location: Current position: Home >> Scientific Research >> Paper Publications

Investigation on Microstructure and Martensitic Transformation Mechanism for the Warm-Stamped Third-Generation Automotive Medium-Mn Steel

Hits:

Indexed by:期刊论文

Date of Publication:2017-10-01

Journal:JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY-TRANSACTIONS OF THE ASME

Included Journals:Scopus、SCIE、EI

Volume:139

Issue:4

ISSN No.:0094-4289

Key Words:warm-stamping; medium-Mn steel; martensitic transformation; process parameters

Abstract:With the development of the automotive industry, the application of the high-strength steel (HSS) becomes an effective way to improve the lightweight and safety. In this paper, the third-generation automotive medium-Mn steel (TAMM steel) is studied. The warm-stamped TAMM steel holds the complete and fine-grained martensitic microstructure without decarbonization layer, which contributes to high and well-balanced mechanical properties. Furthermore, the martensitic transformation mechanism of the TAMM steel is investigated by the dilatation tests. The results indicate that the effects of the loading method on the M-s temperature under different loads are different. The M-s temperature is hardly influenced under the tensile loads and low compressive load. However, it is slightly decreased under the high compressive load. Moreover, the effects of the strain and strain rate on the M-s temperature are insignificant and can be neglected. As a result, this research proves that the martensitic transformation of the TAMM steel is rarely influenced by the process parameters, such as stamping temperature, loading method, load, strain, and strain rate. The actual stamping process can be designed and controlled accurately referring to the continuous cooling transformation (CCT) curves to realize the required properties and improve the formability of the automotive part.

Pre One:Effect of welding parameters on tensile strength of ultrasonic spot welded joints of aluminum to steel - By experimentation and artificial neural network

Next One:Bending of sheet aluminum alloy assisted by arc pretreatment