Laser Material Processing Control Using Bayesian Parameter Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Laser material processing, including drilling and welding, faces challenges in achieving precise control and optimization of process parameters due to the complexity of high-dynamic and interactive physical effects, leading to inefficiencies and increased costs from experimental methods that require numerous tests and are hindered by unknown workpiece characteristic data and simplified models.
Innovation Solution
The implementation of Bayesian optimization using Gaussian processes to iteratively determine optimal process parameters by predicting function values and selecting parameter sets for experiments, potentially replacing real experiments with simulation models when prediction accuracy is sufficient, thereby reducing the number of required tests and improving precision and productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If experimental methods are used for process development, then process parameters can be optimized, but the number of required tests increases significantly and time is consumed
Solution Approach 1:
The patent creates a digital twin (virtual model) of the laser material processing system that replicates the physical system's behavior. This virtual copy allows extensive process development and parameter optimization to be performed in silico, replacing numerous physical experiments. The digital twin includes virtual sensors that provide measurement data without requiring actual physical measurements, thereby reducing the number of required tests while maintaining optimization accuracy.
Solution Approach 2:
The patent performs preliminary virtual experiments and process development in the digital twin environment before conducting actual physical experiments. By pre-optimizing process parameters and exploring different scenarios in the virtual model, the number of required physical tests is significantly reduced. The digital twin allows for preliminary assessment of process parameters and their effects without consuming physical materials or machine time.
2Loss of information
If simplified models are used for prediction, then certain drill hole shape prediction is possible, but reliable predictions of quality characteristics such as weld spatters are not achievable
Solution Approach 1:
The patent implements a comprehensive digital twin that replicates not only the physical geometry and materials but also the complex physical processes and interactions. This virtual copy enables accurate prediction of quality characteristics like weld spatters by modeling the underlying physics in a controlled computational environment, overcoming the limitations of simplified analytical models while avoiding the costs and time requirements of extensive physical experimentation.
3Manufacturing precision
If numerous physical experiments are conducted for process optimization, then accurate process parameters can be found, but manufacturing costs increase due to production downtimes for cleaning and material consumption
Solution Approach 1:
The patent uses a digital twin to perform process optimization virtually, eliminating the need for numerous physical trial runs. This approach avoids material consumption, production downtimes for cleaning laser optics from weld spatters, and associated labor costs. The virtual experiments provide equally accurate or more accurate process parameter optimization without the recurring costs of physical experimentation.
Solution Approach 2:
The patent performs preliminary process optimization and parameter tuning in the digital twin environment before actual production. This pre-optimization prevents costly trial-and-error experimentation during production runs, avoiding material waste, cleaning downtime, and lost productivity. The virtual pre-testing ensures that only optimized parameters are transferred to the physical system.
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 allows for efficient and targeted optimization of laser material processing parameters, reducing the need for extensive experimentation and enabling the achievement of high-quality results with fewer tests, while also accounting for uncertainty and noise in experimental data.
Implementation Method 1
the absorbed laser energy results in a pulse-like, very rapid warming of the workpiece material
Implementation Method 2
a workpiece is acted upon using the, for example, pulsed and focused laser beam
Implementation Method 3
the process-related explosively generated vapor pressure and associated therewith also large pressure gradients
Implementation Method 4
the absorbed laser energy results in a pulse-like, very rapid warming of the workpiece material, which results in melt formation and to some extent also vaporization
Implementation Method 5
The workpieces to be connected are acted upon in this method by a focused laser beam. As a result of the very high intensity, the absorbed laser energy results in a very rapid local heating of the workpiece materials, which results in a joint melt bath formation
Data Source
AI summary
A computer-implemented method for operating a laser material processing machine. An estimated result is ascertained as a function of predefined process parameters, which characterize how good an actual result of the laser material processing will be, and the process parameters are varied by means of Bayesian optimization with the aid of a data-based model, until an actual result of the laser material processing is sufficient enough.