Research Approach and Theorymentioning

confidence: 99%

“…This excellent performance of the final ANN model with an RMS error of just 7.7% for phase fraction prediction over the test dataset establishes that the final ANN model has indeed robustly learned the functional relationship between the thermal history, deformation amount and deformation temperature and the final resulting phase distribution in the boron steel. Thermal history, deformation amount and deformation temperature are the factors which have the greatest influence on the final phase distribution during tailored hot stamping and hence were used for development of the current ANN model [8,10,12,15,35,36,48,49]. There are other process parameters such as strain rate, austenitization temperature and austenitization time which also have limited influence on the final phase distribution during tailored hot stamping.…”

Section: Model Validation and Performance Analysismentioning

confidence: 99%

“…The final phase distribution which results in the different regions of a tailor hot stamped part is dependent on both the thermal and mechanical history of that region. Several papers in the literature have investigated and reported the significant effect of the deformation on the final phase distribution during hot stamping [12][13][14][15]. The majority of early numerical models that have been developed for final phase distribution prediction during hot stamping have had limited success because of their inability to account for the effect of deformation [9,[16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping

Chokshi

Dashwood

Hughes

2017

Computers & Structures

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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.

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Section: Research Approach and Theorymentioning

confidence: 99%

Section: Model Validation and Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping

Chokshi

Dashwood

Hughes

2017

Computers & Structures

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“…Nikravesh, et al [4] simulated the hot stamping process by a deformation dilatometer to investigate the phase transformations. Wu, et al [5] developed a coupled 3D thermomechanical phase transformation finite element simulation of the hot stamping process.…”

Section: Introductionmentioning

confidence: 99%

Microstructure Evolution and Simulation in 22MnB5 Steel during Hot Stamping

Hu¹,

Zhou²,

Rong-dong³

et al. 2018

MSCE

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Hot stamping components with 1500 MPa ultra-high strength are obtained by press hardening during hot stamping, and the properties depend on the microstructures. It is very important that the microstructure evolution rule is found out during hot stamping process. To characterize the microstructure evolution during hot stamping, a method combining finite element and experiment is carried out. Samples were heated to 950˚C and held for 300 second at a induction heating furnace, then taken out from the furnace and stayed in the air at different time (7 s, 11 s, 13 s, 22 s), respectively, finally the specimens were formed and quenched at a die. Microstructural observation as well as surface hardness profiling of formed specimens was performed. And the numerical simulation to predict the austenite transformation into ferrite, pearlite, bainite, and martensite and the volume fraction of each phase during the hot stamping process was made with ABAQUS software. The results show that the ferrite is observed when the specimen stays in the air for 22 s, and the temperature drops to 325˚C when the dwell time increases from 7 s to 22 s. The results of numerical simulation and experimental results are in good agreement. So the method finite element can be used to guide the optimization of hot stamping process parameters.

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“…The investigation of Rohde et al [6] also showed that different phase fraction of ferrite, bainite and martensite are formed dependent on the thermal history of the workpieces. According to the study by Nikravesh et al [7] the critical cooling rate increases from approximately 15°C/s to 60°C/s by inducing deformation. In case of ferrite and pearlite, it is reported that the deformation of austenite increases the density of ferrite nucleation sites due to the increase in austenite grain boundary surface and the higher density of dislocations, which raises the free energy of the austenite and leads to an increased driving force for the austenite-ferrite transformation.…”

Section: Introductionmentioning

confidence: 99%

Extension of a Phase Transformation Model for Partial Hardening in Hot Stamping

Hart-Rawung

Buhl

Bambach�

2018

Journal of Machine Engineering

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The quality of predicted microstructural and mechanical properties in hot stamping simulations relies considerably on the material model. Many researchers studied the effect of the plastic deformation on the phase transformation of the most commonly used hot stamping steel 22MnB5, and proved that the deformation applied at high temperature promotes the formation of ferrite, pearlite and bainite. This behaviour has to be integrated into materials modelling. In this study, the effect of pre-strain on the phase transformation of the material is considered. The specimens are heated to austenitization temperature, isothermally deformed at 700 °C, and quenched down to room temperature. The phase fractions and the temperature-dilatation behaviour obtained from the experiments are used to calibrate the material model. By using the experimental data obtained from dilatometer testing, the accuracy of the material model is evaluated. Additionally, an attempt to predict the results between the tested data points by using interpolation was made and compared with the simulation results.

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Phase transformations in a simulated hot stamping process of the boron bearing steel

Cited by 53 publications

References 28 publications

Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping

Artificial Neural Network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping

Microstructure Evolution and Simulation in 22MnB5 Steel during Hot Stamping

Extension of a Phase Transformation Model for Partial Hardening in Hot Stamping

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