AI Laundry Load Sensing From Motor Current During Tub Acceleration
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Solution Overview
Problem
Conventional laundry treatment apparatuses face limitations in accurately sensing laundry weight and quality due to reliance on experimental constants and simple comparison methods, leading to inefficiencies in spin-drying operations and increased energy consumption.
Innovation Solution
A laundry treatment apparatus utilizing a current sensing unit and a controller that employs an artificial neural network based on machine learning to accurately classify laundry weight and quality by processing current values during accelerated rotation of the washing tub, optimizing the acceleration gradient and reducing the quantity of data needed for determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If experimental constants and simple comparison methods are used to sense laundry weight, then the device complexity is reduced, but the measurement precision deteriorates
Solution Approach 1:
The patent replaces conventional mechanical/sensor-based weight sensing methods with an artificial intelligence-based system that uses motor current data during acceleration. Instead of relying on physical sensors and experimental constants, the system uses machine learning algorithms to infer laundry weight from electrical parameters, thereby improving measurement precision while maintaining relatively simple device complexity.
Solution Approach 2:
The patent changes the sensing approach from direct mechanical measurement to electrical parameter analysis. By monitoring motor current during acceleration phases and using AI algorithms to interpret these parameters, the system achieves more accurate laundry weight sensing without adding complex hardware, thus resolving the contradiction between device simplicity and measurement accuracy.
2Measurement precision
If experimental constants are used to improve laundry weight sensing accuracy, then the measurement precision is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent implements a self-learning system where the AI algorithm automatically adapts to different laundry conditions without requiring manual setting of experimental constants. The system performs self-calibration by learning from motor current patterns during acceleration, eliminating the need for users to manually adjust sensitivity parameters or input experimental data, thus improving ease of operation while maintaining high measurement precision.
Solution Approach 2:
The system performs preliminary learning and adaptation during initial operation phases, automatically establishing the relationship between motor current and laundry weight before actual measurement begins. This preliminary action eliminates the need for manual configuration of experimental constants, making the system easy to operate while achieving accurate measurements.
3Device complexity
If simple comparison methods are used to sense laundry weight, then the device complexity is reduced, but the measurement precision and adaptability deteriorate
Solution Approach 1:
The patent employs a dynamic AI-based sensing algorithm that adapts to different laundry conditions in real-time. Instead of using fixed comparison thresholds, the system continuously learns from motor current patterns and adjusts its measurement criteria accordingly, enabling it to accurately sense various laundry weights and types while maintaining relatively simple device complexity through software-based adaptability.
Solution Approach 2:
The system dynamically changes sensing parameters based on operating conditions by using AI algorithms that interpret motor current data in context. This allows the system to adapt to different laundry weights, types, and washing conditions without requiring complex hardware modifications, thereby improving adaptability while keeping device complexity manageable.
4Device complexity
If inaccurate laundry weight measurement is accepted, then the device complexity is reduced, but the productivity deteriorates
Solution Approach 1:
The patent replaces inaccurate simple sensing methods with an AI-based measurement system that provides accurate laundry weight data. This enables the washing machine to optimize spin-drying operations and other process parameters based on precise measurements, thereby improving productivity and energy efficiency without significantly increasing device complexity through software-based intelligence.
Solution Approach 2:
The system uses accurate AI-based laundry weight measurement to provide feedback for optimizing washing and spin-drying operations. By continuously monitoring and adjusting process parameters based on precise weight data, the system improves productivity and energy efficiency while maintaining relatively simple device architecture through intelligent control algorithms.
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
The solution enables rapid and accurate sensing of laundry weight and quality, improving classification accuracy and reducing energy consumption by optimizing data processing and classification based on machine learning algorithms.
Implementation Method 1
a current sensing unit configured to sense current of the motor
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
Disclosed is an artificial intelligence laundry treatment apparatus including a washing tub (4) configured to receive laundry, the washing tub being configured to be rotatable, a motor (9) configured to rotate the washing tub, a controller (60) configured to control the motor such that the washing tub is accelerated to a predetermined target speed at an acceleration gradient of 1.5 to 2.5 rpm/s within a range within which the laundry moves in the washing tub, and a current sensing unit (75) configured to sense current of the motor, wherein the controller is configured to obtain at least one of laundry weight or laundry quality from output of an output layer of an artificial neural network pre-trained based on machine learning using a current value sensed by the current sensing unit during accelerated rotation of the washing tub as input of an input layer of the artificial neural network.