Method and system for achieving a target cleaning performance with a household appliance
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Solution Overview
Problem
Existing methods for achieving target cleaning performance in household appliances are dependent on the brand and model of the appliance, leading to inconsistencies in cleaning results across different models.
Innovation Solution
A method that involves obtaining master and base models to establish relationships between configuration parameter values and achievable treatment performance, allowing for the identification of suitable and acceptable parameter values to achieve a target cleaning performance independently of the appliance brand or model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cleaning parameters are adjusted to achieve target performance, then cleaning performance improves, but device complexity increases due to multiple parameters to manage
Solution Approach 1:
The system automatically determines optimal cleaning parameters by comparing appliance performance models, eliminating the need for users to manually adjust multiple parameters. The appliance self-configures its operation based on model comparisons between reference and actual appliances.
Solution Approach 2:
The system changes physical parameters (temperature, time, dosage) based on mathematical model comparisons. By calculating parameter adjustments from model differences, the system achieves target performance while managing complexity through automated parameter determination.
2Measurement precision
If users manually set all parameters individually, then parameter precision improves, but ease of operation deteriorates due to lack of technical expertise required
Solution Approach 1:
The system performs automatic parameter determination through model comparison, replacing manual user configuration. Users simply select cleaning goals while the system handles all parameter calculations and optimizations based on appliance-specific models.
Solution Approach 2:
Mathematical models act as intermediaries between user cleaning goals and actual parameter settings. The models translate high-level cleaning objectives into specific parameter values, eliminating the need for users to understand complex parameter relationships.
3Ease of operation
If fixed parameter values are provided based on manufacturer assumptions, then ease of operation improves, but cleaning performance deteriorates due to lack of customization
Solution Approach 1:
The system transitions from static fixed parameters to dynamic parameter determination. Parameters are automatically adjusted based on real-time model comparisons between reference and actual appliances, adapting to each appliance's specific characteristics while maintaining ease of use.
Solution Approach 2:
The system uses performance models as feedback mechanisms to automatically determine optimal parameters. By continuously comparing actual appliance models against reference models, the system self-corrects parameter settings to achieve target performance without user intervention.
4Ease of manufacture
If appliance hardware limitations are present, then manufacturing cost decreases, but cleaning performance deteriorates compared to higher-end models
Solution Approach 1:
The system compensates for hardware limitations by calculating parameter adjustments from model comparisons. By determining optimized parameters specific to each appliance's capabilities, lower-cost appliances can achieve performance parity with higher-end models through software-based parameter optimization.
Solution Approach 2:
The system creates virtual copies of reference appliance models to compare and determine optimal parameters. By modeling and comparing appliance characteristics mathematically, the system enables parameter optimization that compensates for physical hardware differences between appliance tiers.
Data Source
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AI summary
A method and system for achieving a target treatment performance with a household appliance is provided. It comprises the steps of obtaining a master model and a base model. The former establishes a relationship between reference configuration parameter values and values of achievable treatment performance for a reference appliance; the reference configuration parameters comprise a parameter relating to a setting of the reference appliance, a parameter relating to items to be treated and a parameter relating to a treating agent. The base model is generated similarly for the user's appliance. Suitable reference configuration parameter values are obtained using the reference model. Acceptable configuration parameter values are obtained using the base model. The two sets are compared to determine recommended configuration parameter values.