Force Sensor Calibration Using Dynamic Gain and Baseline Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Polyethylene-based force-sensitive resistors (FSRs) suffer from material fatigue and quantum tunneling, 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 gain settings and aligns force readings with a baseline resistance vs. force curve, compensating for material fatigue and positional variations, eliminating the need for pre-use calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If polyethylene is used to make force-sensitive resistors, then the sensor can be manufactured with simple materials and processes, but the sensor accuracy deteriorates over time due to material fatigue and compression set
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the gain setting of the amplifier based on the sensor's response characteristics. The system measures the sensor's output at different gain settings and selects the optimal gain value to compensate for material fatigue and compression set, thereby maintaining measurement accuracy over time without changing the physical sensor structure
Solution Approach 2:
The patent implements feedback by continuously monitoring the sensor's response and using this information to adjust the gain setting. The system measures the sensor output, determines the appropriate gain value based on predefined criteria, and applies this gain adjustment to maintain accurate force measurements, creating a closed-loop system that compensates for material degradation
2Measurement precision
If individual calibration and selection of each sensor is performed, then measurement precision is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent applies self-service by enabling each sensor to calibrate itself through the dynamic gain adjustment process. The sensor's own response characteristics are used to determine the appropriate gain setting, eliminating the need for external calibration equipment or manual intervention. The system automatically adapts to each sensor's unique properties through the gain adjustment algorithm
Solution Approach 2:
The patent uses parameter changes to simplify calibration by adjusting the electrical gain parameter rather than requiring mechanical or physical calibration procedures. This approach transforms the calibration process into a simple parameter adjustment that can be performed automatically, reducing complexity while maintaining precision
3Duration of action of moving object
If repeated compression and decompression of polyethylene is performed, then the sensor can be used multiple times, but the polyethylene does not return to original thickness resulting in invalidation of calibration
Solution Approach 1:
The patent applies dynamics by making the gain setting dynamic rather than static. Instead of using a fixed calibration value, the system continuously or periodically adjusts the gain based on the sensor's current response characteristics, allowing the system to adapt to changes in polyethylene thickness and material properties over time and with repeated use
Solution Approach 2:
The patent uses feedback to compensate for material degradation by monitoring the sensor's output and adjusting the gain setting accordingly. This feedback mechanism allows the system to maintain accurate measurements even as the polyethylene undergoes compression set and permanent deformation from repeated use
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
Ensures consistent and accurate force measurements over time by continuously adapting to material changes, reducing manufacturing costs and complexity, and maintaining precision across various applications.
Implementation Method 1
As a force is applied then removed, the polyethylene will compress and decompress. As the polyethylene compresses, the carbon particles are forced to touch each other and create a resistance based on how many carbon particles are touching.
Implementation Method 2
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.
Data Source
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.