A self-developed electromagnetic induction-heating device was used to investigate the variation in the microstructure and properties of X80 pipeline steel in the rapid induction tempering process at different process ...A self-developed electromagnetic induction-heating device was used to investigate the variation in the microstructure and properties of X80 pipeline steel in the rapid induction tempering process at different process parameters. The effects of the tempering condition on toughness, microstructure, size and distribution of precipitates of X80 pipeline steel were observed using a metallographic microscopy and scanning electron microscopy. Compared with the samples prepared via traditional tempering techniques, results show that the samples prepared via rapid induction tempering had improved performances. When the heating temperature is 590 ℃, at a holding time of 90 s,it was found that acicular ferrite was refined, carbonite precipitation was small, and precipitates were evenly distributed in the matrix. The low-temperature impact energy, also known as the impact absorption energy, at -40 ℃ was found to be 430.5 J for the rapid induction tempering samples and 323.2 J for the traditionally tempered sample. The low-temperature impact energy at -60 ℃ was found to be 351.3 J for the rapid induction tempered sample and 312.1 J for the tradition tempering sample.展开更多
Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50...Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.展开更多
文摘A self-developed electromagnetic induction-heating device was used to investigate the variation in the microstructure and properties of X80 pipeline steel in the rapid induction tempering process at different process parameters. The effects of the tempering condition on toughness, microstructure, size and distribution of precipitates of X80 pipeline steel were observed using a metallographic microscopy and scanning electron microscopy. Compared with the samples prepared via traditional tempering techniques, results show that the samples prepared via rapid induction tempering had improved performances. When the heating temperature is 590 ℃, at a holding time of 90 s,it was found that acicular ferrite was refined, carbonite precipitation was small, and precipitates were evenly distributed in the matrix. The low-temperature impact energy, also known as the impact absorption energy, at -40 ℃ was found to be 430.5 J for the rapid induction tempering samples and 323.2 J for the traditionally tempered sample. The low-temperature impact energy at -60 ℃ was found to be 351.3 J for the rapid induction tempered sample and 312.1 J for the tradition tempering sample.
基金Foundation item:Project (2006BAB02A02) supported by the National Key Technology R&D Program during the 11th Five-year Plan Period of ChinaProject (CX2011B119) supported by the Graduated Students' Research and Innovation Fund of Hunan Province, ChinaProject (2009ssxt230) supported by the Central South University Innovation Fund,China
文摘Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.
