Development of a Fuzzy Logic Control System for the Noodle Dough Sheeting Process
DOI:
https://doi.org/10.24191/jcrinn.v11i2.561Keywords:
Fuzzy Logic , Dough Sheeting, Noodle Production, Food ProcessingAbstract
The process of dough sheeting is the most crucial step in the production of noodles. In the food industry, the sheeting process of noodle dough is important because it specifically aims to improve the quality and efficiency of noodle production. This study provides a background on the mechanical sheeting process on the mechanical sheeting process used in the food industry, highlighting its significance in shaping, and flattening dough for various goods, particularly noodles. The research identifies several challenges in the current noodle dough sheeting procedures, like uneven dough thickness, surface crack and stickiness. The study formulates a problem statement, emphasizing the need for an accurate and controlled sheeting environment to address these challenges. The proposed solution is to develop and implement a fuzzy logic control system for noodle dough sheeting. The study aims to identify critical parameters influencing dough sheeting and implement fuzzy-rule-base in monitoring the dough sheeting process using identified critical parameters. The amount of flour can be adjusted by the fuzzy control system (software) created in this study, depending on the input parameters, which include surface cracks and dough thickness. The results demonstrated that an ideal dough sheeting method is achieved by implementing a fuzzy system. This research contributes to the advancement of noodle production by introducing a fuzzy logic control system in the development of the dough sheeting process. The findings offer practical insights for small and medium-sized enterprises (SMEs), noodle manufacturers, and sheeting machine manufacturers, paving the way for more efficient and higher-quality noodle production in the culinary sector.
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Akgun, A., Sezer, E. A., Nefeslioglu, H. A., Gokceoglu, C., & Pradhan, B. (2012). An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Computers & Geosciences, 38(1), 23–34. https://doi.org/10.1016/J.CAGEO.2011.04.012
Akyazi, T., Goti, A., Oyarbide, A., Alberdi, E., & Bayon, F. (2020). A guide for the food industry to meet the future skills requirements emerging with industry 4.0. Foods, 9(4), 492. https://doi.org/10.3390/foods9040492
Chakrabarti-Bell, S., Bergström, J. S., Lindskog, E., & Sridhar, T. (2010). Computational modeling of dough sheeting and physical interpretation of the non-linear rheological behavior of wheat flour dough. Journal of Food Engineering, 100(2), 278–288. https://doi.org/10.1016/J.JFOODENG.2010.04.010
Heo, S., Lee, S. M., Bae, I. Y., Park, H.-G., Lee, H. G., & Lee, S. (2013). Effect of Lentinus edodes β-Glucan-enriched materials on the textural, rheological, and oil-resisting properties of instant fried noodles. Food and Bioprocess Technology, 6(2), 553–560. https://doi.org/10.1007/s11947-011-0735-z
Kerhervé, S. O., Guillermic, R. M., Strybulevych, A., Hatcher, D. W., Scanlon, M. G., & Page, J. H. (2019). Online non-contact quality control of noodle dough using ultrasound. Food Control, 104, 349–357. https://doi.org/10.1016/J.FOODCONT.2019.04.024
Li, M., Zhu, K. X., Peng, J., Guo, X. N., Amza, T., Peng, W., & Zhou, H. M. (2014). Delineating the protein changes in Asian noodles induced by vacuum mixing. Food Chemistry, 143, 9–16. https://doi.org/10.1016/J.FOODCHEM.2013.07.086
Liu, R., Zhang, Y., Wu, L., Xing, Y., Kong, Y., Sun, J., & Wei, Y. (2017). Impact of vacuum mixing on protein composition and secondary structure of noodle dough. LWT - Food Science and Technology, 85, 197–203. https://doi.org/10.1016/J.LWT.2017.07.009
Liu, S., Li, Y., Obadi, M., Jiang, Y., Chen, Z., Jiang, S., & Xu, B. (2019). Effect of steaming and defatting treatments of oats on the processing and eating quality of noodles with a high oat flour content. Journal of Cereal Science, 89, 102794. https://doi.org/10.1016/J.JCS.2019.102794
Liu, Shuyi, Jiang, Y., Xu, B., & Jiang, S. (2023). Analysis of the effect of rolling speed on the texture properties of noodle dough from water-solid interaction, development of gluten network, and bubble distribution. Food Chemistry, 404, 134359. https://doi.org/10.1016/J.FOODCHEM.2022.134359
Liu, Shuyi, Liu, Q., Li, X., Obadi, M., Jiang, S., Li, S., & Xu, B. (2021). Effects of dough resting time on the development of gluten network in different sheeting directions and the textural properties of noodle dough. LWT, 141, 110920. https://doi.org/10.1016/J.LWT.2021.110920
Mahadevappa, J., Groß, F., & Delgado, A. (2017). Fuzzy logic based process control strategy for effective sheeting of wheat dough in small and medium-sized enterprises. Journal of Food Engineering, 199, 93–99. https://doi.org/10.1016/J.JFOODENG.2016.12.013
Mavani, N. R., Ali, J. M., Othman, S., Hussain, M. A., Hashim, H., & Rahman, N. A. (2021). Application of Artificial Intelligence in food industry—a Guideline. Food Engineering Reviews 2021 14:1, 14(1), 134–175. https://doi.org/10.1007/S12393-021-09290-Z
Ross. (2006). Sheeting characteristics of salted and alkaline Asian noodle doughs: Comparison with Lubricated Squeezing Flow Attributes. Cereal Foods World, 51(5), 191–196. https://doi.org/10.1094/CFW-51-0191
Singh J, Kumar R, Kumar V, Chatterjee S. (2024). Exploring the dynamics of bigdata adoption in the Indian food industry with fuzzy analytical hierarchical process. British Food Journal, 126(6), 2310–2327. https://doi.org/10.1108/BFJ-01-2024-0012
Song, M., Liu, C., Hong, J., Li, L., Zheng, X., Bian, K., & Guan, E. (2019). Effects of repeated sheeting on rheology and glutenin properties of noodle dough. Journal of Cereal Science, 90, 102826. https://doi.org/10.1016/J.JCS.2019.102826
Tosun, M., Dincer, K., & Baskaya, S. (2011). Rule-based Mamdani-type fuzzy modelling of thermal performance of multi-layer precast concrete panels used in residential buildings in Turkey. Expert Systems with Applications, 38(5), 5553–5560. https://doi.org/10.1016/J.ESWA.2010.10.081
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
Zimmermann, H.-J. (2001). Fuzzy Set Theory—and Its Applications. Springer Netherlands. https://doi.org/10.1007/978-94-010-0646-0
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Copyright (c) 2026 Suzanawati Abu Hasan, Aina Irsalina Itqan Rosli, Yeong Kin Teoh, Diana Sirmayunie Mohd Nasir (Author)

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