Laser Machining Parameter Learning for Wide-Range Precision Setup
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Solution Overview
Problem
Current methods for determining laser machining parameters are imprecise and limited to local data ranges, requiring extensive and costly testing, and do not allow for optimal parameter determination across a wide variety of laser machining systems.
Innovation Solution
A method using a machine learning approach with a central unit and learning database to determine optimal laser machining parameters, including polarization, pulse energy, beam diameter, Gaussian order, pulse repetition rate, and wavelength, based on previous machining data samples, enabling accurate and efficient machining across different materials and systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If linear response models are used to model laser-material interaction, then the model is simple to implement, but the reliable response range is limited and cannot cover adequate ranges for full device capacity
Solution Approach 1:
The patent transforms the approach from using simple linear models with limited parameter ranges to using machine learning models that can handle wide parameter ranges. The machine learning model learns complex non-linear relationships between laser parameters and machining results, enabling the system to determine optimal parameters across a broad spectrum of operating conditions without requiring complex physical models for each scenario.
Solution Approach 2:
The patent replaces traditional physics-based linear modeling approaches with data-driven machine learning algorithms. Instead of relying on simplified physical models that require extensive manual calibration and experimentation, the system uses supervised learning algorithms trained on experimental data to directly predict optimal machining parameters, eliminating the need for complex model development and parameter tuning.
2Productivity
If extrapolation of experimental machining results is used to determine laser machining parameters, then the method requires minimal testing, but the results are imprecise and assessment is left to user judgment
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model is trained on experimental machining results and uses this learned knowledge to predict optimal parameters for new machining scenarios. The model continuously improves by learning from the relationship between input parameters and actual machining outcomes, providing objectively assessed predictions rather than relying on user judgment of extrapolated results.
Solution Approach 2:
The patent performs preliminary machine learning training on a comprehensive dataset of experimental machining results before actual machining operations. This pre-learning phase enables the system to quickly determine optimal parameters for new materials or machining conditions without requiring extensive real-time experimentation, thus achieving both speed and precision.
3Device complexity
If local extrapolation around experimental machining results is used, then the method is simple to implement, but it cannot determine parameters over a wide data range
Solution Approach 1:
The patent creates a universal machine learning model that can determine optimal laser machining parameters across a wide variety of materials, machining conditions, and device configurations. The model is trained on diverse experimental data and can generalize to new scenarios, making it applicable to different laser machining systems and materials without requiring system-specific recalibration or separate models for each case.
Data Source
AI summary
Method for determining laser machining parameters for the machining of a material using a laser machining system comprises the following steps: a) providing said central unit with a learning machining function capable of learning on the basis of said plurality of machining data samples, said learning machining function comprising an algorithm capable of defining the following laser machining parameters for said machining result sought and for said machining system: a polarization, an pulse energy Ep, a diameter at the focal point w, a Gaussian order p, a pulse repetition rate PRR of pulses n, a wavelength; b) making said learning machining function to learn so as to said laser machining system can machine said material to be machined according to the machining result sought.