Machine Learning Classifier for Automated Cleaning Outcome Scoring
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Solution Overview
Problem
Automated cleaning machines face challenges in consistently achieving effective cleaning outcomes due to variations in water temperature, pressure, quality, and chemical concentrations, which can lead to unsatisfactory cleaning results.
Innovation Solution
The implementation of a machine learning-based system that classifies or scores cleaning outcomes using a trained classifier. This system monitors various cleaning process parameters, such as wash and rinse temperatures, times, and chemical concentrations, to determine the efficacy of the cleaning process and adjust parameters accordingly to achieve a satisfactory outcome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated cleaning machines use fixed cleaning process parameters, then the device complexity is low, but the cleaning outcome consistency deteriorates due to variations in water temperature, pressure, quality, and chemical concentrations
Solution Approach 1:
The patent implements dynamic adjustment of cleaning process parameters by using a machine learning classifier that continuously monitors cleaning outcomes and automatically modifies water temperature, pressure, chemical concentrations, and other parameters in real-time to maintain consistent cleaning quality despite variations in input conditions
Solution Approach 2:
The system incorporates a feedback mechanism where the machine learning classifier evaluates cleaning outcomes and uses this information to adjust subsequent cleaning cycles. The classifier receives input about cleaning effectiveness and automatically modifies process parameters for the next cycle, creating a closed-loop control system that improves consistency
2Reliability
If the cleaning machine adjusts multiple process parameters in real-time, then the cleaning outcome effectiveness improves, but the ease of operation deteriorates due to automated control complexity
Solution Approach 1:
The cleaning machine performs self-adjustment of cleaning parameters through the machine learning classifier, which automatically monitors outcomes and modifies water temperature, pressure, and chemical concentrations without requiring operator intervention. The system serves itself by making real-time decisions to optimize cleaning effectiveness
Solution Approach 2:
The system performs preliminary classification of cleaning outcomes during the cleaning process itself, allowing adjustments to be made before the next cleaning cycle begins. This preliminary evaluation enables proactive optimization rather than reactive correction
3Measurement precision
If the system monitors multiple cleaning process parameters, then the measurement precision of cleaning outcomes improves, but the device complexity increases due to additional sensors and data processing
Solution Approach 1:
The machine learning classifier serves multiple functions simultaneously: it classifies cleaning outcomes, identifies which parameters most influenced the outcome, and determines optimal adjustments for subsequent cycles. This multi-functional approach consolidates what would otherwise require separate systems into a single integrated component
Data Source
AI summary
An automated cleaning machine includes a trained cleaning outcome classifier that automatically classifies or scores cleaning outcomes for a cleaning machine using machine learning techniques. The cleaning outcome classifier may be trained on training data comprising a plurality of training inputs and a known output for each of the plurality of training inputs. Each of the plurality of training inputs may include one or more cleaning process parameters corresponding to a cleaning process executed by a cleaning machine executed during a training phase. The known output for each training input may include a cleaning outcome classification or score. The cleaning outcome of a novel cleaning process may then be classified or scored with the trained cleaning outcome classifier based on one or more cleaning process parameters corresponding to the novel cleaning process.