Laser Processing Parameter Prediction From 3D Ablation Learning
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Solution Overview
Problem
Existing laser processing systems struggle to accurately determine the degree of ablation processing and set laser beam parameters due to non-linear relationships between ablation volume, number of irradiation pulses, and fluence, making it difficult to achieve precise processing results.
Innovation Solution
A machine learning method that performs deep learning using pre- and post-processed part data, material information, and laser beam parameters to establish relationships between input data and output data, enabling the determination of optimal laser processing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional laser processing control methods are used, then the processing can be performed with simple parameter setting, but the manufacturing precision and accuracy of laser processing deteriorate due to non-linear relationships between ablation volume, number of irradiation pulses, and fluence
Solution Approach 1:
The system performs preliminary learning by collecting training data from actual laser processing operations and pre-training neural networks to establish relationships between laser parameters and processing outcomes. This preliminary action enables the system to predict optimal processing conditions before actual processing begins, improving accuracy while managing complexity through advance preparation
Solution Approach 2:
The patent introduces machine learning models and simulation apparatus as intermediary components between the laser processing system and the control system. These intermediaries process the complex non-linear relationships between laser parameters and ablation outcomes, translating raw parameters into optimized processing conditions without requiring direct complex control logic in the laser system itself
2Productivity
If the number of irradiation pulses and fluence are increased to improve ablation volume, then the processing efficiency increases, but the manufacturing precision deteriorates due to non-linear ablation behavior
Solution Approach 1:
The system dynamically adjusts laser processing parameters based on real-time feedback and predictive modeling. Rather than using fixed parameter settings, the machine learning model continuously optimizes pulse number, fluence, and other parameters according to the specific material properties and desired ablation outcomes, enabling precise control even at higher productivity levels
Solution Approach 2:
The patent systematically varies and optimizes multiple laser processing parameters including pulse duration, repetition rate, fluence, and wavelength based on training data. By changing and optimizing these parameters through machine learning, the system achieves precise control over ablation volume and morphology, decoupling the relationship between productivity and precision
3Manufacturing precision
If machine learning is applied to learn only the result of laser processing, then the system complexity remains relatively low, but the manufacturing precision deteriorates because the degree of processing before and after irradiation is not learned
Solution Approach 1:
The patent segments the learning process into distinct components: learning the initial state of the workpiece, learning the laser processing parameters, learning the processing outcome, and learning the transformation relationship between them. This segmentation allows the system to specifically learn the degree of processing changes, improving manufacturing precision while managing system complexity through modular learning architecture
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
Enhances the accuracy of laser processing by learning the degree of processing before and after irradiation, improving the precision of laser beam parameter settings.
Implementation Method 1
a laser processing system that performs ablation processing by irradiating a processing object with a laser beam
Implementation Method 2
the ablation volume (removal volume) ablated from a processing object increases non-linearly against the number of irradiation pulses
Data Source
AI summary
Deep learning is performed by using a material of a processing object, a laser beam parameter showing a property of laser beam which the processing object is irradiated with, and pre-processed part data and post-processed part data that respectively reflect laser processing-involved three-dimensional shapes of a processed part before and after irradiation of the processing object with the laser beam. A first relationship of input data that are the material of the processing object, the pre-processed part data, and the laser beam parameter to output data that is the post-processed part data after irradiation with the laser beam in relation to the input data is accordingly obtained as one learning result.