## 2 Related Work  
Traditionally, engineers have developed and refined dehydration control specifications through repeated experiments and analyses. This approach, based on long-accumulated expertise within companies, involves engineers repeatedly testing and manually adjusting the control specifications. Each time a parameter in the UB Table changes, the engineer edits and compiles the control code, downloads it to the MCU (Microcontroller Unit), and then manually runstests and analyzes the results. If the experimental results fail to meet the target performance (vibration or unbalance criteria), they readjust the parameters and repeat. Although this method is standard practice in the industry, it is labor-intensive and time-consuming, making it a natural benchmark for comparison with the automated BO approach. The engineer-driven manual process was highly inefficient for applying new parameters proposed by the BO algorithm, as BO generates a new candidate UB Table at every iteration. Applying these parameters manually in the absence of automation significantly slowed down the experimental loop. Research has been conducted through various approaches in previous years to enhance the efficiency and safety of the dehydration process in washing machines. Studies have focused on developing active balancing mechanisms to suppress vibrations due to unbalance [1; 2; 3], designing suspension structures [4; 5], dynamic modeling [6; 7], and applying control algorithms. Various control algorithm approaches have been explored to enhance dehydration performance in washing machines. [PERSON] [8], [PERSON] and [PERSON] [9; 10], and [PERSON] [11] proposed algorithms for measuring and evaluating the magnitude of unbalance caused by fixed loads or for estimating the position of unbalance. [PERSON] and [PERSON] [12] introduced a semi-active vibration control method that adjusts the maximum force generated by suspension elements, thereby enhancing the dynamic stability of front-loading washing machines. [PERSON] and [PERSON] [13] developed a dynamic model combining mechanical and stochastic algorithms to improve vibration suppression efficiency. However, their reliance on simplified two-dimensional models and single-variable unbalance limited the applicability of their approach to real-world spin performance. [PERSON] [14] employed reinforcement learning to optimize spin speed by incrementally adjusting the rotation speed during dehydration to minimize unbalance. [PERSON] and [PERSON] [15] first proposed a dehydration simulator through Monte Carlo simulation based on experiments estimating the tub's vibrations. They suggested control factors that minimize the dehydration time and vibrations using the simulator. Despite these studies, significant variations and uncertainty remain in the maximum vibration levels and entry times during repeated dehydration experiments, even under identical conditions. Overcoming these issues requires optimizing the dehydration algorithm. Existing washing machines use algorithms that stop and retry if the vibration value exceeds a threshold during the rotation cycle. Determining algorithms under various load conditions requires substantial effort because there is a trade-off between the total vibration and dehydration time. With advancements in artificial intelligence technology, prediction control methods based on recurrent neural networks (RNNs) have recently been used to solve the dehydration unbalance problem. However, they require large amounts of training data and additional technology to determine active control factors. Conversely, BO has been utilized across engineering disciplines for variable optimization. It effectively performs repeated search processes for global optimization and black-box problems by efficiently finding optimal solutions using previous search results [16; 17; 18]. Recent studies have used BO in various contexts based on these characteristics.