Washing machine for adjusting operation based on injected detergent and method for controlling the same
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Solution Overview
Problem
Existing washing machines lack accurate detection of detergent type and amount, leading to ineffective washing cycles and rinsing operations, especially when users manually add detergent, and are limited by reliance on conductivity sensors which can misclassify detergents and suffer from signal noise.
Innovation Solution
A washing machine equipped with conductivity and turbidity sensors that detect the physical properties of washing and rinsing water, corrected for temperature, and a neural network model to determine the type and amount of detergent, adjusting washing and rinsing cycles accordingly, and requesting additional detergent if needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only conductivity sensor is used to detect detergent type, then the device complexity is reduced, but the measurement precision deteriorates due to misclassification of detergent types
Solution Approach 1:
The patent combines multiple sensors (conductivity sensor, turbidity sensor, and temperature sensor) into an integrated detection system. This merging of sensors allows the system to collect multiple parameters simultaneously, enabling more accurate detergent type classification through comprehensive data analysis rather than relying on a single conductivity measurement.
Solution Approach 2:
The patent changes the detection parameters from单一的conductivity measurement to a multi-parameter approach including conductivity, turbidity, and temperature. By analyzing changes in multiple physical parameters and their relationships, the system can distinguish between different detergent types more accurately, resolving the misclassification issue.
2Ease of operation
If conductivity sensor is used in noisy environment, then the ease of operation is maintained, but the reliability deteriorates due to signal noise
Solution Approach 1:
The patent implements a feedback mechanism where the control unit receives sensor data, processes it through algorithms that account for noise interference, and adjusts the detection parameters accordingly. The system continuously monitors the washing water conditions and refines its measurements based on feedback from multiple sensors, improving reliability in noisy environments.
Solution Approach 2:
The control unit acts as an intermediary that processes and filters the raw sensor signals before making decisions. It integrates data from multiple sensors and uses algorithms to eliminate noise interference, providing a reliable interpretation of detergent conditions without requiring changes to the simple sensor operation.
3Ease of operation
If manual detergent addition is allowed, then the ease of operation is improved, but the measurement precision deteriorates due to inability to detect actual detergent amount
Solution Approach 1:
The system enables self-service detergent addition by users while maintaining detection capability. The sensors automatically detect the detergent conditions in the washing water regardless of how the detergent was added, and the control unit adjusts the washing parameters based on the detected detergent type and amount, making the system adaptable to manual addition.
Solution Approach 2:
The patent replaces the mechanical approach of automatic detergent dispensing with a sensor-based detection system. Instead of mechanically controlling detergent addition, the system uses conductivity and turbidity sensors to detect the actual detergent conditions in the water and automatically adjusts washing parameters, achieving precision measurement without mechanical intervention.
4Device complexity
If general washing cycle is performed irrespective of detergent type, then the device complexity is reduced, but the productivity deteriorates due to ineffective washing
Solution Approach 1:
The patent implements dynamic washing cycle adjustment based on real-time detergent detection. The control unit modifies washing parameters such as water temperature, washing time, and drum rotation speed according to the detected detergent type and amount. This dynamic adaptation optimizes washing effectiveness for different detergent conditions without requiring complex manual programming.
Solution Approach 2:
The system changes washing cycle parameters (temperature, time, mechanical action) based on detected detergent properties. By automatically adjusting these parameters according to the detergent type and concentration detected by the sensors, the system maintains high washing productivity while keeping the control logic relatively simple.
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 accurate detection and adjustment of washing and rinsing cycles, ensuring effective detergent use, reducing misclassification and signal noise, and providing user feedback for improved washing performance.
Implementation Method 1
a detergent detection sensor configured to detect a first conductivity and a first turbidity of the washing water supplied to the first tub
Implementation Method 2
a detergent detection sensor configured to detect a first conductivity and a first turbidity of the washing water supplied to the first tub
Data Source
AI summary
A washing machine capable of operating in an IoT environment through a 5G communication network and estimating the type and amount of injected detergent through a neural network model created according to machine learning, and a method of controlling the washing machine, are provided. The washing machine may include a first tub into which laundry is loaded, a water supplier configured to supply washing water to the first tub, a detergent detection sensor configured to detect first conductivity and first turbidity of the washing water, and a processor configured to determine a washing cycle of the washing machine based on detected information.