Novel Method for Predicting Hardness Distribution of Hot-Stamped Part Using FE-Simulation Coupled with Quench Factor Ana
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IN the automotive industry, hot stamping technology has increasingly been adopted for manufacturing ultrahigh-strength part to meet requirements such as weight reduction, improving fuel efficiency, and better crash performance.[1] During this process, austenitized blanks are formed and subsequently quenched in the watercooled die, which dramatically changes their mechanical properties because of the austenite to martensite phase transformation.[2] Mechanical properties of the hotstamped part such as hardness and strength are influenced by various process parameters such as the austenitizing condition, blank thickness, forming temperature, and quenching duration.[3] All these factors affect the resultant mechanical properties through the effect of cooling rate on microstructural transformation. A precise prediction of the mechanical properties of the formed piece will facilitate optimization of the process design to obtain the desired material characteristics prior to production development.[4] It is also possible to DAE-HOON KO, Researcher, is with the Department of Special Steel Technology Development, Hyundai-Steel Corporation, Dangjin, Chungnam 343-711, South Korea. DAE-CHEOL KO, Associate Professor, is with the Industrial Liaison Innovation Center, Pusan National University, Pusan 609-735, South Korea. BYUNG-MIN KIM, Professor, is with the Department of Mechanical Engineering, Pusan National University, Pusan 609-735, South Korea. Contact e-mail: [email protected] Manuscript submitted July 21, 2014. Article published online July 9, 2015. 2072—VOLUME 46B, OCTOBER 2015
optimize a component with multi-strength or functional mechanical properties by using a partial quenching, tailor welded blank (TWB), or tailor rolled blank (TRB) in hot stamping.[5] Therefore, a numerical or an analytical method for predicting the mechanical properties is definitely required in the hot stamping process. Kirkaldy and Venugopalan[6] developed a model (K–V model) which predicts the microstructure in terms of the formation of ferrite, pearlite, bainite, and martensite based upon the kinetic equation. This model has been widely used to predict the microstructure evolution and hardness during the welding process for steels. Watt et al.[7] established an algorithm for modeling microstructural development in heat-affected zones during welding operation. And then, Henwood et al.[8] further developed Watt’s algorithm and coupled it to a transient finite element heat transfer analysis for computing the microstructure of heat-affected zone in the welding process of low alloy steel. Also, Li et al.[9] investigated the use of the K–V model for microstructure prediction for the case of isothermal transformation based on the time–temperature–transformation (TTT) diagram. Then, Li et al. (Li’s model) modified the K–V model to improve the accuracy of the results by using the continuous cooling transformation (CCT) diagram instead of the TTT diagram. In addition, Akerstro¨m and Oldenburg[5] established a more effective model (A–O model) through the mod
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