模具
厚板
材料科学
铸造
流量(数学)
壳体(结构)
连铸
喷射(流体)
机械
强度(物理)
复合材料
电流(流体)
冶金
机械工程
作者
Pu Wang,Hong Xiao,Xi-qing Chen,Wei-hong Li,Bing Yi,Hai-yan Tang,Jia-quan Zhang
标识
DOI:10.1007/s11663-022-02478-6
摘要
For the application of a novel in-mold multi-poles electromagnetic rotative stirring (EMRS) instrumentation, a coupled three-dimensional numerical model is established to study the effect of EMRS on the metallurgical behavior in the mold of 2150 mm × 230 mm size slab casting. The model is validated experimentally through the measurement of magnetic flux density and electromagnetic force. It has been proved that both the magnetic flux density and electromagnetic force produced by the in-mold multi-poles traveling wave stirring are mainly concentrated in front of the initial solidified shell along the mold wide sides especially at an optimal frequency of 4 Hz, which can produce a beneficial horizontal flow pattern for interstitial-free steels to wash away any hooked inclusions and/or bubbles under the meniscus. When the current intensity increases from 0 to 400 A, six swirl flows are observed in the cross-section of the mold stirrer center, the jet flow impinging depth decreased by 162 mm, and the tangential velocities of fluid flow on the solidification front increased by 0.126 and 0.120 m s−1 on the narrow and wide sides, respectively, which should be the key reasons for the washing and floating removal of the locally hooked inclusions. Meanwhile, the level fluctuation and shell thickness on the narrow side of mold decreased at first but increased later with an increasing current. A comprehensive evaluation method for the mold metallurgical behavior of EMRS is proposed based on the results from the numerical model and the statistical analysis of defect ratio in actual steel productions. It suggests that the optimum stirring current intensity is 300 A, which can cut the defect ratio of the hot rolled plates down to the lowest value of 0.06 pct while produced by the slab continuous casting process.
科研通智能强力驱动
Strongly Powered by AbleSci AI