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The hot rolled strip thickness accuracy sometimes can not be guaranteed after a long time for waiting for slabs or other reasons. The reason is when rolling, the rollgap adaptive model has considered the thermal expansion of the roll. So when rolling is restarted, thermal expansion of the rolls must be cleared in order to accurately calculate the setup rollgap value. The finite element software ANSYS is used to calculate the temperature field and thermal expansion amount of the rolls in the rolling process. Application results show that this method can improve the accuracy of strip thickness.

Using the characteristics of prediction model, rolling optimization and feedback correction, a AWC system based on generalized predictive control was designed, and its control performance was simulated based on a hot strip continuous mill. The results show that generalized predictive controller achieves better control effects than the normal PID on response time and steady precision with matching model; when model mismatching is caused by inaccuracy of plastic coefficient and pure delay time, the normal PID is overshot or even oscillation, but the control performance of the generalized predictive controller is not influenced by model parameter variations .

Aimed at the main factors of hot rolled strip steel product width fluctuation, rolled piece is put forward to plasticity coefficient and entrance width as the input variables of feedforward AWC control algorithm.Expounds a kind of optimization of plasticity coefficient Q feedforward AWC control system principle and plasticity coefficient Q of linear prediction algorithm.Field application shows that the Q prediction feedforward AWC has significant effect on inhibition of hot rolled strip width deviation.

Thickness model adaption plays a important role in obtaining strip with high thickness precision. In order to improve the thickness precision, a complete thickness model adaption system has been designed for every specific model used in the setup system including gap position model, temperature model, rolling force model. Snapshot data and stretch equation are used to adapt gap position. Forced convection coefficient is selected as adaption parameter. A deformation resistance-based fitting quadratic curve is proposed in rolling force adaption, it can be inherited to any other thick range class and a quick thickness adaption method is introduced. Application results show that this adaptive system is with high accuracy, quick adaption and high stability.

The accuracy of roll force model and the rationality of roll fore adaptive model play a key role in obtaining the thickness of strip with high precision. The roll force model has been established. It includes the elastic flattened roller model and deformation resistance model considering the chemical composition of strip. A deformation resistance-based fitting curve is proposed in rolling force adaption, it can be inherited to any other thick range class. Application results show that the rolling force model and its adaptation are with high prediction accuracy and it has improved the strip thickness accuracy.

Using the characteristics of prediction model, rolling optimization and feedback correction, a AWC system based on explicit indirect predictive control was designed, and its control performance was simulated based on a hot strip continuous mill. The results show that explicit indirect predictive control achieves better control effects than the normal PID on response time and steady precision with matching model; when model mismatching is caused by inaccuracy of plastic coefficient and pure delay time, the normal PID is overshot or even oscillation, but the control performance of the explicit indirect predictive control is not influenced by model parameter variations [1].

Aptamer was modified the gold nanoparticle (AuNP) to form stable aptamer-AuNP probe that was not gathered in the pH 7.2 2-[4-(2-hydroxyethyl)-1-piperazinyl]ethanesulfonic acid (HEPES) buffer solution and in the presence of NaCl. The Ag+ react with the aptamer-AuNP probe to fold a hairpin structure complex of Ag+-aptamer and release AuNPs that were aggregated to large particles, which lead to resonance Rayleigh scattering (RRS) peak at 596 nm enhancement. The enhanced value ΔI596nm is linear to Ag + concentration in the range of 6.7×10-8-1.33×10-6 mol/L. Thus, a new RRS methods were proposed for detection of Ag+, with high sensitivity, good selectivity and simplicity.

In the automotive industry, the hot forming of high strength steels offers the possibility to obtain significant reduction of weight without affecting the structural performances of final products. Compared with conventional sheet metal forming, the proper design of hot stamping process chain requires the deep knowledge of both interface phenomena and material behavior at high temperatures in order to obtain the desired properties of final products in terms of microstructure and strength characteristics. The work presented in this paper aims at accurately evaluate the effect of heating parameters on the properties of final sheet components produced in hot forming operations. Different from that in the lab, all the samples and parts used for the experimental test were produced in the production line, which can objectively show the manufacturing properties and microstructure character of products in mass. Microstructure evaluation, hardness measurement and dimensional accuracy test after hot stamping were performed and considered. The best heating parameters for the researched hot stamping B-Pillar’s production were obtained through the above research.