查看更多>>摘要：Breakout is the most serious production accident in continuous casting and must be detected and predicted by stable and reliable methods.The sticking region,which forms on the local copper plate and expanded into a"V"shape,is the typical precursor of breakout.Therefore,computer vision technology was exploited to visualize the temperature change rate of the copper plate based on the temperature signals from thermocouples;then,the static and dynamic features of the abnormal sticking region were extracted.Meanwhile,logistic regression and Adaboost models were used to study and identify these features,resulting in the development of a mold breakout prediction model based on computer vision and machine learning.The test results demonstrate that the proposed model can effectively distinguish anomalous temperature patterns and considerably reduce false alarms without any missing reports.As a result,the proposed method could offer valuable insights into the realm of abnormality detection and prediction during continuous casting process.
查看更多>>摘要：The distribution of inclusions at the bottom of a Ce-treated heavy steel ingot was detected and calculated.The three-dimensional morphology and spatial distribution of CeAlO3 clusters were characterized using the electrolytic extraction and Micro-CT detection.A model of inclusion collision to predict the aggregation of CeAlO3 inclusions in the ingot was established and validated by measured results.Inclusions were mainly CeAlO3 and a small amount of Ce2O2S in the tundish after cerium treatment.The collision and aggregation of inclusions led to the formation of large clusters in the ingot during the solidification process.Large slag entrainment inclusions,large CeAlO3 clusters and small CeAlO3 particles were observed from the center to the edge of the ingot bottom.Large inclusions were mainly concentrated at the center.The number density of inclusions larger than 200 μm was 0.21 mm-3.The maximum diameter of CeAlO3 clusters was 1340 μm.From the edge to the radial center and from the bottom to the top,the average diameter of inclusions gradually increased due to the longer solidification time of the ingot.
查看更多>>摘要：The evolution of microstructure,texture,and magnetic properties with random texture,near-copper texture,weak near-cube texture,and strong λ fiber(＜001＞//ND(normal direction))before rolling of non-oriented electrical steel was studied.Three recrystallized hot bands with different textures but similar grain sizes were prepared by pre-annealing at low-temperature and high-temperature normalization annealing.It was observed that the final annealed products exhibited similar recrystallized microstructures.By contrast,the final annealed product with more λ fiber before rolling exhibited a stronger cube texture.With the λ fiber before rolling becoming stronger,the proportion of {111}＜110＞deformed matrices became larger,which could be observed in the early recrystallization stage.The overwhelmingly dominant λ orientation nuclei are formed in the {111}＜110＞deformed matrix and become the dominant texture.Eventually,the best magnetic properties are obtained in the products with strong λ fiber before rolling,corresponding to the strong cube texture and low anisotropy parameter.
查看更多>>摘要：The effect of magnesium treatment and calcium treatment on the microstructure and mechanical properties of industrial H13 steel after quenching and tempering was investigated.The impact toughness and tensile tests were mainly carried out,and the microstructure was observed by scanning electron microscopy,electron backscattered diffraction,and X-ray diffraction.The results show that magnesium treatment is still feasible in industrial trials.It is mainly manifested in the refinement of lath martensite and carbides.Compared with calcium treatment,the prior austenite grains and carbides size of industrial H13 steel treated with magnesium decreased by 3.17 μm after quenching.After quenching and tempering,the carbides(especially V-rich carbides)in Mg treatment obviously spheroidized and distributed uniformly and increased in quantity significantly.The lath martensite size is reduced from 2.45 to 2.31 μm.This suggests that magnesium treatment was able to yield smaller grains and more evenly distributed carbides.Moreover,the impact toughness,yield strength,and ultimate tensile strength of industrial H13 steel with magnesium treatment increased by 28％,65.5 MPa and 123.7 MPa,respectively.The increment of strength mainly comes from dislocation strengthening,grain refinement strengthening,and precipitation strengthening,among which precipitation strengthening accounts for the largest proportion.
查看更多>>摘要：The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(VRA)is challengeable to directly predict due to the complicated relationships between the chemical composition and process(like quenching temperature(QT))-A Gaussian process regression model in machine learning was developed to predict VRA,and the model accuracy was further improved by introducing a metallurgical parameter of martensite fraction(fα')to accurately predict VRA in Q&P steels.The developed machine learning model combined with Bayesian global optimization can serve as another selection strategy for the quenching temperature,and this strategy is very efficient as it found the"optimum"QT with the maximum VRA using only seven consecutive iterations.The benchmark experiment also reveals that the developed machine learning model predicts VRA more accurately than the popular constrained carbon equilibrium thermodynamic model,even better than a thermo-kinetic quenching-partitioning-tempering-local equilibrium model.
