Modeling and Optimization of 3D Printing Process of Pleurotus Eryngii Powder Using Neural Network-Genetic Algorithm
[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.
3D food printingPleurotus eryngiineural networkgenetic algorithmprocess optimization
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