中国农业科学2024，Vol.57Issue(3)：584-596,13.DOI:10.3864/j.issn.0578-1752.2024.03.012
神经网络-遗传算法对杏鲍菇粉3D打印的建模与优化
Modeling and Optimization of 3D Printing Process of Pleurotus Eryngii Powder Using Neural Network-Genetic Algorithm
摘要
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.关键词
3D食品打印/杏鲍菇/神经网络/遗传算法/工艺优化Key words
3D food printing/Pleurotus eryngii/neural network/genetic algorithm/process optimization引用本文复制引用
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):584-596,13.基金项目
江苏省科技成果转化项目(BA2021062)、江苏高校优势学科建设工程资助项目(PAPD)、江苏高校品牌专业建设工程资助项目(TAPP) （BA2021062）
