Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (6): 1537-1547.doi: 10.13229/j.cnki.jdxbgxb.20230025

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Internal surface treatment of gas-liquid-solid technology based on improved neural network and Fluent

Guang-bao LI1,2(),Dong GAO2,Yong LU2,Hao PING1,Yuan-yuan ZHOU1   

  1. 1.Shanghai Aerospace Precision Machinery Research Institute，Shanghai 201600，China
    2.School of Mechanical and Electrical Engineering，Harbin Institute of Technology，Harbin 150001，China
  • Received:2023-01-08 Online:2024-06-01 Published:2024-07-23

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Abstract:

At present， the manufacturing methods of complex multi-bore workpieces in aviation and aerospace often have raised burrs on the inner bore surface of the workpieces， which are not easy to remove. In view of this situation， a gas-liquid-solid multiphase flow technology is proposed to treat the inner surface of complex workpieces. Based on fluent numerical analysis， different gas pressure， water velocity and abrasive particle concentration parameters under multiphase flow are simulated. The parameters of abrasive particle velocity， relative pressure and abrasive particle volume fraction at the inner surface of the near-wall area which affect the machining are obtained. The parameters are fitted by using BP neural network with MATLAB software， and the optimal input value of gas-liquid-solid three-phase flow satisfying the constraint mathematical model is solved by using the BP prediction model and PSO （Particle Swarm Optimization） in the dominant solution set. Based on the optimal input value， an experimental platform is built and L9（33） orthogonal experiment is designed to machine the inner surface of the workpiece. Finally， the surface accuracy of the inner hole before and after machining is measured by a white light interferometer， which verifies the consistency between the simulation optimal parameters and the experimental optimal parameters. The inner surface of the workpiece is machined by using the gas-liquid-solid three-phase flow with the optimal parameters. The measurement shows that the inner surface accuracy is increased by 75%， which meets the application requirements of aerospace workpieces.

Key words: machinery manufacturing technology and equipment, gas-solid technology, fluid simulation, neural network, particle swarm optimization： orthogonal experiment

CLC Number: 

  • TG664

Fig.1

Multi-bore workpiece of nitrogen charging equipment"

Fig.2

Technical flow chart"

Fig.3

Three-dimensional model of bore fluid"

Fig.4

Grid division"

Fig.5

Nephogram of simulation parameters"

Fig.6

Monitoring surface"

Table 1

Simulation parameter data set"

序号气相压力/MPa液相速度/（m·s-1）磨粒浓度/%壁面压力/MPa磨粒速度/（m·s-1）体积分数/%仿真均匀度/%
10.1510.1223.589162
20.21052.1265.012668
30.315105.3368.7421353
40.4201512.46111.6411632
50.5252020.17412.3471869

Fig.7

BP neural network model"

Fig.8

Training results"

Fig.9

Comparison between predicted parameters and actual parameters"

Fig.10

Fitness iteration curve"

Fig.11

Corresponding fitness of population particles"

Fig.12

Experimental equipment"

Table 2

Orthogonal experimental parameters of internal surface processing of gas-liquid-solid technology"

编号气体压力/MPa

水流速度/

（m·s-1）

固相浓度

/%

加工时间/min
10.419930
20.520930
30.621930
40.6201030
50.5191030
60.4211030
70.4201130
80.5211130
90.6191130

Table 3

Orthogonal experimental results of internal surface treatment of gas-liquid-solid technology"

编号气体压力/MPa水流速度/（m·s-1）固相浓度/%加工时间/min粗糙度/μm加工均匀度/%
10.4199300.42231.2
20.5209300.31425.6
30.6219300.45230.3
40.62010300.32224.5
50.51910300.31724.3
60.42110300.35128.8
70.42011300.34829.1
80.52111300.35228.9
90.61911300.45532.1

Table 4

Response value of roughness"

水平因 素
气体压力/MPa水流速度/（m·s-1）磨粒浓度/%
10.3740.3980.396
20.3270.3280.33
30.4090.3850.385

Table 5

Uniformity response value"

水平因 素
气体压力/MPa水流速度/（m·s-1）磨粒浓度/%
129.729.229.1
226.226.425.8
328.929.330.1

Fig.13

Survey schematic diagram"

Fig.14

Schematic diagram of measuring point"

Fig.15

Schematic diagram of inner hole surface"

Table 6

Measuring roughness parameters of monitoring surface"

