Comparison of online identification methods of material parameters in press straightening process
More and more automatic pressure straighteners are used on the manufacturing industry because of their high productivity and good quality. When the technologic parameters are calculated, the precision of results will be influenced by the part's material performance parameters. In this paper, three online identification methods of performance parameters were studied. They were BP neural network method, RBF neural network method and the support vector machine method. Then their identification effects were compared. It was shown that all three methods can be used to identify material performance parameters in straightening process. But later two methods have better identification precision and shorter response time than BP neural network method.
Research on straightening process model based on iteration and self-learning
Shaft parts are widely used in machinery industry, however, bending deformation often occurred when this kind of parts is being heat treated. This parts needs to be straightened in order to meet the requirement of straightness. As for the pressure straightening process, A good straightening stroke algorithm is related to the precision and efficiency of straightening process. In this paper, the relationship between straightening load and deflection during the straightening process is analyzed, and the mathematical model of the straightening process has been established. By the mathematical model, the iterative method is used to solve the straightening stroke. Compared to the traditional straightening stroke algorithm, straightening stroke calculated by this method is much more precise because it can adapt to the change of material performance parameters. Considering that the straightening method is widely used in the mass production of the shaft parts, knowledge base is used to store the data of the straightening process, and a straightening stroke algorithm based on empirical data is set up. In this paper, the straightening process control model which combine the straightening stroke method based on iteration and straightening stroke algorithm based on empirical data has been set up. Finally, an experiment has been designed to verify the straightening process control model.
Microstructure of the solder-casting zone in bridges of dental gold alloys.
The composition and structure were studied in the cast and in the solder of bridges which had failed clinically. Both materials were gold alloys but with different contents of Pt, Ag and Zn. The metallographic investigation revealed defects mostly in the solder, situated in the subsurface layer. SEM studied of the fracture surface revealed large porosites and a structure of dense parallel lines--striations--, indicating that the material had failed from fatigue. The materials in the fractured bridges were identified by micro-probe measurements. It was stated that cast material and solder material used together, were of different composition.