Method of using image recognition processes for improved operation of a laundry appliance
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Solution Overview
Problem
Conventional washing machines often fail to maintain optimal spin speed, leading to inadequate or excessive water extraction during the drain cycle, resulting in undesirable residual moisture content in clothes, which can cause wrinkles or prolonged cycle times.
Innovation Solution
A washing machine system equipped with a camera assembly and a controller that uses machine learning image recognition to determine the remaining moisture content by analyzing images of the wash chamber, synchronizing the camera's frame rate with the basket speed, and adjusting operating parameters to ensure optimal water extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If open-ended or time-based drain cycles are used with predetermined spin speeds, then the drain cycle can complete within a fixed time, but the remaining moisture content cannot be precisely controlled resulting in either insufficient or excessive water extraction
Solution Approach 1:
The system continuously monitors the remaining moisture content during the drain cycle and adjusts the spin speed accordingly. The controller receives moisture content signals from sensors and modifies the basket rotation speed in real-time to achieve the target moisture level, creating a closed-loop control system that precisely controls the drain cycle endpoint.
Solution Approach 2:
The patent replaces traditional mechanical timing-based control with an intelligent control system that uses image recognition technology. A camera captures images of the clothes during the spin cycle, and image processing algorithms analyze the visual data to determine moisture content, substituting mechanical sensors with optical detection methods.
2Productivity
If the basket rotates at high spin speed to extract more water, then water extraction efficiency improves, but out-of-balance loads or frictional forces prevent reaching and maintaining target spin speed
Solution Approach 1:
The system dynamically adjusts the basket spin speed during the drain cycle based on real-time moisture content measurements. Rather than maintaining a fixed high spin speed that may be unstable, the controller continuously modifies the rotation speed to optimize water extraction while adapting to changing load conditions, ensuring both productivity and reliability.
Solution Approach 2:
The moisture content sensors provide continuous feedback to the controller, which adjusts the spin speed in response to actual extraction performance. This feedback loop ensures the system maintains optimal spin speed despite out-of-balance conditions or frictional forces, reliably achieving the desired water extraction.
3Manufacturing precision
If the drain cycle runs for extended periods to extract more water, then remaining moisture content decreases, but cycle time increases causing wrinkles and unnecessary lengthy operation
Solution Approach 1:
The system uses real-time moisture content feedback to determine when the target moisture level is achieved, allowing the drain cycle to terminate precisely when needed. This prevents unnecessary extended operation that would increase cycle time without providing additional benefit, reducing both time loss and wrinkle formation.
Solution Approach 2:
The controller is pre-programmed with target moisture content values for different fabric types and load conditions. The system works toward these predetermined targets from the beginning of the cycle, adjusting parameters in advance to achieve optimal results without excessive duration, rather than relying on fixed time-based termination.
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
This solution ensures a desirable residual moisture content in clothes by dynamically adjusting the wash cycle parameters, improving wash performance and preventing wrinkles or excessive drying time.
Implementation Method 1
obtain one or more images of the wash chamber using the camera assembly, the camera assembly operating at a frame rate that is equal to the basket speed
Implementation Method 2
analyze the one or more images using a machine learning image recognition process to determine a remaining moisture content of the load of clothes
Data Source
AI summary
A washing machine appliance includes a wash basket that is rotatably mounted within a wash tub and that defines a wash chamber for receiving a load of clothes. A basket speed sensor monitors the speed of the wash basket and a camera assembly is mounted within the wash tub for taking images of the load of clothes during and/or after a wash or rinse cycle. A controller determines the basket speed, adjusts a frame rate of the camera assembly to match the basket speed, obtains a still image from the camera assembly, analyzes the image using a machine learning image recognition process to determine a remaining moisture content (RMC) of the load of clothes, and adjusts at least one operating parameter of the washing machine appliance based on the remaining moisture content, e.g., such as ending the drain cycle when a target RMC is reached.