Multi-objective optimization of printer control parameters for 3D printing of millet dough.

IF 3.5 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Journal of the Science of Food and Agriculture Pub Date : 2025-09-11 DOI:10.1002/jsfa.70181

Sanket Balasaheb Kokane, Vinkel Kumar Arora, Senthilkumar Thangalakshmi

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引用次数: 0

Abstract

Background: Three-dimensional (3D) food printing enables precise customization and intricate shapes of food materials. The influence of printer control parameters on the printing performance of millet-based dough is still underexplored.

Objective: This study investigates the effect of different printer control parameters on the millet dough printing performance, which is evaluated using height ratio, mass flow rate, and bending angle to enhance printing precision.

Methodology: The already optimized best printable formulation, of 40 g composite flour, 30 g shortening, 22 g jaggery, and 25 g water. The printer control parameters included nozzle diameter (ND) at 1.2, 1.6, and 2 mm; printing speed (PS) at 20, 25, and 30 mm/s; layer height (LH) at 35, 50, and 65% of ND; infill density (ID) at 40%, 60%, and 80%. Response surface methodology (RSM) and Artificial neural networks (ANN) were used for predictive modeling and comparing its statistical measures. Multi-objective optimization was performed through response surface methodology with desirability function (RSMDF) and Artificial neural networks with genetic algorithm (ANNGA). The best-performing printer control parameters were determined by validating the optimized conditions.

Results: The ID and ND strongly influenced the height ratio. LH and ND significantly affect the mass flow rate. ID and LH were the significant parameters affecting the bending angle. While comparing the statistical measures for predictive modeling, the ANN exhibited lower root-mean-square error (RMSE) values (0.0013 for height ratio, 0.0336 for mass flow rate, and 0.202 for bending angle) and higher coefficient of determination (R²) values (0.97, 0.99, and 0.98, respectively) as compared to RSM. These results indicate that ANN has slightly better prediction capabilities than RSM. Based on the prediction capability performance of multi-objective optimization techniques, the ANNGA performs marginally better in predicting height ratio and mass flow rate with lower prediction errors (0.006 and 0.063, respectively) and higher accuracy (99.993 and 99.936, respectively) than the RSMDF model.

Conclusion: The optimal condition predicted by ANNGA was as follows: 2 mm of ND, 27.75 mm/s PS, 64.98% LH, and 67.80% ID were obtained for maximum height ratio (5.633), mass flow rate (5.633 g/min), and minimum bending angle (1°). © 2025 Society of Chemical Industry.

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3D打印小米面团的打印机控制参数多目标优化。

背景：三维（3D）食品打印可以精确定制和复杂形状的食品材料。打印机控制参数对小米面团打印性能的影响尚不清楚。目的：研究不同打印机控制参数对小米面团打印性能的影响，通过高度比、质量流量和弯曲角度来提高打印精度。方法：已优化的最佳印刷配方为40克复合面粉，30克起酥油，22克砂糖和25克水。打印机控制参数包括喷嘴直径（ND）在1.2、1.6和2 mm；打印速度（PS）在20、25和30毫米/秒；层高（LH）在35、50和65%的ND；填充密度（ID）分别为40%、60%和80%。采用响应面法（RSM）和人工神经网络（ANN）进行预测建模，并比较其统计度量。采用响应面期望函数法（RSMDF）和人工神经网络遗传算法（ANNGA）进行多目标优化。通过对优化条件的验证，确定了最佳的打印机控制参数。结果：胸径和胸径对身高比影响较大。LH和ND对质量流量有显著影响。内径和LH是影响弯曲角的重要参数。在比较预测建模的统计方法时，与RSM相比，人工神经网络的均方根误差（RMSE）值更低（高度比为0.0013，质量流量为0.0336，弯曲角为0.202），决定系数（R2）值更高（分别为0.97,0.99和0.98）。这些结果表明，人工神经网络的预测能力略好于RSM。基于多目标优化技术的预测能力性能，ANNGA在预测高度比和质量流量方面的预测误差（分别为0.006和0.063）略优于RSMDF模型，预测精度（分别为99.993和99.936）略优于RSMDF模型。结论：ANNGA预测的最佳条件为：ND为2 mm， PS为27.75 mm/s， LH为64.98%，ID为67.80%，最大高度比为5.633，质量流量为5.633 g/min，最小弯曲角为1°。©2025化学工业协会。

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来源期刊

Journal of the Science of Food and Agriculture 工程技术-农业综合

CiteScore

8.10

自引率

4.90%

发文量

634

审稿时长

3.1 months

期刊介绍： The Journal of the Science of Food and Agriculture publishes peer-reviewed original research, reviews, mini-reviews, perspectives and spotlights in these areas, with particular emphasis on interdisciplinary studies at the agriculture/ food interface. Published for SCI by John Wiley & Sons Ltd. SCI (Society of Chemical Industry) is a unique international forum where science meets business on independent, impartial ground. Anyone can join and current Members include consumers, business people, environmentalists, industrialists, farmers, and researchers. The Society offers a chance to share information between sectors as diverse as food and agriculture, pharmaceuticals, biotechnology, materials, chemicals, environmental science and safety. As well as organising educational events, SCI awards a number of prestigious honours and scholarships each year, publishes peer-reviewed journals, and provides Members with news from their sectors in the respected magazine, Chemistry & Industry . Originally established in London in 1881 and in New York in 1894, SCI is a registered charity with Members in over 70 countries.