Prediction of maximum carbon element content in continuous casting billets of 82B cord steel based on statistics of extreme values method

The research of carbon content along the casting direction of 82B cord steel billets is of great significance for improvingthe quality of cord products from subsequent processing.However,the traditional segregation and billets quality evaluationmethods have certain limitations,such as sampling length and analysis area.which affect the accuracy of quality judgment.Thus.the statistics of extreme values(SEV)was introduced to predict the maximum value of carbon element contentalong the casting direction,which can quantitatively characterize the segregation degree.The size of the selected billet is150 mm×150 mm,and the sampling location is the centerline of the billet.The experiment was conducted by consideringthe effect of cooling intensity and casting speed on the maximum value of carbon element content.Firstly,the calculationresults show that the SEN method can predict the maximum value of carbon element content along the casting directionof 82B cord steel,and the SEV method is proved to be effective by analyzing the carbon distribution and fluctuation in billets.To some extent,the SEV method can break the limitations of the sampling length and analysis area by predicting themaximum value of carbon element on a larger range of continuous casting billets with few samples.During the continuouscasting process the increase in cooling intensity makes the surface shrinking rate increase,which can slow down the flowof solute-enriched liquid to the center,and the center segregation can be reduced.On the other hand,the function area ofthe final electromagnetic stirring can be expanded with the increase in the casting speed,which can reduce the concentration of carbon element in the center of the billets and reduce the maximum value of carbon element content.Ilt can providea new theoretical reference for the quantitative calculation of carbon content in continuous casting billets and the qualityevaluation of continuous casting billcts.

Estimation of maximum inclusion by statistics of extreme values method in bearing steel

A statistic method, statistics of extreme values （SEV）, was described in detail, which can esti mate the size of maximum inclusion in steel. The characteristic size of the maximum inclusion in a high clean bearing steel （GCrl5） was evaluated by this method, and the morphology and corn position of large inclusions found were analyzed by scanning electron microscopy （SEM）. When standard inspection area （S0） is 280 mm2, the characteristic size of the biggest inclusion found in 30 standard inspection area is 23.93 μm, and it has a 99.9% probability of the characteristic size of maximum inclusion predicted being no larger than 36.85μm in the experimental steel. SEM result shows that large inclusions found are mainly composed of CaS, calcium-aluminate and MgO. Compositing widely exists in large inclusions in high clean bearing steel. Compared with traditional evaluation method, SEV method mainly focuses on inclusion size, and the esti- mation result is not affected by inclusion types. SEV method is suitable for the inclusion eval uation of high clean bearing steel.

Gigacycle Fatigue Behavior of 1800 MPa Grade High Strength Spring Steel for Automobile Lightweight

Gigacycle fatigue behavior of 60Si2CrVA high strength spring steel was investigated by ultrasonic fatigue test machine. Fatigue fractography was observed by scanning electron microscopy （SEM）. Maximum inclusion sizes and fatigue strength in different volumes were estimated by statistics of extreme values （SEV） and generalized Pareto distribution （GPD） methods. The results showed that S N curves of 60Si2CrVA spring steels for two rolling processes were not horizontal asymptotes but a gradient in a regime of 109 cycles, and traditional fatigue limits were eliminated. Surface machined topography and inclusions in steel were major factors that led to elimination of fatigue limit for 60Si2CrVA spring steel. The SEV and GPD methods could effectively predict size of the maximum inclusion and fatigue strength in different volumes of 60Si2CrVA spring steel. Predicted fatigue strength was in accordance with experimental results by ultrasonic fatigue testing.