AI Hue Calculation for Fluid Dispensing Without Manual Calibration
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Solution Overview
Problem
Existing methods for determining the hue of fluids in dispensing systems, such as paint, require manual calibration of light sensors due to optical fiber attenuation, which is complex and resource-intensive.
Innovation Solution
A calculation unit using a pre-trained artificial intelligence model to determine fluid hue based on light intensity parameters from a light sensor, eliminating the need for manual calibration and simplifying the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration of light sensor is performed to account for optical fiber attenuation, then measurement precision is improved, but device complexity and time consumption increase
Solution Approach 1:
The system performs preliminary calibration by training an artificial neural network model offline with datasets that include optical fiber attenuation characteristics. The trained model is then deployed for real-time hue determination, eliminating the need for manual calibration during operation. This preliminary action transfers the complexity from operational calibration to one-time model training.
Solution Approach 2:
The patent replaces the manual mechanical calibration process with an automated artificial intelligence-based system. The neural network automatically compensates for optical fiber attenuation effects by learning from training data, substituting the need for manual sensor calibration adjustments and simplifying the operational process.
2Reliability
If manual calibration process is implemented, then measurement reliability is improved, but productivity decreases
Solution Approach 1:
The system implements self-service through automated calibration using the trained neural network model. The model automatically adjusts for optical fiber attenuation and determines fluid hue without requiring manual intervention, thereby maintaining measurement reliability while significantly improving system setup efficiency and operational productivity.
Solution Approach 2:
By performing calibration training in advance and deploying the trained model, the system eliminates time-consuming manual calibration steps during operation. The preliminary model training captures all necessary calibration data, enabling rapid and reliable hue determination without repeated manual calibration processes.
3Measurement precision
If additional light signal processing steps are performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple signal processing functions into a single integrated neural network model. The model simultaneously performs optical fiber attenuation compensation, hue determination, and fluid purity assessment in one unified processing step, eliminating the need for separate processing stages and reducing overall system complexity while maintaining measurement precision.
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
Provides reliable and simplified determination of fluid hue without additional processing, reducing complexity and resource consumption.
Implementation Method 1
a light sensor configured to receive a light signal that has been reflected on the fluid or transmitted through the fluid, the light signal corresponding to an optical reflection or to an optical transmission by the fluid
Implementation Method 2
a light sensor configured to receive a light signal that has been reflected on the fluid or transmitted through the fluid, the light signal corresponding to an optical reflection or to an optical transmission by the fluid
Implementation Method 3
an acquisition optical fiber configured to convey the light signal to the light sensor
Data Source
AI summary
A calculation unit for a device including at least one light source configured to emit light towards a fluid, a light sensor configured to receive a light signal representing a hue of the fluid circulating in a fluid distribution system, the light sensor being further configured to emit parameters representing a light intensity of the light signal for a given color channel, the calculation unit including an estimation module configured to determine a variable representing a hue of the fluid via an artificial intelligence model, each parameter representing a light intensity being an input variable of the model, an output variable of the artificial intelligence model being the variable representing a hue of the fluid.