Laundry Appliance Sensor Control for Personalized Fabric Care

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing laundry appliances require manual setting adjustments by operators, which can be intimidating and lead to poor results, especially for inexperienced users, due to the variety of fabric types, load sizes, and soiling levels, and often lack optimal setting utilization by casual users.

Innovation Solution

Integration of machine learning models within laundry appliances that utilize sensors, such as tactile surface sensors and spectral sensors, to determine fabric attributes and treatment processes, allowing for personalized control based on user inputs, historical data, and sensor data, including temperature sensing, to automate and personalize the laundry process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual setting adjustments are provided for various fabric types and conditions, then the appliance can handle diverse laundry needs, but the operation becomes intimidating and complex for inexperienced users

Engineering Contradiction:

Improveability to handle diverse fabric types and conditionsVSAvoiduser operation simplicity

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The appliance performs self-diagnosis and automatic setting selection by analyzing sensor data about fabric type, load size, and soiling level. The system independently determines optimal washing parameters without requiring user expertise in selecting appropriate settings for different fabric types and conditions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical setting adjustments are replaced with an automated electronic control system that uses sensors and machine learning models to automatically select and adjust washing parameters based on detected laundry characteristics.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If multiple settings and controls are provided for optimal performance, then the appliance can achieve better washing results, but casual operators may not know how to use them to maximum benefit

Engineering Contradiction:

Improvewashing result qualityVSAvoidsettings utilization by casual users

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The control system automatically determines optimal washing settings by analyzing sensor data about fabric type, load characteristics, and soiling level. The system independently selects appropriate temperature, cycle time, and mechanical action parameters without requiring user knowledge of optimal settings for different conditions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors sensor data during the washing process and automatically adjusts settings based on real-time feedback about load conditions, fabric response, and washing progress to maintain optimal performance.

Inventive Principle:

Principle #23Feedback

3Extent of automation

If automated machine learning models are integrated to determine fabric attributes and control treatment, then the appliance becomes more intelligent and personalized, but the device complexity increases

Engineering Contradiction:

Improvelaundry process automation levelVSAvoidappliance system complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

A single integrated control system performs multiple functions including fabric identification, soiling level detection, treatment selection, and parameter optimization. The machine learning model serves as a universal decision-making component that handles various laundry types and conditions through a unified automated process.

Inventive Principle:

Principle #6Universality (Multi-functionality)

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 more intelligent and automated laundry appliances that accurately determine fabric types and soiling levels, allowing for optimized settings, improving washing and drying results and personalizing the process according to user preferences, leading to better fabric care and increased user satisfaction.

Implementation Method 1

Sensor(s), including tactile surface sensors

Methodology Applied

Scientific EffectTactile sensing:

Implementation Method 2

optionally spectral sensors

Methodology Applied

Scientific EffectSpectral sensing:

Implementation Method 3

temperature sensing and other sensor data

Methodology Applied

Scientific EffectTemperature sensing:

Data Source

PatentEP3682049B1Personalized laundry appliance

Publication Date: 2023.02.22 MIDEA GROUP CO LTD

Figure 1
Figure 2A
Figure 2B

AI summary

Laundry appliances use machine learning models and/or personalization to provide better treatments. As one example, a laundry appliance has a chamber in which laundry items are placed for treatment. Sensor (s) are positioned to sense contents of the chamber or to sense the laundry items as they are loaded into the chamber. The machine learning model uses data from these sensors to determine various attributes of the laundry items and/or the treatment, such as the type of fabric and/or how dirty the items are, and the treatment process is controlled accordingly. Personalized data, such as an individual's preferences for laundry treatments or his sensitivies and allergies, may also be used to personalize the treatment process.