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

摘要/Abstract

摘要：

【目的】食品3D打印技术是食品领域具有发展前景的新技术，但是打印过程影响因素多，存在打印参数确定困难、打印精度预测能力差等问题。寻找有效建模方法，对杏鲍菇粉3D打印参数进行寻优，以确定最佳3D打印条件。【方法】本研究采用杏鲍菇粉和刺槐豆胶为3D打印原料，以单因素试验为基础，通过中心组合试验设计，研究喷嘴直径、打印高度、喷嘴移动速度和填充率4个关键的工艺参数对杏鲍菇粉3D打印精度的影响，并在此基础上采用响应面法和神经网络-遗传算法分别建模分析，确定3D打印的工艺参数。【结果】单因素试验及中心组合试验结果表明，影响3D打印精度的主要因素从大到小顺序为填充率、喷嘴直径、喷嘴移动速度、打印高度。响应面法和神经网络-遗传算法均可用于杏鲍菇粉3D打印参数优化，但是优化效果不同。响应面法的决定系数R²值、均方根误差、相对误差、预测最优值分别为0.8817、0.2314、72.73%、0.148；神经网络-遗传算法的决定系数R²值、均方根误差、相对误差、预测最优值分别为0.9389、0.2269、33.85%、0.215。比较模型参数可得，神经网络-遗传算法的决定系数R²值较高，均方根误差、相对误差较低，比响应面法拟合能力更好，同时其预测最优值较高，具有更好的预测能力。神经网络-遗传算法比响应面法更适合于杏鲍菇粉3D打印参数工艺的优化。采用神经网络-遗传算法获得以杏鲍菇为原料的3D打印最佳工艺参数条件为：喷嘴直径1.2 mm、打印高度1.1 mm、喷嘴移动速度24 mm∙s^-1、填充率84%。经过试验验证，神经网络-遗传算法确定的最优参数打印样品偏差为0.325，优于响应面的实际打印偏差0.550。【结论】本研究结果表明神经网络-遗传算法可以有效确定3D打印过程最优工艺参数，准确预测食品3D打印产品的精度，可作为农产品及食品个性化3D打印工艺参数优化的一种有效便捷方法。

关键词: 3D食品打印, 杏鲍菇, 神经网络, 遗传算法, 工艺优化

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

苏安祥, 贺安琪, 马高兴, 赵立艳, 杨文建, 胡秋辉. 神经网络-遗传算法对杏鲍菇粉3D打印的建模与优化[J]. 中国农业科学, 2024, 57(3): 584-596.

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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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