Laundering is essential for maintaining personal hygiene but can lead to fabric damage, such as color fading and fiber loss, caused by frequent and excessive mechanical action. Front-loading washers utilize mechanical force such as falling, rotating, and sliding to remove soil from textiles, where fabric properties influence these movements, resulting in varied mechanical actions depending on fabric type. Herein, the relationship between fabric properties and movement pertaining to mechanical action was analyzed, focusing on correlated fabric movements and motor torque current parameters. The results demonstrate that fabric properties, such as wet friction, moisture regain, thickness, and stiffness, significantly influence fabric movement in laundering. Moisture regain was associated with fabric movement, causing hydrophilic fabrics to undergo concentrated and repeated movements, resulting in higher rates of thread removal. In addition, heavier, thicker, and stiffer fabrics displayed dynamic changes in fabric shape, contributing to increased Poka-Dot removal. The motor current data required for rotating the drum was analyzed in association with the fabric properties. Using the current parameters as internal nodes of a decision tree model, fabric types were successfully classified. The results imply that the real-time current data and laundry movement information can be utilized for optimizing washing technologies for different fabric types, ultimately maximizing washing efficiency and minimizing fabric damage.
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