Thermal Displacement Compensation Using Machine Learning
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Solution Overview
Problem
Existing methods for compensating measurement errors due to thermal displacement in workpieces are inaccurate and cumbersome, particularly when using contact-type temperature sensors that obstruct shape inspection and require trial-and-error for sensor placement.
Innovation Solution
A machine learning device that observes temperature and shape data of workpieces, learning their association through state variables and judgment data to accurately compensate for thermal displacement errors, utilizing non-contact temperature measurement and machine learning algorithms like multilayer structures and neural networks, potentially implemented in cloud, fog, or edge computing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contact-type temperature sensors are used to measure workpiece temperature for thermal displacement compensation, then temperature measurement capability is improved, but shape inspection is obstructed and sensor placement requires trial-and-error
Solution Approach 1:
The patent replaces contact-type temperature sensors with non-contact infrared temperature measurement. The infrared measurement unit captures thermal radiation from the workpiece surface to obtain temperature distribution without physical contact, thereby eliminating obstruction to shape inspection while maintaining temperature measurement capability
Solution Approach 2:
The patent uses infrared radiation detection to create a thermal image copy of the workpiece temperature distribution. This optical copy allows temperature measurement without physical sensor contact, avoiding interference with shape inspection operations
2Loss of information
If contact-type temperature sensors are used for thermal displacement compensation, then temperature data acquisition is improved, but device complexity and setup difficulty increase due to multiple sensors and placement optimization
Solution Approach 1:
The patent replaces multiple contact-type temperature sensors with a single non-contact infrared measurement unit that captures temperature distribution across the entire workpiece surface simultaneously, dramatically reducing system complexity while maintaining complete temperature data acquisition
Solution Approach 2:
The patent transitions from point-based temperature measurement (contact sensors at discrete locations) to area-based temperature distribution measurement (infrared imaging), obtaining complete thermal information in a single measurement without requiring multiple sensors or complex placement optimization
3Measurement precision
If thermal equilibrium waiting is implemented before inspection, then measurement accuracy is improved, but inspection time increases significantly
Solution Approach 1:
The patent performs temperature distribution measurement and thermal displacement compensation calculations before the workpiece reaches thermal equilibrium. By measuring temperature early and compensating for thermal displacement computationally, the system obtains accurate dimensional measurements without waiting for thermal equilibrium, thereby maintaining measurement precision while improving inspection throughput
Solution Approach 2:
The patent uses measured temperature distribution data to calculate thermal displacement compensation amounts, which are then applied to correct dimensional measurements. This feedback loop enables accurate measurements during non-equilibrium states by compensating for thermal effects computationally rather than waiting for thermal equilibrium
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
Enables accurate and efficient compensation of measurement errors without waiting for thermal equilibrium, reducing inspection time and eliminating the need for trial-and-error in sensor placement, while maintaining high accuracy and flexibility in temperature measurement.
Implementation Method 1
a state observing unit for observing image data showing the temperature distribution of a workpiece
Data Source
AI summary
A thermal displacement compensation apparatus for compensating a dimensional measurement error due to a thermal displacement of a workpiece, including a machine learning device for learning shape measurement data at the time of inspection of the workpiece, wherein the machine learning device observes image data showing the temperature distribution of the workpiece and shape data after machining as state variables representing the current state of the environment, acquires judgment data indicating the shape measurement data at the time of inspection, and learns the image data showing the temperature distribution of the workpiece and shape data after machining and the shape measurement data at the time of inspection in association with each other using the observed state variables and the acquired judgment data.