Galvanometer Laser Position Compensation Using Temperature Learning
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Solution Overview
Problem
Existing laser machining systems face difficulties in accurately compensating for position errors caused by thermal deformation and temperature changes in galvanometer mirrors and other optical parts, making it challenging to maintain precise machining accuracy.
Innovation Solution
A machine learning device is integrated into the laser machining system to acquire temperature data from galvanometer mirrors and motors, using supervised learning to construct a mathematical model that calculates compensated machining target positions, thereby adjusting the laser beam's path to account for thermal deformations and position errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional temperature compensation methods are used, then position error compensation is attempted, but accurate compensation is difficult due to multiple optical parts and constituting members affecting position error
Solution Approach 1:
The patent transforms the compensation approach by changing from manual/calibration-based parameter adjustment to automated machine learning model-based parameter calculation. The system collects temperature parameters from multiple sensors and uses a pre-trained ML model to automatically calculate compensation values, resolving the contradiction between achieving high precision and managing system complexity.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between temperature detection and position compensation. This intermediary component processes multiple temperature inputs and generates compensation values without requiring complex manual calculations or calibrations, thereby maintaining high precision while simplifying the overall system architecture.
2Measurement precision
If multiple temperature sensors are deployed to monitor galvanometer mirrors and motors, then temperature data is captured, but determining compensation values remains difficult due to multiple affecting factors
Solution Approach 1:
The patent replaces the complex mechanical/calibration-based compensation determination process with a software-based machine learning model. The model automatically processes temperature measurements from multiple sensors and computes compensation values, eliminating the difficulty of manual determination while maintaining accurate temperature measurement capabilities.
Solution Approach 2:
The machine learning model enables the system to self-determine compensation values autonomously based on temperature inputs. The pre-trained model automatically processes sensor data and generates appropriate compensation without requiring external calibration or manual intervention, resolving the difficulty in determining compensation values.
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 effectively compensates for position errors due to temperature changes, enhancing the accuracy of laser machining by using a mathematical model learned from temperature data to adjust the machining target positions in real-time, thereby improving the overall precision of the machining process.
Implementation Method 1
a plurality of galvanometer mirrors for reflection of a laser beam
Implementation Method 2
a plurality of galvanometer motors for driving corresponding ones of the galvanometer mirrors to rotate
Implementation Method 3
scanning the laser beam over a workpiece
Implementation Method 4
performs machining by scanning a laser beam over a workpiece
Data Source
AI summary
A machine learning device performs machine learning on a laser machine including a plurality of galvanometer mirrors for reflection of a laser beam and a plurality of galvanometer motors for driving the galvanometer mirrors to rotate, and scanning the laser beam over a workpiece. The machine learning device includes: input data acquisition unit that acquires at least two detected temperatures from the galvanometer mirrors and the galvanometer motors as input data; label acquisition unit that acquires a coefficient as a label for calculating a machining target position from an actual position of machining with the laser beam on the workpiece; and learning unit that performs supervised learning using a set of the label and the input data as training data to construct a mathematical model for calculating the machining target position from the actual machining position on the workpiece based on the at least two detected temperatures.