Washing apparatus and control method thereof
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Solution Overview
Problem
Existing washing apparatuses face inefficiencies in determining the optimal time to terminate the dry operation of laundry, leading to increased washing time and energy consumption, as they lack accurate methods to measure laundry load, relying on motor output and weight, which can result in inaccurate dry operation termination.
Innovation Solution
A washing apparatus equipped with a controller, motor, weight sensor, and current sensor that uses artificial intelligence model learning to derive a set condition for terminating the dry operation based on motor output, weight of laundry, and current patterns, eliminating the need for a separate humidity sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a washing apparatus uses traditional methods to detect laundry amount (motor acceleration time, DC voltage, or torque value), then the detection algorithm is simple to implement, but the measurement precision is insufficient leading to inaccurate dry operation termination timing
Solution Approach 1:
The patent transforms the detection approach by changing from direct physical measurement parameters (torque, voltage) to operational pattern parameters (current consumption patterns over time). The controller learns optimal current patterns through machine learning that correspond to different laundry amounts and dry operation stages, enabling accurate detection without complex hardware modifications.
Solution Approach 2:
The patent replaces traditional mechanical/electrical measurement systems (torque sensors, voltage meters) with an intelligent software-based system using machine learning. The controller analyzes current consumption patterns and learns to recognize when dry operation should terminate, substituting physical measurement devices with computational intelligence.
2Reliability
If the washing apparatus extends dry operation time to ensure thorough drying, then the drying completeness improves, but the washing time and energy consumption increase
Solution Approach 1:
The patent implements a feedback mechanism where the controller continuously monitors current consumption patterns during dry operation and compares them against learned optimal patterns. When the actual pattern matches the learned termination pattern, the controller stops the dry operation, providing real-time feedback control that prevents both under-drying and over-drying.
Solution Approach 2:
The patent makes the dry operation duration dynamic rather than fixed. The termination timing adapts based on learned patterns that account for variations in laundry amount, fabric type, and initial moisture levels, allowing the system to optimize drying time for each specific condition rather than using a static predetermined time.
3Reliability
If the washing apparatus uses motor output and weight alone to determine dry operation termination, then the device structure remains simple, but the reliability of termination timing is insufficient
Solution Approach 1:
The patent makes the existing current sensor serve multiple functions: it continues to monitor motor operation for basic control while simultaneously providing data for machine learning-based dry operation termination detection. This multi-functional use of the current sensor eliminates the need for additional specialized sensors while improving termination accuracy.
Solution Approach 2:
The system performs self-learning and self-optimization by automatically analyzing current patterns and improving its termination detection accuracy over time. The machine learning model is trained using data from normal operations, allowing the system to enhance its own performance without external intervention or additional complex hardware.
Data Source
AI summary
Disclosed are a washing apparatus and a control method thereof using an artificial intelligence (AI) algorithm and/or a machine learning algorithm in a 5G environment connected for Internet of things (IoT). A washing apparatus including a container in which laundry is accommodated includes a controller, a motor electrically connected to the controller and configured to rotate the container, a weight sensor electrically connected to the controller and configured to measure a weight of the laundry accommodated in the container, and a current sensor electrically connected to the controller and configured to measure a current value applied to the motor.