测量位置粗糙度算术平均高度sa/μm粗糙度均方根高度sq/μm粗糙度最大高度sz/μm
监视面1测量点10.2310.32813.423
测量点20.2660.3609.907
测量点30.2190.32313.542
监视面2测量点10.3340.4338.004
测量点20.3730.56617.047
测量点30.2620.37719.611
监视面3测量点10.3030.41712.864
测量点20.2710.36815.857
测量点30.2210.2826.169
监视面4测量点10.1740.2547.422
测量点20.2310.41318.624
测量点30.3220.5378.340
1 王杰,李爱蓉.小波包分析识别气液固三相流流型[J].石油化工,2020(1):62-69.
Wang Jie, Li Ai-rong. Identification of gas-liquid-solid three-phase flow pattern by wavelet packet analysis[J]. Petrochemical Industry, 2020(1):62-69.
2 辛亚男,张建文,姜爱国.一种新型螺旋内槽管的气液固三相流数值模拟研究[J].计算力学学报,2018(2):238-248.
Xin Ya-nan, Zhang Jian-wen, Jiang Ai-guo. Numerical simulation of gas-liquid-solid three-phase flow of a new type of spiral inner groove tube[J]. Journal of Computational Mechanics, 2018(2):238-248.
3 黄豆豆,计时鸣,陈国达,等.气液固三相湍流环境中气泡破裂对SiC颗粒的影响研究[J].机电工程,2017,34(9):965-970.
Huang Dou-dou,Timing, Chen Guo-da,et al. Study on the influence of bubble burst on SiC particles in gas-liquid-solid three-phase turbulent environment[J]. Electromechanical Engineering, 2017,34(9):965-970.
4 杨建.水力空化气液固三相磨粒流抛光工具及系统研究[D].杭州:浙江工业大学机械工程学院,2019.
Yang Jian. Study on polishing tools and systems of hydraulic cavitation gas-liquid-solid three-phase abrasive flow[D]. Hangzhou:College of Mechanical Engineering,Zhejiang University of Technology, 2019.
5 胡焰.基于气液固混合的磨料射流加工系统及实验研究[D].南京:南京航空航天大学机电学院,2019.
Hu Yan. Abrasive jet machining system and experimental study based on gas-liquid-solid mixing[D].Nanjing: School of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 2019.
6 李瑞光,丁宇亭,杨增辉,等.航天伺服阀阀套相交孔磨粒流工艺技术研究[J].液压气动与密封,2022,42(7):109-112.
Li Rui-guang, Ding Yu-ting, Yang Zeng-hui,et al. Study on abrasive flow technology of intersecting hole of aerospace servo valve sleeve[J]. Hydraulic Pneumatic and Sealing, 2022,42(7):109-112.
7 计时鸣,曹慧强,赵军,等.三相磨粒流文丘里管结构空化辅助抛光机理与试验[J].农业工程学报,2018,34(16):71-79.
Timing, Cao Hui-qiang, Zhao Jun,et al. Cavitation-assisted polishing mechanism and experiment of three-phase abrasive flow venturi tube structure[J]. Journal of Agricultural Engineering, 2018,34(16):71-79.
8 王景坡,宋超,凌洋,等.多通管接头磨粒流光整均匀性研究[J].表面技术,2022,(4):299-307.
Wang Jing-po, Song Chao, Ling Yang,et al. Study on abrasive particle polishing uniformity of multi-port pipe joints[J]. Surface Technology, 2022(4):299-307.
9 李晓波,魏武,才冬涛,等.基于磨粒流技术的阀块内孔道光整加工[J].金属加工(冷加工),2022(3):20-24.
Li Xiao-bo, Wei Wu, Cai Dong-tao,et al. Finishing of inner bore of valve block based on abrasive flow technology[J]. Metalworking (Cold Working), 2022(3):20-24.
10 王卫东.自激振荡脉冲空化射流喷嘴多目标优化研究[D].秦皇岛:燕山大学机械工程学院,2021.
Wang Wei-dong. Study on multi-objective optimization of self-excited oscillation pulsed cavitation jet nozzle[D]. Qinghuangd:School of Mechanical Engineering, Yanshan University, 2021.
11 褚聪,戴勇,沈明,等.基于Fluent仿真的强约束磨粒射流抛光特性[J].表面技术,2016(6):198-204.
Chu Cong, Dai Yong, Shen Ming,et al. The polishing characteristics of strongly constrained abrasive jet based on Fluent simulation[J]. Surface Technology, 2016(6):198-204.
12 刘江,黄晓斌,何吕,等.基于Fluent的喷油嘴倒锥孔磨粒流加工数值模拟[J].机械制造与自动化,2021,50(2):40-42, 50.
Liu Jiang, Huang Xiao-bin, He Lyu,et al. Numerical simulation of abrasive flow machining in the inverted cone hole of fuel injector based on Fluent[J]. Machinery Manufacturing and Automation, 2021,50(2):40-42, 50.
13 李研彪,陈强,张利.钛合金薄壁曲面液态金属-磨粒流加工仿真与试验研究[J].机械工程学报,2021(23):220-231.
Li Yan-biao, Chen Qiang, Zhang Li. Simulation and experimental study on liquid metal-abrasive flow machining of titanium alloy thin-walled curved surface[J]. Journal of Mechanical Engineering, 2021(23):220-231.
14 刘洋,李俊烨,苏宁宁,等.磨粒流抛光弯管的数值模拟与试验优化[J].机械设计与制造,2021(7):137-140.
Liu Yang, Li Jun-ye, Su Ning-ning,et al. Numerical simulation and experimental optimization of abrasive flow polishing bend[J]. Mechanical Design and Manufacturing, 2021(7):137-140.
15 计时鸣,葛江勤,谭大鹏,等.三相磨粒流抛光及其气泡溃灭分布特性[J].光学精密工程,2018(2):388-398.
Timing, Ge Jiang-qin, Tan Da-peng,et al. Three-phase abrasive flow polishing and bubble collapse distribution characteristics[J]. Optical Precision Engineering, 2018(2):388-398.
16 姚燕生,周瑞根,张成林,等.增材制造复杂金属构件表面抛光技术[J].航空学报,2022(4):244-256.
Yao Yan-sheng, Zhou Rui-gen, Zhang Cheng-lin,et al. Surface polishing technology of complex metal components manufactured by additives[J]. Acta Aeronautical, 2022(4):244-256.
17 李光保.基于三相流技术的零件内表面毛刺处理的系统研究[D].哈尔滨:哈尔滨工业大学机电工程学院,2019.
Li Guang-bao. Systematic research on burr treatment of parts inner surface based on three-phase flow technology[D]. Harbin:School of Mechanical and Electrical Engineering, Harbin Institute of Technology, 2019.
18 张方东.磨料流加工工件表面粗糙度研究[J].现代制造技术与装备,2020,56(10):162-163.
Zhang Fang-dong. Study on the surface roughness of abrasive flow machining workpiece[J]. Modern Manufacturing Technology and Equipment, 2020,56(10):162-163.
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