关键词: 喷射成形, 大规格锭坯, 工艺参数, 神经网络, 速度调控

Abstract:

With the development of modern technology, the automotive and aerospace fields are pursuing the lightweight of materials, and the high strength and high toughness of materials is the basis of lightweight. 7000 series aluminum alloys (Al-Zn-Mg-Cu series aluminum alloys) have the advantages of high strength, high hardness, good corrosion resistance, et al. Among all aluminum alloys, 7055 aluminum alloy has the highest strength. The common preparation method of 7055 aluminum alloy is spray forming process. Stable growth of the aluminum ingot during deposition is the basis for the preparation of large-size ingots with uniform deposition quality by the spray forming process. Due to the variation of numerous process parameters during the jet forming process, the existing theoretical model is difficult to meet the requirements of quality control in the actual production process. This paper built a GA-BP neural network prediction model for the diameter and a model for regulating the growth rate of the ingot billet based on the correlation analysis between the historical data of the injection molding process and the diameter of the deposited surface of the ingot billet, by combining BP neural network and genetic algorithm. Based on the real-time fluctuation of process parameters, the diameter variation was calculated and used as an input layer into a trained velocity regulation neural network model to optimally regulate the lifting speed of the deposition substrate, resulting in a uniform and stable deposition growth profile of the ingotst. Finally, this method was used to regulate the growth rate of ingots. The results show that the deviation of large-size ingot diameter is within 5%, which verifies the feasibility of growth rate regulation.

Key words: spray forming, large-size ingots, process parameter, neural network, rate regulation