Force Sensor Medium Calibration for Fatigue-Driven Resistance Drift
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Solution Overview
Problem
Polyethylene-based force-sensitive resistors (FSRs) suffer from material fatigue and quantum tunneling effects, leading to inconsistent resistance values and reduced accuracy over time, necessitating individual calibration and costly proprietary processes.
Innovation Solution
A system employing a programmable gain amplifier and machine learning algorithms dynamically adjusts force readings by sampling at high frequencies, aligning data with a baseline resistance vs. force curve, compensating for material fatigue and positional variations, and continuously refining calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polyethylene is used in making force-sensitive resistors, then the sensor can detect force through resistance changes, but the polyethylene does not decompress to the original thickness resulting in different absolute values with subsequent compressions
Solution Approach 1:
The system dynamically adjusts the gain setting based on the measured resistance at peak force. The gain is calculated as gain = (desired_output - offset) / measured_resistance, allowing the measurement system to adapt to the changing polyethylene properties after repeated compression cycles, thereby maintaining measurement accuracy despite material degradation
Solution Approach 2:
The patent changes the electrical parameter (gain setting) to compensate for the mechanical parameter change (polyethylene thickness degradation). By recalibrating the gain based on current resistance measurements, the system compensates for the loss of mechanical recovery in the polyethylene material
2Measurement precision
If individual calibration is performed for each sensor, then measurement accuracy is improved, but manufacturing time and cost increase
Solution Approach 1:
The sensor performs self-calibration by measuring its own resistance at peak force and automatically calculating the appropriate gain setting. The system uses the sensor's inherent electrical properties (resistance measurement) to determine its own calibration parameters, eliminating the need for external calibration equipment and manual adjustment
Solution Approach 2:
The system measures the actual resistance of each sensor during operation and uses this feedback to calculate the optimal gain setting. This closed-loop approach allows each sensor to be automatically calibrated based on its actual performance characteristics rather than relying on factory pre-calibration
3Reliability
If gain setting is adjusted dynamically, then measurement accuracy is maintained over time, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical calibration mechanisms with simple electrical measurements and calculations. Instead of using adjustable mechanical components or complex calibration hardware, the system uses microcontroller-based resistance measurement and gain calculation, significantly simplifying the overall system while maintaining reliability
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
Maintains high accuracy and reliability over time by eliminating the need for pre-use calibration, reducing manufacturing costs, and ensuring consistent performance across repeated uses.
Implementation Method 1
Due to the random nature of the carbon particles that are embedded in the polyethylene, the resistance generated by a force will create a different value for every point on the sheet. This is a result of quantum tunneling.
Implementation Method 2
adjusting a gain setting for the sensor; calculating the gain setting based on a resistance recorded at the peak force
Data Source
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AI summary
A system and method for dynamic calibration of force sensor mediums is provided. The method includes: causing a force to be applied to and removed from a sensor; making force readings as the force is applied and removed; adjusting a gain setting; calculating the gain setting based on a resistance recorded at a peak force; calculating measured curves of the force readings; calculating a subsequent resistance upon a change in response to the force applied to the sensor; recording a duration that the force is applied; and calculating an absolute force based at least in part on the duration and the peak force. The method may also include: preprocessing data; training a machine learning model to align data points from the data to the baseline curve; and using the trained machine learning model to adjust the force readings in real time.