###### Abstract  
Optimizing control specifications to prevent unbalance during the dehydration process in washing machine development is a complex task that consumes significant time and resources. Traditional methods involve expert engineers conducting various experiments and data analyses to develop optimal control specifications. However, these approaches are time-consuming and struggle to optimize diverse performance factors simultaneously. Additionally, the quality of the results heavily depends on the engineers' experience and capabilities, making it challenging to maintain consistent quality. To overcome these limitations, a new data-driven approach is proposed. This study proposes a methodology that uses Bayesian Optimization to predict the unbalance during the dehydration process and derive optimal control specifications to minimize it. Bayesian Optimization builds a predictive model based on collected data and uses an acquisition function for efficient exploration to find the optimal solution. Through this method, we automated the optimization of unbalance prevention control specifications. Applying the proposed methodology to an actual washing machine model achieved performance equivalent to that derived by expert engineers. Specifically, we succeeded in maintaining the maximum vibration during the dehydration process below the target level and reducing the time to reach high-speed rotation (RPM) ranges. The main contribution of this study is the rapid derivation of machine learning-based optimized control specifications with minimal human intervention and small-scale experiments by building a test automation system within the home appliance development process. This approach shortened the development period and improved quality consistency. washing machine; dehydration; spinning process; control; tuning; Bayesian Optimization 202515632025156  
trade-offs in which improving one aspect may degrade another. With recent stringent energy efficiency regulations and consumer preferences for reduced vibration, washing machine manufacturers are continually striving to shorten the time to reach high-speed dehydration. This effort aims to reduce overall washing time and improve energy efficiency while minimizing vibration during the dehydration process. However, achieving these goals simultaneously is a technically challenging task. In particular, the unbalance occurring during the dehydration process is a major cause of vibration and noise, which can compromise the product's safety and reliability. The unbalance phenomenon in washing machines originates from the uneven distribution of laundry and dynamically changes depending on the type, amount, and placement of clothes. When unbalance occurs, the degree of unbalance is calculated by measuring the difference in angular velocity at each rotational position of the motor; this value is called the unbalance (UB) value. If the motor continues to accelerate while unbalance is detected, vibration increases rapidly. To prevent this, the motor stops rotating when the UB value exceeds specific thresholds during operation. These thresholds are specified in the Unbalance Table (UBT)--the dehydration control specification--and the dehydration performance is determined according to the UBT values set for each dehydration stage. Therefore, developing optimal dehydration unbalance prevention control specifications under various conditions requires numerous repetitive experiments and specialized data analysis, relying heavily on the experience and intuition of expert engineers. This dependence leads to prolonged development processes, increased costs, and variability in development quality based on the engineer's expertise. Automating and streamlining the development of control specifications is essential to address these issues. Methodologies that utilize artificial intelligence techniques are required to derive optimal control specifications from small amounts of data, enhancing the consistency and reliability of the development process.