Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (11): 168-174.doi: 10.13475/j.fzxb.20180604807

• Management & Information • Previous Articles     Next Articles

Optimization of process parameters for 3D additive screen printing based on genetic algorithm and neural network

WANG Xiaohui(), LIU Yuegang, MENG Zhuo, SUN Yize   

  1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
  • Received:2018-06-14 Revised:2019-08-05 Online:2019-11-15 Published:2019-11-26

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Abstract:

Aiming at the problem that the combination of parameters such as blade pressure, squeegee speed, blade angle and ink viscosity in the 3D additive printing process has a great influence on the printing quality, but the combination of various process parameters in actual production cannot be optimal, the BP neural network improved by the additional momentum method was adopted to construct a 3D additive printing process model. The model was trained by the experimental parameters to determine the nonlinear relationship between process parameters and printing quality. The genetic algorithm was adopted to optimize the nonlinear function to achieve the optimal parameter combination of the 3D additive printing process: printing pressure of 4 800 N, squeezing angle of 18 degrees, squeezing speed of 400 mm/s and ink viscosity of 170.5 Pa·s, The model prediction error is basically stable within the range of 0.01. The comparison experiments were carried out using the parameters before and after optimization. The experimental results show that the algorithm can realize the quality prediction and parameter optimization of 3D additive printing process, thereby improving the printing quality and shortening the product development time.

Key words: 3D additive screen printing process, BP neural network, genetic algorithm, parameter optimization, quality prediction

CLC Number: 

  • TS194.49

Fig.1

Topology diagram of BP neural network"

Fig.2

Flow chart of an algorithm for optimizing process parameters of 3D additive printing"

Fig.3

Structure of 3-layer BP neural network"

Tab.1

Printing quality data of 3D additive printing machine"

样本
编号		 印花
压力/N		 刮印
角度/(°)		 刮印速度/
(mm·s-1)		 油墨黏度/
(Pa·s)		 油墨
转移率		 样本
编号		 印花压力/
N		 刮印
角度/(°)		 刮印速度/
(mm·s-1)		 油墨黏度/
(Pa·s)		 油墨
转移率
1 4 500 20 350 172.1 0.386 55 6 500 10 350 180.7 0.219
2 5 000 20 350 172.1 0.267 56 6 500 10 500 180.7 0.363
3 5 500 20 350 172.1 0.386 57 5 000 20 350 180.7 0.298
4 5 500 10 350 111.4 0.327 58 5 500 20 350 180.7 0.275
5 6 500 10 350 111.4 0.279 59 5 500 20 350 180.7 0.275
6 5 000 10 350 111.4 0.357 60 5 500 10 350 180.7 0.342
7 4 500 20 350 121.4 0.368 61 6 500 20 500 180.7 0.401
8 5 500 20 350 121.4 0.345 62 6 500 20 500 180.7 0.405
9 5 500 20 350 121.4 0.367 63 6 500 20 500 180.7 0.332
10 6 500 10 350 121.4 0.289 64 6 500 10 700 180.7 0.34
11 6 500 10 350 121.4 0.289 65 6 500 10 700 180.7 0.372
12 6 500 10 500 121.4 0.319 66 6 500 25 350 180.7 0.275
13 4500 20 350 155.0 0.459 67 6 500 25 350 180.7 0.249
14 5 500 20 350 155.0 0.317 68 6 500 5 350 180.7 0.281
15 5 500 20 350 155.0 0.317 69 6 500 5 700 180.7 0.212
16 6 500 10 350 155.0 0.376 70 6 500 5 700 180.7 0.212
17 6 500 10 350 155.0 0.376 71 6 500 20 500 180.7 0.265
18 6 500 10 500 155.0 0.321 72 6 500 20 500 180.7 0.315
19 4500 20 350 122.1 0.286 73 6 500 10 500 180.7 0.324
20 5 500 20 350 122.1 0.318 74 6 000 10 500 180.7 0.317
21 5 500 20 350 122.1 0.318 75 5 500 5 500 180.7 0.296
22 5 500 10 350 142.1 0.316 76 6 500 5 500 180.7 0.288
23 5 500 10 350 142.1 0.316 77 6 500 20 400 180.7 0.366
24 5 500 10 500 180.7 0.229 78 6 500 20 400 180.7 0.277
25 6 500 20 350 180.7 0.256 79 6 500 10 500 180.7 0.274
26 6 500 20 350 180.7 0.217 80 6 500 10 700 180.7 0.294
27 6 500 20 350 180.7 0.232 81 5 500 5 700 180.7 0.318
28 6 500 10 350 180.7 0.211 82 5 500 5 700 180.7 0.318
29 6 500 10 350 180.7 0.207 83 5 500 20 500 180.7 0.343
30 6 500 20 350 180.7 0.215 84 5 500 20 500 180.7 0.372
31 6 500 20 350 180.7 0.278 85 5 500 20 500 180.7 0.361
32 6 500 20 350 180.7 0.273 86 5 000 10 500 180.7 0.392
33 6 000 10 350 180.7 0.232 87 5 000 10 500 180.7 0.381
34 6 000 10 350 180.7 0.216 88 6 500 20 500 180.7 0.329
35 6 000 20 350 180.7 0.198 89 6 500 20 500 180.7 0.335
36 7 000 20 350 180.7 0.187 90 6 500 20 500 180.7 0.321
37 6 000 20 350 180.7 0.237 91 6 000 5 500 180.7 0.265
38 6 000 10 350 180.7 0.256 92 6 000 5 700 180.7 0.241
39 5 500 10 350 180.7 0.263 93 6 500 20 500 180.7 0.337
40 5 500 10 350 180.7 0.263 94 6 500 20 500 180.7 0.348
41 5 500 10 350 180.7 0.237 95 6 500 20 500 180.7 0.329
42 7 500 20 350 180.7 0.276 96 6 000 5 500 180.7 0.348
43 7 500 20 350 180.7 0.289 97 6 500 5 500 180.7 0.373
44 7 500 20 350 180.7 0.231 98 6 500 20 500 180.7 0.424
45 7 500 10 350 180.7 0.314 99 6 500 20 500 180.7 0.424
46 7 500 10 350 180.7 0.316 100 6 500 20 500 180.7 0.424
47 6 500 20 350 180.7 0.329 101 6 500 5 500 180.7 0.411
48 6 000 20 350 180.7 0.312 102 6 500 5 500 180.7 0.411
49 6 500 20 350 180.7 0.314 103 4 000 20 400 180.7 0.277
50 6 500 10 350 180.7 0.305 104 4 000 20 400 180.7 0.226
51 6 500 10 500 180.7 0.297 105 4 000 20 400 180.7 0.338
52 6 500 20 350 180.7 0.312 106 4 500 5 400 180.7 0.347
53 6 500 20 350 180.7 0.312 107 4 500 5 400 180.7 0.347
54 6 500 20 350 180.7 0.299 108 4 000 20 400 180.7 0.259

Fig.4

Results of neural network prediction"

Fig.5

Errors (a) and errors percentage (b) of neural network prediction"

Fig.6

Fitness curve of genetic algorithm optimization"

Fig.7

Comparison of experimental results before (a) and after (b) parameter optimization"

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