Implementing an automatic temperature controller based on real-time fabric classification for a smart ironing appliance

Abstract

Ironing is a common household task that requires careful temperature control to avoid damaging different kinds of fabric. The manual temperature adjustment used by traditional irons can cause errors and even damage to the fabric. Using a machine learning-based fabric classifier, this research presents a novel approach to automating iron temperature control. The iron temperature is automatically adjusted based on the classifier response. The proposed solution seeks to improve the ironing experience by reducing fabric damage and increasing energy efficiency. This study aims at developing a user-friendly and reasonably priced smart ironing system by combining computer vision and machine learning techniques with the hardware control system.