Lightweight and high-performance aluminum alloys are crucial for the weight reduction design of aerospace equipment, thus spray forming with rapid solidification technology has garnered increasing attention for the fabrication of high-strength aluminum alloys. To meet the demands of large-scale aerospace components, a multinozzle collaborative system is required to achieve larger billet diameters. During the scanning and deposition process of atomization cones formed by multiple nozzles at a certain inclination angle on the deposition interface, ensuring uniform distribution of the molten material, a flat deposition interface at the top of the billet, and stable growth are key to obtaining high-quality, dense, and uniform deposited billet structures. These factors are key to producing high-quality billets with dense and uniform microstructures. The process parameters associated with multi-nozzle configurations directly influence the scanning trajectories of atomized droplets and the material deposition state at the interface, playing a decisive role in billet growth. Accordingly, by targeting the fabrication of large-size billets with consistent surface morphology and uniform deposition quality, a multi-nozzle deposition surface behavior model (DSBM) at the micro-scale was established based on the scanned deposition height, taking into account the overlap and intersection of deposition regions that arise during scanning of the multi-nozzle atomization cones. The initial nozzle tilt angle, nozzle eccentric offset, and melt mass flow rate were selected as adjustable parameters; constraints were set according to the actual operating conditions to construct a DSBM-based control model. Using the height difference H of the billet’s deposition-surface unevenness as the optimization objective, the GA-DSBM intelligent control method for the deposition interface was employed to simulate and optimize the relevant process parameters during deposition. A four-nozzle spray-forming experiment was conducted to verify the optimized parameters. The resulting billet, with a diameter of 600 mm, exhibited a surface unevenness height difference of 7.52 mm, meeting the process design requirements. Meanwhile, the top-surface unevenness of the billet was markedly reduced, interfacial material uniformity was improved, and the billet porosity was effectively lowered—thereby validating the feasibility of the proposed intelligent control and optimization method.

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 realtime 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.