Research Article Towards the Optimal Performance of Washing Machines Using Fuzzy Logic Ayodeji Olalekan Salau 1,2 and Haymanot Takele 3 1 Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria 2 Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Sriperumbudur, Tamil Nadu 600124, India 3 School of Electrical and Computer Engineering, Debre Markos Institute of Technology, Debre Markos University, Debre Markos, Ethiopia Correspondence should be addressed to Ayodeji Olalekan Salau; ayodejisalau98@gmail.com Received 1 April 2022; Revised 2 August 2022; Accepted 20 August 2022; Published 26 September 2022 Academic Editor: Cristian Mateos Copyright © 2022 Ayodeji Olalekan Salau and Haymanot Takele. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Washing machines (WMs) are common household appliances that help to save time and effort used in brushing and washing clothes. It is a common practice to use manually operated WMs, and based on their uses, WMs are classified into top and front open cloth washing machines, which operate based on an automatic control mechanism. In this paper, we present the design and simulation of an Arduino fuzzy logic-based WM control system, with an emphasis on improvement in its operating algorithm. Simulations were performed to determine the optimal time the WM takes to wash clothes, the maximum number of clothes the WM can wash per time, the acceptable dirtiness level of cloths, and the type of clothes that the machine can wash. e number of clothes to be washed, the degree of dirtiness, and the type of clothes govern the fuzzy logic control process adopted by the WM. e output voltage of the WM varies as the degree of dirtiness of the water varies from 0 to 1.95 V for very dirty water output from washed clothes and varies from 4 to 4.89 V for low-contaminated water. Another constraint considered was the load current, which increased as the number of clothes increased. e WM’s operation time is determined by the amount of voltage and load current used during its operation. As a result, the control of the WM is dependent on the dirtiness level of the clothes and the amount of load. 1. Introduction One of the frequently used machines in the household is the washing machine (WM) [1]. A WM is a device used for cleaning and washing clothes/textiles that are dirty. Washing machines (WMs) can be classified into various categories depending on their mode of operation and loading charac- teristics. Depending on the operating characteristics, WMs are classified into two: manual and automatic washing ma- chines (AWMs). is may be either semi or fully automatic depending on the loading conditions (top-loading or front- loading WMs). WMs that employ fuzzy control automatically calculate the effect of numerous variables and select the optimal wash settings, despite the fact that WMs do not al- ways use fuzzy logic and their wash durations are typically not well controlled. Performance, productivity, simplicity, and low cost are some of the advantages of this device [2]. Fuzzy logic is a concept that is utilized by computers to make de- cisions that are similar to human behavior. It helps to make production more convenient and to boost industry pro- ductivity. ese machines help to save time and effort used in washing clothes. Washing by hand involves soaking, beating, scrubbing, and rinsing dirty textiles. Manual washing con- sumes resources, labor, time, water, and detergent, and is most times costly depending on the number of clothes in consideration. Most WMs have been found to operate at low speeds, and although they do not measure the water level with a sensor, they must measure the level of inclination by weight. Furthermore, some WMs do not have temperature sensors to detect overheating of the system, and wash times are manually Hindawi Scientific Programming Volume 2022, Article ID 8061063, 11 pages https://doi.org/10.1155/2022/8061063