Project SAMPAY: A Sensor-Activated Machine for Cloth-Placing and Drying

"Project SAMPAY: A Sensor-Activated Machine for Cloth-Placing and Drying" tackles the problems with conventional clothing drying techniques, which either depend on the weather or use a lot of energy, like electric dryers. In order to automate the drying process, this study investigates the creation of a sensoractivated cloth drying machine that incorporates temperature, humidity, and moisture sensors. The technology automatically modifies drying parameters to guarantee energy efficiency and fabric care by continuously observing ambient elements and fabric conditions. Because less human involvement is required, the drying process is more effective, convenient, and energy-efficient. Additionally, it offers the possibility of more intelligent, environmentally friendly solutions by utilizing machine learning and IoT capabilities for customized drying cycles. In addition to improving the user experience, the article shows how these advances might help reduce the environmental effect and energy consumption of traditional drying processes. The results lend credence to the increasing promise of sensor-based drying technologies in creating more ecological and effective home appliances.

Authors

Publication Details

Journal
International Journal of Innovative Research in Multidisciplinary Education
Published
2025-03-22
DOI
https://doi.org/10.58806/ijirme.2025.v4i3n07
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
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article

Project SAMPAY: A Sensor-Activated Machine for Cloth-Placing and Drying

Freddie O. Orperia, Maribel S. Abalos, Crestian A. Agustin, Frederick Gallo et al.
International Journal of Innovative Research in Multidisciplinary Education
Industrial Vision Systems and Defect Detection
article

Project SAMPAY: A Sensor-Activated Machine for Cloth-Placing and Drying

Freddie O. Orperia, Maribel S. Abalos, Crestian A. Agustin, Frederick Gallo, Arnold L. Alburo, Rafael S. Ramos
article en

Abstract

No abstract available for this paper.

International Journal of Innovative Research in Multidisciplinary EducationVol. 04(03)
Openalex Percentile: Top 93%
Industrial Vision Systems and Defect Detection
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