针对马钢150×150 mm 2断面小方坯在生产Nb微合金化冷镦钢时低倍角裂问题,通过热酸侵低倍试验分析,讨论了铸坯低倍角裂的影响因素及改进措施。结果表明,通过采取结晶器铜管进行优化设计、结晶器振动改善,二次冷却优化、过程增氮控...针对马钢150×150 mm 2断面小方坯在生产Nb微合金化冷镦钢时低倍角裂问题,通过热酸侵低倍试验分析,讨论了铸坯低倍角裂的影响因素及改进措施。结果表明,通过采取结晶器铜管进行优化设计、结晶器振动改善,二次冷却优化、过程增氮控制等措施,使得Nb微合金化冷镦钢低倍角裂评级≤1.5级比例由64.1%提高至95.8%,低倍质量得到有效改善。展开更多
Microseismicity signals released during rock failure process are firstly recorded using microseismicity monitoring system.A wavelet transform scheme is then developed on the basis of the discrete wavelet transform and...Microseismicity signals released during rock failure process are firstly recorded using microseismicity monitoring system.A wavelet transform scheme is then developed on the basis of the discrete wavelet transform and implemented into MATLAB to study the energy distribution characteristics of the monitored microseismicity signals.The wavelet transform scheme decomposes the recorded microseismicity signals into various wavelets at seven scales and eight frequency bands.The microseismicity energy at each frequency band is then calculated by integrating the wavelets in each scale.It is found that,for the microseismicity signals recorded during the uniaxial loading of the granite,the microseismicity energies are mainly distributed between the bands 7.8125-15.625 kHz,15.625-31.250 kHz and 31.25-62.5 kHz and the percentages of the released energies at these frequency bands are 8.24%,62.72%,28.08% of the total energies,respectively.The results reveal that the microseismicity energies at these levels are directly related to the damage mechanisms of the granite although further studies are need to identify the failure modes.Then these monitored signals were processed using wavelet transformation to find out the frequency distribution rule and the frequency band energy varying rule of the acoustic emission(AE) signals during the different rock damage and failure stages.The rock failure mechanism was interpreted from the perspective of the relationship between AE signal frequency change and crack propagation.The frequency band energy distribution histograms of the microseismicity signals at different damage stages were computed and drawn by the energy calculation method of wavelet transformation implemented into MATLAB.The energy percentage of the low frequency band(d7-a7) and that of the dominated frequency band(d4-d6) and their variation rule were analyzed especially.Accordingly,the critical damage point is that the low frequency energy percentage is above a certain threshold.This index could be used as the failure precursor criterion for rock mass instability monitoring and early warning,since it provided a theoretical guidance for evaluating internal damage of rock.Finally,it is concluded that the proposed wavelet transform method may provide a new mean for the characteristics analysis of the microseismicity signals recorded by the microseismicity monitoring system and may stimulate the application of the microseismicity monitoring technology in the geotechnical and mining engineerings through analyzing the energy of the microseismicity signals to understand the law of microseismicity emitted by rockmass.展开更多
文摘针对马钢150×150 mm 2断面小方坯在生产Nb微合金化冷镦钢时低倍角裂问题,通过热酸侵低倍试验分析,讨论了铸坯低倍角裂的影响因素及改进措施。结果表明,通过采取结晶器铜管进行优化设计、结晶器振动改善,二次冷却优化、过程增氮控制等措施,使得Nb微合金化冷镦钢低倍角裂评级≤1.5级比例由64.1%提高至95.8%,低倍质量得到有效改善。
基金partially supported by the National Natural Science Foundation of China (Grant Nos.51204029, 51525402,51374049,51474050 and U1602232)the Science Career Public Welfare Research Foundation of Liaoning Province (No.2015003001)the Scientific Research Foundationfor the Returned Overseas Chinese Scholars,State Education Ministry (No.50-2).
文摘Microseismicity signals released during rock failure process are firstly recorded using microseismicity monitoring system.A wavelet transform scheme is then developed on the basis of the discrete wavelet transform and implemented into MATLAB to study the energy distribution characteristics of the monitored microseismicity signals.The wavelet transform scheme decomposes the recorded microseismicity signals into various wavelets at seven scales and eight frequency bands.The microseismicity energy at each frequency band is then calculated by integrating the wavelets in each scale.It is found that,for the microseismicity signals recorded during the uniaxial loading of the granite,the microseismicity energies are mainly distributed between the bands 7.8125-15.625 kHz,15.625-31.250 kHz and 31.25-62.5 kHz and the percentages of the released energies at these frequency bands are 8.24%,62.72%,28.08% of the total energies,respectively.The results reveal that the microseismicity energies at these levels are directly related to the damage mechanisms of the granite although further studies are need to identify the failure modes.Then these monitored signals were processed using wavelet transformation to find out the frequency distribution rule and the frequency band energy varying rule of the acoustic emission(AE) signals during the different rock damage and failure stages.The rock failure mechanism was interpreted from the perspective of the relationship between AE signal frequency change and crack propagation.The frequency band energy distribution histograms of the microseismicity signals at different damage stages were computed and drawn by the energy calculation method of wavelet transformation implemented into MATLAB.The energy percentage of the low frequency band(d7-a7) and that of the dominated frequency band(d4-d6) and their variation rule were analyzed especially.Accordingly,the critical damage point is that the low frequency energy percentage is above a certain threshold.This index could be used as the failure precursor criterion for rock mass instability monitoring and early warning,since it provided a theoretical guidance for evaluating internal damage of rock.Finally,it is concluded that the proposed wavelet transform method may provide a new mean for the characteristics analysis of the microseismicity signals recorded by the microseismicity monitoring system and may stimulate the application of the microseismicity monitoring technology in the geotechnical and mining engineerings through analyzing the energy of the microseismicity signals to understand the law of microseismicity emitted by rockmass.
