2018年马鞍山市某钢铁企业接噪钢轧工职业健康检查结果

目的了解钢铁企业噪声接触人员的健康状况,为企业职业健康管理和劳动者保护提供参考依据。方法对2018年马鞍山市某钢铁企业炼钢和轧钢所有2 467名接触噪声作业人员的职业健康检查结果进行收集并进行分析。结果接噪钢轧工在纯音听力、血压、尿常规和心电图异常率均有随年龄的增长而增长的趋势(均P<0.01),血常规和血清丙氨酸转氨酶(ALT)异常率随年龄的增长有下降的趋势(P<0.01);接噪钢轧工在纯音听力、血压和心电图异常率有随接噪工龄的增长而增长的趋势(均P<0.01),血常规和血清ALT随接噪工龄的增长有下降的趋势(均P<0.01);接噪男性在纯音听力、血压、尿常规、心电图和血清ALT异常率均大于女性;接触噪声同时叠加接触其他因素作业人员和单纯接触噪声人员在尿常规异常率比较,差异有统计学意义(P<0.01);不同接触种类人员在纯音听力、血压、血常规、心电图和血清ALT异常比较,差异均无统计学意义(均P>0.05)。结论应尽早有针对性的开展接噪人员防治工作,保障接噪作业人员的职业健康。

Industrial shape detecting system of cold rolling strip

A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP） shape signal processing board and the shape control model. Based on the shape detecting principle, the shape detecting roller is designed with a new integral structure for improving the precision of shape detecting and avoiding scratching strip surface. Based on the DSP technology, the DSP shape signal processing circuit board is designed and embedded in the shape detecting system for the reliability and stability of shape signal processing. The shape detecting system was successfully used in Angang 1 250 mm HC 6-high reversible cold rolling mill. The precision of shape detecting is 0.2 I and the shape deviation is controlled within 6 1 after the close loop shape control is input.

Artificial neural network prediction of mechanical properties of hot rolled low carbon steel strip

Conventionally, direct tensile tests are employed to measure mechanical properties of industrially pro- duced products. In mass production, the cost of sampling and labor is high, which leads to an increase of total pro- duction cost and a decrease of production efficiency. The main purpose of this paper is to develop an intelligent pro- gram based on artificial neural network （ANN） to predict the mechanical properties of a commercial grade hot rolled low carbon steel strip, SPHC. A neural network model was developed by using 7 x 5 x 1 back-propagation （BP） neural network structure to determine the multiple relationships among chemical composition, product pro- cess and mechanical properties. Industrial on-line application of the model indicated that prediction results were in good agreement with measured values. It showed that 99.2 % of the products＇ tensile strength was accurately pre- dicted within an error margin of ~ 10 %, compared to measured values. Based on the model, the effects of chemical composition and hot rolling process on mechanical properties were derived and the relative importance of each in- put parameter was evaluated by sensitivity analysis. All the results demonstrate that the developed ANN models are capable of accurate predictions under real-time industrial conditions. The developed model can be used to sub- stitute mechanical property measurement and therefore reduce cost of production. It can also be used to control and optimize mechanical properties of the investigated steel.

Microstructure ＆ texture evolution and magnetic properties of high magnetic-induction 6.5% Si electrical steel thin sheet fabricated by a specially designed rolling route

Electrical steel sheets with 6.5%(mas fraction) Si with good shapes and superior magnetic inductions were successfully produced by a specially designed processing route including ingot casting, hot rolling and warm rolling both with interpass thermal treatment, and final annealing. The sheets were of 0.2 mm and 0.3 mm thick over 140 mm width. A detailed study of the microstructural and textural evolutions from the hot rolling to annealing was carried out by optical microscopy, X-ray diffraction and electron backscattered diffraction. The hot rolled sheet characterized by near-equiaxed grains was dominated by the mixture of <001>//ND fiber(λ-fiber), <110>//RD fiber(α-fiber) and <111>//ND fiber(γ-fiber) textures owing to the partial recrystallization and strain induced boundary migration(SIBM) during the hot rolling interpass thermal treatment. The static recovery and SIBM during the warm rolling interpass thermal treatment result in large and elongated warm rolling grains. The warm rolling texture is dominated by obvious λ, Goss and strong γ-fiber textures. The application of the interpass thermal treatment during hot and warm rolling significantly enhances the impact of SIBM during annealing, which is responsible for the formation of the moderate λ-fiber, some near-λ fiber texture components and the obviously weakened γ-fiber texture in the annealed sheet, leading to a higher magnetic induction compared to the commercially produced 6.5% Si steel by chemical vapor deposition(CVD).

Parametric analysis of mixed lubrication characteristics in work zone of strip rolling

A theoretical model for mixed lubrication with more accurate contact length has been developed based on the average volume flow model and asperity flattening model,and the lubricant volume flow rate and outlet speed ratio are determined by integrating differential equations based on rolling parameters.The lubrication characteristics at the roll-strip interface with different surface roughness,rolling speed,reduction and lubricant viscosity are analyzed respectively.Additionally,the average volume flow rates of lubricant under different rolling conditions are calculated and used to explain the change rule of lubrication characteristics.The developed scheme is able to determine the total pressure,lubricant pressure,film thickness and real contact area at any point within the work zone.The prediction and analysis of mixed lubrication characteristics at the interface is meaningful to better control the surface quality and optimize the rolling process.