Study On The Of Process Parameters Optimization Of Hot Dip Aluminized Steel

Metallic corrosion is a problem which worries the whole world.The annual cost of corrosion is very high.Hot dip aluminizing has been used widely in many fields for its simplicity of manufacturing,relatively low cost,and especially for its outstanding corrosion resistance and high temperature oxidation resistance.However,the quality of hot dip aluminizing materials varies largely due to the difference of aluminizing process.This paper focuses on the optimization of process parameters to improve the corrosion resistance of hot dip aluminizing.At the same time,the influence of welding on the corrosion resistance of the layer is also studied.In this paper,the types,quality characteristics and the process of aluminizing is analyzed.The process flow of hot dip aluminizing is completed.Then,factors that may affect the thickness of aluminizing layer in aluminizing process is analyzed,and temperature for dipping,time for dipping,temperature for diffusing,and time for diffusing are chosen as the main improved parameters of this paper.Each process parameters are divided into 5 different levels,and 25 groups of experiments are conducted according to the principle of orthogonal experiment.By the range analysis of the experiment results,the impact of each element is determined,and a group of preliminary optimal parameters has been found(when the temperature is 760℃,dipping for 10 minutes,and when the temperature is 940℃,diffusing for 3 hours).In addition,according to the hot corrosion experiment,the corrosion rates of the samples are calculated.It is shown that the thickness of aluminized layer has a significant positive correlation with the hot corrosion resistance.Compared with the non-aluminized material,the aluminized one is 20%lower on the hot corrosion rate.Because the level of each parameter in the experiment is limited,the optimal solution obtained by range analysis may not be the optimal one.The PSO neural network optimization model of hot dip aluminizing is established to search for a better solution.Temperature for dipping,time for dipping,temperature for diffusing,and time for diffusing are chosen as the inputs of the model.Besides,layer thickness is chosen as the output.The training mean square deviation of the network is 0.00011.After 100 epochs of optimization,the optimal solution is as follows:when the temperature is 766℃,dipping for 9.6 minutes,and when the temperature is 833℃,diffusing for 3.16 hours,and the thickness of the layer is 156.55μm,which is better than the predicted output value of the preliminary optimal solution,indicating the advantage of PSO-neural network optimization model.Besides,the effect of welding on the corrosion resistance of the aluminized pipe is studied by immersion corrosion experiment in this paper.The two aluminized pipes are joined by the method of one-side welding and two-sided forming method.The results show that after welding,the immersion corrosion rate of aluminized materials near the welding position is 9.3%lower than that near the non-welding position.It can be concluded that the corrosion resistance of the layer near the welding position is not damaged by this welding method.This paper proposes a fast and feasible method for the optimization of the process parameters of the hot dip aluminizing.It is verified that the corrosion resistance of the aluminized pipe will not be damaged by the welding of the aluminized pipe with one side welding and two sides forming method.In this paper,there 29 figures,16 tables,and 75 refences.