AI-Based Laundry Material Identification for Adaptive Dryer Control
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Solution Overview
Problem
Existing dryers lack the ability to accurately identify the material of laundry and adjust drying settings accordingly, leading to inefficient and potentially damaging drying processes.
Innovation Solution
A dryer equipped with temperature and dryness sensors, along with an AI model, to identify laundry material and adjust settings such as drum rotation speed, heating temperature, and operation time based on material type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional drying methods are used without material identification, then the drying process is simple and fast, but the drying effectiveness is poor and laundry may be damaged
Solution Approach 1:
The system performs preliminary material identification using temperature and dryness sensors before executing the drying process. The AI model analyzes sensor data to determine fabric type, enabling the system to prepare appropriate drying parameters in advance, thereby improving drying effectiveness without excessive complexity
Solution Approach 2:
The system continuously monitors temperature and dryness during the drying process using sensors, feeding this data back to the AI model which adjusts drying parameters in real-time. This closed-loop feedback mechanism ensures optimal drying effectiveness while maintaining manageable system complexity through adaptive control
2Measurement precision
If expensive sensors are used for material identification, then the identification accuracy is high, but the cost increases
Solution Approach 1:
Instead of using expensive dedicated fabric identification sensors, the system creates a computational model (AI model) that replicates material identification capabilities by analyzing data from ordinary temperature and dryness sensors. This virtual copying approach achieves high identification accuracy without the cost of specialized hardware
Solution Approach 2:
The system replaces physical expensive sensors with an intelligent software-based AI model that processes data from simple, low-cost temperature and dryness sensors. This substitution of mechanical sensing with intelligent data processing achieves accurate material identification at lower cost
3Reliability
If a fixed drying setting is used for all materials, then the operation is simple, but the drying effectiveness varies and some materials may be damaged
Solution Approach 1:
The system dynamically adjusts drying settings based on real-time material identification and sensor feedback. The AI model continuously optimizes drying parameters such as temperature and duration according to the specific fabric type detected, providing material-specific protection while maintaining ease of operation through automated adaptive control
4Measurement precision
If material identification is performed continuously, then the identification accuracy is maintained, but the energy consumption increases
Solution Approach 1:
The system performs material identification periodically at key stages rather than continuously. The AI model analyzes sensor data at intervals during the drying process, maintaining identification accuracy while significantly reducing energy consumption compared to continuous monitoring and re-identification
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and material-specific drying processes, reducing damage to laundry and improving drying effectiveness.
Implementation Method 1
a first temperature sensor configured to produce first temperature data corresponding to a temperature of the heated air
Implementation Method 2
a second temperature sensor configured to produce second temperature data corresponding to a temperature in the drum
Implementation Method 3
a dryness sensor in the drum and configured to produce dryness data corresponding to a dryness level of laundry accommodated in the drum
Implementation Method 4
a heating element configured to heat air
Implementation Method 5
a fan configured to blow the heated air into the drum to dry the laundry
Data Source
AI summary
A dryer may include: a drum to accommodate laundry to be dried; a heating element to heat air; a fan to blow the heated air into the drum to dry the laundry; a first temperature sensor to produce first temperature data corresponding to a temperature of the heated air; a second temperature sensor to produce second temperature data corresponding to a temperature in the drum; a dryness sensor in the drum to produce dryness data corresponding to a dryness level of the laundry; and at least one processor configured to: extract a temperature feature point based on the first and second temperature data, extract a dryness feature point based on the dryness data, identify, by an AI model, a material of the laundry based on the temperature feature point and the dryness feature point, and change a dry setting of the dryer based on the identified material.