神经网络-遗传算法对杏鲍菇粉3D打印的建模与优化

doi:10.3864/j.issn.0578-1752.2024.03.012

Abstract

Abstract:

【Objective】Food 3D printing technology, a promising technology in the field of food, can be affected by multiple factors and thus has problems, such as difficulty in determining printing parameters and poor ability of predicting printing accuracy. This paper aimed to seek out an effective modeling method to optimize 3D printing parameters of Pleurotus eryngii powder and to determine the optimal conditions for 3D printing.【Method】Pleurotus eryngii powder and locust bean gum were adopted as 3D printing ink. Then, based on single-factor experiments, the central composite experimental design was performed to study the influence of four key process parameters - nozzle diameter, printing height, nozzle movement speed and fill density - on the accuracy of 3D printing. In order to optimize 3D printing parameters of Pleurotus eryngii powder, response surface methodology (RSM) and artificial neural network and genetic algorithm (ANN-GA) were employed to achieve different effects.【Result】The determination coefficient (R²), root mean square error (RMSE), relative error (RE), and optimal value of prediction (VOP) of RSM model were 0.8817, 0.2314, 72.73%, and 0.148, respectively; the R², RMSE, RE, and optimal VOP of ANN-GA model were 0.9389, 0.2269, 33.85%, and 0.215, respectively. The ANN-GA model obtained higher R², lower RMSE and RE, and was better fitting ability, and higher optimal VOP than RSM model, so ANN-GA model possessed better prediction ability. Compared with RSM, ANN-GA was more suitable for optimization of 3D printing parameters of Pleurotus eryngii powder. The optimal process parameters of 3D printing obtained by ANN-GA, with Pleurotus eryngii as printing ink, included nozzle diameter 1.2 mm, printing height 1.1 mm, nozzle movement speed 24 mm·s^-1, and fill density 84%. Experimental verification suggested that the deviation of printed samples by ANN-GA was 0.325, which was superior to the actual printing deviation 0.550 by RSM.【Conclusion】ANN-GA was effective in determining the optimal process parameters of 3D printing and accurate in predicting the accuracy of food 3D printing products. Therefore, ANN-GA could serve as an effective and convenient method for optimizing personalized 3D printing parameters of agricultural products and food.

Key words: 3D food printing, Pleurotus eryngii, neural network, genetic algorithm, process optimization

SU AnXiang, HE AnQi, MA GaoXing, ZHAO LiYan, YANG WenJian, HU QiuHui. Modeling and Optimization of 3D Printing Process of Pleurotus Eryngii Powder Using Neural Network-Genetic Algorithm[J].Scientia Agricultura Sinica, 2024, 57(3): 584-596.

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References 36

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序号 No.	因素Factor				偏差量 Deviation (%)
序号 No.	喷嘴直径 Nozzle diameter (mm)	打印高度 Nozzle height (mm)	喷嘴移动速度 Moving speed of nozzle (mm∙s^-1)	填充率 Fill density (%)	偏差量 Deviation (%)
1	1.00	1.00	20	70	2.60
2	1.40	1.00	20	70	1.60
3	1.00	1.40	20	70	2.20
4	1.40	1.40	20	70	1.58
5	1.00	1.00	30	70	2.69
6	1.40	1.00	30	70	2.03
7	1.00	1.40	30	70	2.21
8	1.40	1.40	30	70	1.99
9	1.00	1.00	20	90	1.54
10	1.40	1.00	20	90	0.91
11	1.00	1.40	20	90	1.95
12	1.40	1.40	20	90	0.61
13	1.00	1.00	30	90	1.60
14	1.40	1.00	30	90	1.30
15	1.00	1.40	30	90	1.73
16	1.40	1.40	30	90	1.03
17	0.80	1.20	25	80	2.00
18	1.60	1.20	25	80	2.51
19	1.20	0.80	25	80	2.51
20	1.20	1.60	25	80	1.94
21	1.20	1.20	15	80	0.47
22	1.20	1.20	35	80	1.84
23	1.20	1.20	25	60	2.56
24	1.20	1.20	25	100	0.92
25	1.20	1.20	25	80	0.12
26	1.20	1.20	25	80	0.43
27	1.20	1.20	25	80	0.86
28	1.20	1.20	25	80	0.06
29	1.20	1.20	25	80	0.19
30	1.20	1.20	25	80	0.19

方差来源 Source of variance	平方和 Sum of squares	自由度 Degree of freedom	均方 Mean square	F值 F value	P值 P value
模型 Model	17.510	14	1.250	7.99	0.0001**
A喷嘴直径Nozzle diameter	0.800	1	0.800	5.10	0.0392*
B打印高度Nozzle height	0.190	1	0.190	1.22	0.2871
C移动速度Moving speed of nozzle	0.770	1	0.770	4.92	0.0424*
D填充率 Fill density	3.760	1	3.760	24.01	0.0002**
AB	0.005	1	0.005	0.03	0.8668
AC	0.190	1	0.190	1.19	0.2917
AD	0.015	1	0.015	0.10	0.7612
BC	0.007	1	0.007	0.04	0.8377
BD	0.052	1	0.052	0.33	0.5739
CD	0.006	1	0.006	0.04	0.8474
A²	5.860	1	5.860	37.45	<0.0001**
B²	5.800	1	5.800	37.04	<0.0001**
C²	1.020	1	1.020	6.48	0.0224*
D²	3.170	1	3.170	20.23	0.0004**
残差 Residual	2.350	15	0.160
失拟项Lack-of-Fit	1.900	10	0.190	2.12	0.2102
纯误差 Pure error	0.450	5	0.090
总和 Sum	19.860	29

Modeling and Optimization of 3D Printing Process of Pleurotus Eryngii Powder Using Neural Network-Genetic Algorithm

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