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
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