Study on the Morphology Control Technology of Spray Forming Ingot Billets Based on GA-BP Neural Network

doi:10.12141/j.issn.1000-565X.220147

Abstract

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

CLC Number:

TF124

LENG Sheng, FU Youwei, MA Wantai, et al. Study on the Morphology Control Technology of Spray Forming Ingot Billets Based on GA-BP Neural Network[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(2): 27-34.

Figures/Tables 15

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References 14

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喷射成形工艺参数	Pearson相关系数	显著性（P值）
沉积室压力	-0.042	0.004
沉积室温度	0.136	0.002
漏包实际液位	0.386	0.000
铝水左侧倾倒装置转速	-0.031	0.026
铝水右侧倾倒装置转速	-0.025	0.062
熔体温度	0.044	0.008
雾化气压力	-0.256	0.000
沉积基底旋转速度	-0.068	0.104
沉积基底升降速度	-0.011	0.124
沉积室排风速度	0.138	0.000

样本号	沉积室压力/ Pa	沉积室温度/ ℃	漏包实际液位/mm	铝水温度/ ℃	总管道压力/MPa	沉积室排风速度/ （r·min^-1）	锭坯沉积表面直径/mm
1-1	-188.15	40.89	304.04	686.9	2.194	838.07	917.05
1-2	-167.21	40.99	303.75	686.9	2.194	836.73	916.89
?	?	?	?	?	?	?	?
1-7 200	-119.36	140.01	103.81	677.2	2.128	1 036.29	895.41
2-1	-125.33	37.24	312.72	688.0	2.186	817.08	916.40
2-2	-138.78	41.59	311.06	688.0	2.120	826.02	916.26
?	?	?	?	?	?	?	?
2-7 054	-111.65	145.16	99.18	678.3	2.039	1 027.40	895.20
3-1	-104.28	42.19	282.34	683.7	2.066	843.47	916.73
3-2	-118.27	42.23	281.91	683.7	2.066	843.42	916.57
?	?	?	?	?	?	?	?
3-6 843	-106.45	137.39	200.31	674.9	2.198	1 013.06	895.74

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