Load detection and cycle modification in laundry appliance applications
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Solution Overview
Problem
Users often select the wrong laundry cycle, leading to potential damage or inefficient resource use due to unawareness of the differences between preset cycles, which existing technologies fail to address effectively.
Innovation Solution
A method and system that use measurements such as absorption ratio, retention ratio, dry mass, wet mass, and spun mass of a laundry load during a pre-rinse cycle to determine load parameters, employing a machine learning model to infer the optimal laundry cycle type, and optionally confirming or updating user-selected settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select laundry cycles based on preset options, then the appliance is easy to operate, but users often select the wrong cycle leading to potential damage or inefficient resource use
Solution Approach 1:
The system performs self-detection of load characteristics (mass, absorption ratio, retention ratio) and automatically determines the appropriate laundry cycle without user intervention. The controller infers load type from measurements taken during a pre-rinse cycle and selects optimal cycle parameters, allowing the appliance to serve itself in the cycle selection process.
Solution Approach 2:
The system measures actual load properties during operation and uses this feedback to adjust and determine the optimal laundry cycle. By continuously monitoring absorption ratio, retention ratio, and mass measurements, the system adapts the cycle selection to match the actual load characteristics, ensuring reliable and appropriate cycle selection.
2Device complexity
If the appliance provides only preset cycle options, then the device complexity is low, but it cannot provide customized settings for consumer-specific loads
Solution Approach 1:
The system transitions from static preset cycles to dynamic, adaptive cycle determination. Based on real-time measurements of load mass, absorption ratio, and retention ratio during the pre-rinse cycle, the controller dynamically adjusts cycle parameters to optimize treatment for the specific load type, enabling customized settings without requiring complex pre-programmed options for every possible load scenario.
Solution Approach 2:
The system changes key operational parameters (cycle type, water temperature, agitation intensity, spin speed) based on measured load characteristics. By varying these parameters according to the inferred load type from absorption and retention ratio measurements, the system provides customized cycle settings that adapt to different load requirements while maintaining a simple preset interface structure.
3Measurement precision
If the appliance uses detailed measurements during pre-rinse cycle, then the measurement precision is high, but the loss of time increases due to additional measurement steps
Solution Approach 1:
The system performs measurements during the pre-rinse cycle, which is an existing necessary step in the laundry process. By conducting absorption ratio, retention ratio, and mass measurements during this preliminary treatment phase rather than as a separate step, the system obtains precise load characterization data without adding extra time to the overall laundry process.
Solution Approach 2:
The system combines multiple measurement functions (mass measurement, absorption ratio measurement, retention ratio measurement) into a single integrated pre-rinse cycle. By merging these measurement activities that would otherwise require separate steps into one unified process, the system achieves high measurement precision while minimizing the total time lost to measurements.
Data Source
AI summary
Inferring the laundry cycle type for a load of laundry items is provided. Measurements are performed of a laundry load in a drum of a laundry appliance during a pre-rinse cycle, the measurements including one or more of an absorption ratio of the laundry load, a retention ratio of the laundry load, a dry mass of the laundry load, a wet mass of the laundry load, or a spun mass of the laundry load. A model is used to determine load parameters based on the measurements. The load parameters are used to determine a laundry cycle type for the laundry load.