Laser Machining Parameter Learning for Precise Process Prediction
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Solution Overview
Problem
Current methods for determining laser machining parameters are limited by the need for empirical tests and extrapolation, leading to imprecise results and restricted applicability to local data ranges, failing to maximize the capabilities of laser machining devices efficiently and effectively.
Innovation Solution
An automated learning method that uses previous experimental data to determine optimal machining parameters through a learning function, considering material-specific interaction parameters like delta, threshold fluence, incubation coefficient, and complex refractive index, allowing for faithful reproduction of target machining results across extended ranges.
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 range over which the model exhibits a reliable response is limited and does not cover sufficient range to allow the laser machining device to be used to its maximum capabilities
Solution Approach 1:
The patent transitions from linear response models to non-linear models that account for cumulative damage effects through the incubation coefficient. This parameter change allows the model to accurately represent laser-material interaction across a wider range of fluence values and pulse numbers, enabling the system to operate at its maximum capabilities while maintaining model reliability.
2Loss of time
If extrapolation of experimental machining results is used to determine laser machining parameters, then the method requires fewer preliminary tests, but the results are imprecise and the assessment of parameter adequacy is left to the user
Solution Approach 1:
The patent implements a feedback mechanism where the model continuously compares predicted machining results with actual experimental results. The incubation coefficient and other parameters are adjusted based on this feedback, allowing the system to self-correct and improve precision over time rather than relying on user assessment of extrapolated results.
Solution Approach 2:
The patent performs preliminary modeling and simulation before actual machining operations. By using the non-linear model to predict optimal parameters beforehand and then validating these predictions with minimal experimental tests, the system reduces the need for extensive preliminary testing while maintaining high precision in parameter determination.
3Measurement precision
If extensive empirical tests are conducted to determine optimal laser machining parameters, then the precision of parameter determination is improved, but the time and cost required for parameter optimization increases significantly
Solution Approach 1:
The patent replaces the mechanical trial-and-error approach with a computational model-based system. The non-linear model incorporating incubation effects allows for virtual experimentation and parameter optimization through simulation, significantly reducing the need for physical empirical tests while maintaining or improving parameter determination precision.
Solution Approach 2:
The system performs preliminary parameter optimization through computational modeling before actual machining. By pre-calculating optimal parameters using the non-linear model and then verifying with minimal tests, the patent achieves high precision parameter determination without requiring extensive empirical testing campaigns.
4Device complexity
If local extrapolation around experimental results is used, then the method is simple to implement, but it does not allow determination of laser machining parameters over an extended data range
Solution Approach 1:
The patent introduces the incubation coefficient as a key parameter that enables the model to handle extended data ranges. This parameter accounts for cumulative damage effects that occur over multiple pulses, allowing the model to accurately predict machining results across a wide range of fluence values and pulse numbers rather than being limited to local extrapolation around single experimental points.
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 enables precise and efficient determination of laser machining parameters without extensive empirical testing, ensuring high-quality machining results for various materials and machining systems by leveraging machine learning algorithms to predict optimal parameters for different machining conditions.
Implementation Method 1
Laser beams are sometimes used for machining parts. It is in fact possible to melt, evaporate or sublimate part of a material exposed to a laser beam.
Implementation Method 2
Laser beams are sometimes used for machining parts. It is in fact possible to melt, evaporate or sublimate part of a material exposed to a laser beam.
Implementation Method 3
The method of the invention comprises an automated learning step which allows, on the basis of previous experimental results, the determination of machining parameters for the machining of a result to be achieved.
Data Source
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AI summary
A method for determining laser machining parameters for machining a material using a laser machining system comprises the following steps, inter alia: (a) providing the central unit with a machining learning function capable of learning on the basis of a plurality of machining data samples, the machining learning function comprising an algorithm capable of defining the following laser machining parameters for the machining result sought and for the machining system used: • a polarisation, • a pulse energy Ep, • a diameter at the focal point w, • a Gaussian order p, • a pulse repetition rate PRR of n pulses, • a wavelength; (b) learning on the basis of the machining learning function so as to enable the laser machining system to machine the material to be machined according to the machining result sought.