The coupling effect among the flow of fluid film, the frictional heat of fluid film and the thermal deformation of sealing rings is inherent in mechanical seals. The frictional heat transfer analysis was carded out to...The coupling effect among the flow of fluid film, the frictional heat of fluid film and the thermal deformation of sealing rings is inherent in mechanical seals. The frictional heat transfer analysis was carded out to optimize the geometrical parameters of the sealing rings, such as the length, the inner radius and the outer radius. The geometrical parameters of spiral grooves, such as the spiral angle, the end radius, the groove depth, the ratio of the groove width to the weir width and the number of the grooves, were optimized by regarding the maximum bearing force of fluid film as the optimization objective with the coupling effect considered. The depth of spiral groove was designed to gradually increase from the end radius of spiral groove to the outer radius of end face in order to decrease the weakening effect of thermal deformation on the hydrodynamic effect of spiral grooves. The end faces of sealing rings were machined to form a divergent gap at inner radius, and a parallel gap will form to reduce the leakage rate when the thermal deformation takes place. The improved spiral groove mechanical seal possesses good heat transfer performance and sealing ability.展开更多
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 p...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.展开更多
文摘The coupling effect among the flow of fluid film, the frictional heat of fluid film and the thermal deformation of sealing rings is inherent in mechanical seals. The frictional heat transfer analysis was carded out to optimize the geometrical parameters of the sealing rings, such as the length, the inner radius and the outer radius. The geometrical parameters of spiral grooves, such as the spiral angle, the end radius, the groove depth, the ratio of the groove width to the weir width and the number of the grooves, were optimized by regarding the maximum bearing force of fluid film as the optimization objective with the coupling effect considered. The depth of spiral groove was designed to gradually increase from the end radius of spiral groove to the outer radius of end face in order to decrease the weakening effect of thermal deformation on the hydrodynamic effect of spiral grooves. The end faces of sealing rings were machined to form a divergent gap at inner radius, and a parallel gap will form to reduce the leakage rate when the thermal deformation takes place. The improved spiral groove mechanical seal possesses good heat transfer performance and sealing ability.
文摘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.
