Online Grade Selection for Composite Sheet Weight Measurements
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Solution Overview
Problem
Conventional methods for measuring the weight of composite sheet materials, such as ceramic-coated lithium-ion battery separators, face inaccuracies due to uncertainty in the composite sheet material grade during online production, requiring manual calibration and being prone to errors from production anomalies and contamination.
Innovation Solution
An automated online method using differentiating spectral markers, such as peaks in the infrared spectrum, to identify the correct composite sheet material grade and select appropriate calibrations for accurate weight measurements of the coating and sheet material, employing a measurement apparatus with sensors and a computing device to determine physical parameters like weight, thickness, or density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration is used for different composite sheet material grades, then measurement accuracy can be maintained, but human error and operational complexity increase
Solution Approach 1:
The system automatically identifies the composite sheet material grade through spectral analysis and selects the appropriate calibration curve without manual intervention. The computing device autonomously processes the spectrum, determines the grade, and loads the corresponding calibration data, eliminating the need for operator involvement in calibration selection.
Solution Approach 2:
The manual mechanical process of selecting calibration curves is replaced with an automated optical and computational system. Spectral markers detected by the sensor are processed by algorithms to automatically identify grades and select calibrations, substituting human operational decisions with automated digital processing.
2Measurement precision
If multiple calibrations are stored for different grades, then measurement accuracy improves, but device complexity increases
Solution Approach 1:
The calibration system is segmented into separate, grade-specific calibration curves stored in the computing device. Each calibration curve is associated with specific spectral markers and grade information, allowing the system to store multiple calibrations in an organized manner and select the appropriate one based on detected spectral characteristics.
Solution Approach 2:
Spectral markers act as intermediaries between the physical composite sheet material and the calibration system. The markers provide objective, measurable characteristics that automatically trigger the selection of corresponding calibration curves, serving as a bridge that eliminates the need for direct manual calibration selection.
3Ease of operation
If automated grade selection is implemented, then operational ease improves, but measurement precision may deteriorate due to unknown production variations
Solution Approach 1:
The system continuously monitors spectral markers during production and uses this feedback to automatically select the appropriate calibration curve. The spectral analysis provides real-time information about the material grade, which feeds back into the calibration selection process, ensuring that the correct calibration is applied based on actual material characteristics rather than assumptions.
Solution Approach 2:
The system adapts to production variations by detecting changes in spectral marker characteristics and adjusting calibration selections accordingly. When production anomalies or contamination occur, the spectral analysis can identify deviations and trigger appropriate calibration changes to maintain measurement accuracy under varying conditions.
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
This approach enhances measurement accuracy and process control by automatically selecting the correct calibration, reducing human error and accounting for production variations, ensuring precise weight measurements even in the presence of contaminants or unknown changes in the manufacturing process.
Implementation Method 1
using differentiating spectral markers, such as peaks in the infrared spectrum, to identify the correct composite sheet material grade
Data Source
AI summary
A measurement apparatus includes a first sensor, scanner head, and computing device coupled to the first sensor that stores relative signal level ranges at selected spectral marker region(s) compared to a common region each corresponding to a composite sheet material grade and an associated sensor calibration. The computing device measures a first signal in the spectral marker region and a second signal in the common region of a composite sheet including a sheet material and a high-z material, determining a current relative signal level comparing a current signal level of the first and second signal, and identifying a current composite sheet material grade for the composite sheet from the composite material grades using the current relative signal level. Based on the current composite material grade a current sensor calibration is chosen from the sensor calibrations, and ≥1 physical parameter for the composite sheet is determined from the current sensor calibration.