Effect of fabric properties on the laundry movement, mechanical action, and motor current of front-loading washers

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 f...

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Bibliographic Details
Published in:Textile research journal 2024-02, Vol.94 (3-4), p.375-389
Main Authors: Song, Eugene, Kim, Mingyeong, Chang, Seokhee, Choi, Heewon, Lim, Siyeon, Kim, Sungmin, Kim, Jooyoun
Format: Article
Language:English
Subjects:
Color fading
Damage
Decision trees
Fabric analysis
Fabrics
Hygiene
Information processing
Laundry
Moisture
Optimization
Parameters
Personal hygiene
Rotation
Soil mechanics
Textiles
Washing
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Summary: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.
ISSN:0040-5175
1746-7748
DOI:10.1177/00405175231200651