Laser Machining Spectrogram Analysis for Real-Time Quality Detection
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Solution Overview
Problem
Current in-process monitoring systems for laser machining processes struggle to reliably assess the quality of the process and detect machining errors in real-time, often requiring complex parameterization and separate measuring cells for post-process monitoring.
Innovation Solution
A method and system that utilize a trained neural network to analyze spectrograms of process emissions, allowing for the determination of physical properties and classifications of the laser machining process without the need for feature extraction, enabling efficient and automated quality assessment and error detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-process monitoring systems use traditional spectral detection methods with diodes and optical filters, then the system structure is relatively simple, but the measurement precision and reliability of quality assessment are insufficient
Solution Approach 1:
The patent transforms the spectral data from a one-dimensional spectrum into a two-dimensional spectrogram by adding the time dimension. This allows the system to capture both spectral information and temporal evolution of the laser machining process, significantly improving quality assessment precision while maintaining reasonable system complexity
Solution Approach 2:
The patent introduces a neural network as an intermediary between the spectral detection and quality assessment. The neural network automatically extracts features from the spectrogram and maps them to quality parameters, eliminating the need for complex manual feature engineering and threshold setting while achieving high measurement precision
2Measurement precision
If post-process monitoring is used to ensure quality, then the measurement precision is high, but the loss of time and productivity are significant
Solution Approach 1:
The patent performs quality monitoring during the laser machining process itself (in-process monitoring) rather than after completion. By analyzing spectrograms in real-time, the system can detect quality issues as they occur, eliminating the need for separate post-process inspection and thus eliminating inspection time loss while maintaining high precision through spectral analysis
3Measurement precision
If in-process monitoring systems attempt to detect all process radiation, then the measurement precision improves, but the device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent extracts only the relevant spectral information needed for quality assessment by using a spectrometer to capture process radiation in specific wavelength ranges. The neural network then extracts the most important features from this extracted data, eliminating the need to process all radiation information and thus reducing detection difficulty while maintaining precision
4Ease of operation
If traditional monitoring systems use fixed threshold values for quality assessment, then the ease of operation is improved, but the adaptability to different machining conditions deteriorates
Solution Approach 1:
The patent replaces fixed threshold values with a dynamic neural network model that automatically adapts to different machining conditions. The neural network is trained on diverse process data and can dynamically adjust its assessment criteria based on the specific conditions being monitored, providing both ease of operation and high adaptability without requiring manual reconfiguration
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 real-time monitoring and classification of laser machining processes, reducing the need for complex parameterization and enabling closed-loop control of the machining process to prevent errors and ensure high-quality workpieces.
Implementation Method 1
detecting a plurality of spectra of process emissions... a spectrometer for detecting a plurality of spectra of process emissions... The laser machining process emits radiation in a wide range between about 400 and 1800 nm
Data Source
AI summary
A method for analysing a laser machining process includes the steps of: detecting a plurality of spectra of process emissions at successive points in time; generating at least one spectrogram on the basis of the detected spectra; and determining at least one prediction value of a physical quantity and/or determining at least one classification of the laser machining process by means of a trained neural network, wherein the neural network receives the spectrogram as input tensor and outputs the physical quantity and/or the classification of the laser machining process as output tensor.