文献类型：期刊文献

中文题名：神经网络优化膳食营养补充剂胶囊支架3D打印工艺

英文题名：Optimization of 3D printing technology of nutraceuticals capsule scaffold using neural network

作者：陈虹竹[1,2];张良[2];姚佳[3];胡小佳[2];刘倩楠[2];刘伟[2];孙钦秀[1];胡宏海[2];刘书成[1]

机构：[1]广东海洋大学食品科技学院,广东省水产品加工与安全重点实验室,广东省海洋生物制品工程实验室,广东省海洋食品工程技术研究中心,水产品深加工广东普通高等学校重点实验室,湛江524088;[2]中国农业科学院农产品加工研究所,农业农村部农产品加工综合性重点实验室,北京100193;[3]北京城市学院生物医药学部,北京100083

年份：2021

卷号：37

期号：4

起止页码：18

中文期刊名：农业工程学报

外文期刊名：Transactions of the Chinese Society of Agricultural Engineering

收录：CSTPCD、、CSCD2021_2022、EI(收录号：20211910339473)、Scopus、北大核心、CSCD、北大核心2020

基金：中央级公益性科研院所基本科研业务费专项(S2019XK01);农业农村部财政经费项目;广东普通高等学校海洋食品绿色加工技术研究团队(2019KCXTD011)。

语种：中文

中文关键词：神经网络;工艺优化;3D打印;熔融沉积成型;胶囊支架;

外文关键词：artificial neural networks;process optimization;3D printing;fused deposition modeling;capsule scaffold;

中文摘要：为了确定熔融沉积成型3D打印技术制备个性化膳食营养补充剂胶囊支架的工艺条件,打印9通道聚乳酸(Polylactic Acid,PLA)胶囊支架结构,采用Box-Behnken设计,以支架通道的面积均方误差(Mean-Square Error,MSE)作为响应指标,通过神经网络模型模拟温度、打印头孔径和速度3个因素对胶囊支架打印效果的影响。结果表明:3×4×1结构3层神经网络模型能够较好拟合3D打印过程(R2=0.998),当孔径为0.3 mm时,随着打印温度和打印速度的增大,MSE均低于10%。打印温度过高或打印头孔径过大时,胶囊支架内部会出现拉丝现象。综合考虑打印能耗和效率,确定当打印头孔径为0.3mm,打印速度25~30mm/s,打印温度介于173~180℃之间,MSE范围为1%~2%时,胶囊支架打印精度和微观结构较好。研究结果为3D打印精细化结构及打印参数优化提供理论参考。

外文摘要：Nutraceuticals are herbal and food products with high nutritional values,which are commonly used to treat or prevent diseases.In personalized nutrition,there is an inter-individual response to nutraceuticals intervention,due to the fact that a sub population can benefit more than others.The traditional production of dietary supplements or functional food has very limited flexibility of fabrication to achieve personalized customization with nutrients or functional factors.The oral dosage forms can inevitably lead to insufficient or excessive intake.A type of 3 D printing technology,fused deposition modeling(FDM)has been selected to create complex and accurate shapes using food-derived thermoplastic materials.Particularly,polylactic acid(PLA)has been successfully used to produce the scaffold of oral capsule.In this study,a 9-channel PLA capsule scaffold structure and Box-Behnken design were used to explore the effects of printing temperature,nozzle diameter,and printing speed on the FDM 3 D printing accuracy.Meanwhile,the printing channel area error(MSE)was used as the response index during the optimization.A 3×4×1 three-layer neural network(NN)model was established.The coefficients of determination(R2)were above 0.998,indicating an excellent fitness between the actual and predicted MSE in the NN model.The nozzle diameter was the most influential factor.The MSE remained almost unchanged with the increase of printing temperature and speed at the nozzle diameter of 0.3 mm,where the MSE of the capsule channel area was less than 10%.In terms of energy consumption and efficiency,a low level of MSE was obtained with a lower temperature and faster speed.No significant difference was observed between the prediction values of the NN model and the experimental ones.The results indicated that the NN model can be used to predict the process of FDM 3 D printing.An optimal combination of parameters was achieved,where the nozzle diameter was 0.3 mm,and the printing speed ranged from 25 mm/s to 30 mm/s,while the printing temperature ranged from 173℃to 180℃and the MSE ranged between 1%-2%.In this case,the printing accuracy of the capsule scaffold structure was better with the lower energy consumption and higher printing efficiency.A scanning electron microscope was used to characterize the internal microstructure and monitor the wiredrawing phenomena of the capsule scaffold.There was an uniform microstructure of the internal channel.The bonding between printing layers was compact when the printing temperature was fixed at 176℃with the nozzle diameter of 0.3 mm and printing speed of 25 mm/s.An obvious wiredrawing was observed in the capsule scaffold during the high printing temperature or large nozzle diameter.The contraction of PLA was greater as the increase in the slice layers at the small diameter of the nozzle,leading to the loose internal structure and the poor adhesion between the printed layers.The optimized parameters were obtained in the NN model to print the delicate internal microstructure of the capsule scaffold.These findings can provide a theoretical basis for the personalized production of nutraceuticals,thereby realizing the customization and controlled release of minerals,vitamins,or functional factors.