Laser Processing Parameter Optimization With Bayesian Models
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Solution Overview
Problem
Existing laser material processing methods, such as drilling and welding, face challenges in achieving precise and efficient results due to the complexity of modeling highly dynamic physical effects, unknown workpiece characteristics, and the need for numerous experimental trials to optimize process parameters, leading to increased costs and reduced productivity.
Innovation Solution
The use of Bayesian optimization with Gaussian processes to iteratively determine optimal process parameters by training data-based models on experimental and simulated data, allowing for efficient and targeted optimization of laser material processing without relying on gradient calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional experimental methods are used to optimize laser material processing parameters, then process quality can be improved, but the number of required experiments increases significantly, leading to increased costs and reduced productivity
Solution Approach 1:
The patent creates a digital twin (virtual model) of the laser material processing system that replicates the behavior of the physical system. This digital copy allows for virtual experimentation and optimization without requiring numerous physical trials, thereby maintaining process quality while dramatically reducing the number of actual experiments needed.
Solution Approach 2:
The patent performs preliminary virtual experiments and optimizations in the digital twin environment before conducting physical experiments. This preliminary action identifies optimal parameter ranges and reduces the search space for physical experimentation, minimizing the number of required physical trials while ensuring high process quality.
2Speed
If simplified models are used for prediction, then computational speed is improved, but prediction accuracy of quality properties deteriorates
Solution Approach 1:
The patent employs surrogate models that use transformed and normalized process parameters to improve prediction accuracy. By changing the parameter representation and using statistical transformations, the model achieves better predictive performance for quality properties while maintaining computational efficiency through the surrogate modeling approach.
Solution Approach 2:
The patent combines multiple modeling approaches (physics-based models, statistical models, and machine learning models) into a composite surrogate model. This composite approach leverages the strengths of each individual model to achieve both computational speed and prediction accuracy, balancing the trade-off between simulation fidelity and computational cost.
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 significantly reduces the number of required experiments while ensuring high-quality results, compensating for simulation errors, and enabling rapid convergence to optimal process settings, thus improving precision and productivity.
Implementation Method 1
the absorbed laser energy results in pulsed very rapid heating of the workpiece material
Implementation Method 2
the absorbed laser energy results in very rapid local heating of the workpiece materials
Implementation Method 3
Due to the very high intensity, the absorbed laser energy results in pulsed very rapid heating of the workpiece material, which results, on very short time scales and in a spatially very localized manner, in melt formation and also partial vaporization
Implementation Method 4
The molten material is expelled from the drilled hole by the process-related explosively generated vapor pressure and also large pressure gradients linked thereto
Implementation Method 5
The molten material is expelled from the drilled hole by the process-related explosively generated vapor pressure and also large pressure gradients linked thereto
Implementation Method 6
A focused laser beam is applied to the workpieces to be connected. Due to the very high intensity, the absorbed laser energy results in very rapid local heating of the workpiece materials, which results in a shared melt bath formation on short time scales
Implementation Method 7
the absorbed laser energy results in very rapid local heating of the workpiece materials, which results in a shared melt bath formation
Implementation Method 8
The molten material is expelled from the melt bath by the process-related explosively generated vapor pressure and also large pressure gradients linked thereto
Implementation Method 9
The molten material is expelled from the melt bath by the process-related explosively generated vapor pressure and also large pressure gradients linked thereto
Data Source
AI summary
A computer-implemented method for operating a laser material processing machine. Process parameters are varied with the aid of Bayesian optimization until a result of the manufacturing, in particular the laser material processing, is sufficiently good. The Bayesian optimization is carried out with the aid of a data-based process model in a first phase, the data-based process model being trained as a function of estimated results. In a second phase, the data-based process model is trained as a function of the ascertained result resulting upon activation of the laser material processing machine.