查看更多>>摘要：The mechanical properties of the sample and the stability of retained austenite were studied by designing two kinds of ultra-fine bainitic steel with different heat treatment methods(austempering above and below Ms(martensite start tem-perature)),which were subjected to tensile tests at 20 and 450 ℃,respectively.The results show that compared to room temperature(20 ℃)tensile properties,the uniform elongation of the sample at high temperature(450 ℃)significantly decreased.Specifically,the uniform elongation of the sample austempered above Ms decreased from 8.0％to 3.5％,and the sample austempered below Ms decreased from 10.9％to 3.1％.Additionally,the tensile strength of the sample austempered above Ms significantly decreased(from 1281 to 912 MPa),and the sample austempered below Ms slightly decreased(from 1010 to 974 MPa).This was due to the high carbon content(1.60 wt.％),high mechanical stability,low thermal stability for the retained austenite of the sample austempered below Ms.Besides,the retained austenite decomposed at high temper-atures,the carbon content and transformation driving force were significantly reduced,the transformation rate increased,and the phase transformation content reduced.
查看更多>>摘要：The effect of spatial temperature gradient on the microstructural evolution of a 308L stainless steel during the directed energy deposition(DED)process was experimentally investigated.A novel cooling system was designed and incorporated to a DED system in order to control the temperature gradient along the deposition direction during solidification.During deposition,the workpiece was placed on a lifting platform,and as the deposition process proceeded,the platform and workpiece were gradually lowered into cooling water so that the temperature gradient along the deposition direction could be controlled and maintained stable during the deposition process.The microstructure characterization results indicated that a deposition strategy with higher G and G/R values(where G is temperature gradient and R is solidification rate)produced finer cellular grains that were better aligned with the deposition direction,while a deposition strategy with lower G and G/R values produced columnar grains with larger primary arm spacing and less aligned with the deposition direction.
查看更多>>摘要：The evolution of the microstructure and toughness of APL5L X80 pipeline steel after thermal welding simulation was investigated by X-ray diffraction,electron backscatter diffraction,and transmission electron microscopy.The results indicated that primary heat-affected zones can be divided into weld,coarse-grained,fine-grained,intercritical,and sub-critical zones.The microstructure of the weld zone is mainly composed of bainitic ferrite and a small amount of granular bainite;however,the original austenite grains are distributed in the columnar grains.The structure of the coarse-grained zone is similar to that of the weld zone,but the original austenite grains are equiaxed.In contrast,the microstructure in the fine-grained zone is dominated by fine granular bainite,and the effective grain size is only 8.15 μm,thus providing the highest toughness in the entire heat-affected zone.The intercritical and subcritical zones were brittle valley regions,and the microstructure was dominated by granular bainite.However,the martensite-austenite(M/A)constituents are present in island chains along the grain boundaries,and the coarse size of the M/A constituents seriously reduces the toughness.The results of the crack propagation analyzes revealed that high-angle grain boundaries can significantly slow down crack growth and change the crack direction,thereby increasing the material toughness.The impact toughness of the low-temperature tempering zone was equivalent to that of the columnar grain zone,and the impact toughness was between those of the critical and fine-grained zones.
查看更多>>摘要：Online estimation of the double nugget diameters was performed by means of a back propagation neural network.The double nugget diameters were obtained using actual welding experiment and numerical simulation,according to different characteristics of aluminum nugget and steel nugget.The input of the neural network was some key characteristic parameters extracted from dynamic power signal,which were peak point,knee point and their variation rate over time,as well as heat energy delivered into the welding system.The architecture of the neural network was confirmed by confirming the number of neurons in hidden layer through a series of calculations.The key parameters of the neural network were obtained by means of training 81 arrays of data set.Then,the neural network was used to test the remaining 20 arrays of verifying data set,and the results showed that both of the mean errors for the two nugget diameters were below 3％.In addition,corresponding analyses showed that the accuracy of two nugget diameters was higher than that of tensile-shear strength.
查看更多>>摘要：NiCoFe alloy,a medium-entropy alloy,shows potential for applications in extreme environments.However,there is a theoretical barrier concerning the unclear understanding of its high-temperature dislocation motion mechanism.The load response exhibits distinct signatures relevant to thermal activation,most notably a decrease in critical force(i.e.,softening)from cryogenic to elevated temperatures,e.g.,from 200 to 1000 K.The onset of plasticity is characterized by the nucleation of stacking faults and prismatic loops at low temperatures,whereas the surface nucleation of Shockley partial dislocations dominates plasticity at elevated temperatures.We show that thermal effects lead to non-uniform atom pile-ups and control the rate of phase transformation with increasing indentation depth.The findings in this work extend the understanding of the mechanical response of NiCoFe alloys under indentation at different temperatures,shedding light on the underlying dislocation motion mechanisms and surface deformation characteristics.The observed transformation-induced plasticity mechanism has implications for the properties of medium-entropy alloys and their potential applications in extreme environments.
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