Sistem Cerdas untuk Inovasi Blender Control System Menggunakan Fuzzy Control System dengan Metode Mamdani
This research aims to establish a control system on blender by using fuzzy control system with mamdani method. In this study, researchers used input in the form of hardness level and volume of fruit to be blend, while the output is blend time (0 to 180 seconds) with assumption of constant blender velocity). Researchers used fuzzy inference control system with Mamdani method with some stages: fuzzification, inference, rule base, and defuzzification. Fuzzification changes the hardness of the fruit and the volume into a value. Inference created fuzzy output using pre-made rules. Defuzzification counted the time it takes to blend into output. Based on the results of the research, the results obtained for the sample of fruit with a level of hardness of 40%, and volume 4 (400 ml), in obtaining the minimum time required to smooth the fruit about 79 seconds. Thus the fuzzy control system can be used as an innovation to make the control system in blender. This system not only applies to blenders only, but also can be applied to other machines using fuzzy control system.
M. E. Biery, “The 10 Fastest-Growing Industries In The U.S.,” Sageworks Stats, 2017. [Online]. Available: https://www.forbes.com/sites/sageworks/2017/04/09/the-10-fastest-growing-industries-in-the-u-s/7f1b2d531ef2. [Accessed: 01-Nov-2018].
D. Berry, “High pressure processing providers and users partnering to promote technology,” Food Safety News, 2017. [Online].
G. V. Research, “Food Service Equipment Market Analysis By Product (Kitchen Purpose, Refrigeration, Storage, Ware Washing, Food Holding And Serving), By Washware Equipment (Booster Heaters, Dish Washers, Disposers, Utensil Washer) And Segment Forecasts To 2024,” California, 2016.
HegiLibrary, “Fruit Consumption Per Capita in the World,” Food & Drink, 2013. [Online]. Available: http://www.helgilibrary.com/indicators/fruit-consumption-per-capita/world/.
Marketsandmarkets.com, “Food Blender & Mixer Market by Type (High Shear, Shaft, Ribbon Mixer, Double Cone, Planetary Mixer, Screw Mixer & Blender), Application (Bakery, Dairy, Beverage, Confectionery), Technology (Batch, Continuous) & by Region - Global Trend & Forecast to 2020,” 2015.
Y. Tekmen, “An analysis of the evolution of multifunctional kitchen mixing tools,” Middle East Technical University, 2007.
E. Makin, “10 best jug blenders,” Independet, 2018. [Online]. Available: http://www.independent.co.uk/extras/indybest/house-garden/kitchen-appliances/best-jug-blender-for-smoothies-reviews-ice-soup-glass-a6938586.html.
M. Sen et al., “Analyzing the Mixing Dynamics of an Industrial Batch Bin Blender via Discrete Element Modeling Method,” Processes, vol. 5, no. 2, p. 22, 2017.
A. I. Lanas, R. Tanscheit, M. M. Vellasco, and M. A. Pacheco, “Fuzzy and neuro-fuzzy control of a fluid mixer,” Computational Intelligence and Applications, 1999.
M. S. Khan and K. Benkrid, “Design of Liquids Mixing Control System using Fuzzy Time Control Discrete Event Model for Industrial Applications,” vol. II, no. 12, pp. 3–8, 2009.
P. B. Osofisan, “Fuzzy Logic Control of the Syrup Mixing Process in Beverage Production,” Leonardo Journal of Sciences, no. 11, pp. 93–108, 2007.
R. K. Karambe and D. H. Gahane, “Automation of Grinder - An Introduction of Fuzzy Logic,” Journal of Electrical and Electronics Engineering, vol. 2014, pp. 16–20, 2014.
L. X. Wang, A Course in Fuzzy Systems and Control. New Jersey: Prentice Hall, 1997.
S. K. dan H. Purnomo, Aplikasi Logika Fuzzy. Yogyakarta: Graha Ilmu, 2010.