Laser Machining Quality Estimation From Camera and Photodiode Signals
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Solution Overview
Problem
Current laser machining systems lack reliable real-time quality estimation during the cutting process, particularly due to limitations in existing sensor technologies and classical feature extraction methods, which are not robust against material properties, ambient light, and process variations, leading to inefficiencies and quality control challenges.
Innovation Solution
A computer-implemented method using a deep neural network (DNN) for process monitoring that captures and processes signal sequences from cameras and photodiodes, enabling real-time quality estimation by classifying machining quality without prior feature extraction, utilizing alternating signal sequences with and without illumination to improve accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to determine cutting quality, then quality assessment can be performed, but real-time monitoring during the cutting process is not achieved and production efficiency is reduced
Solution Approach 1:
The patent replaces manual inspection (mechanical/human system) with an automated optical sensing and machine learning system. Optical sensors capture process lighting and images during cutting, and a trained neural network automatically assesses cutting quality in real-time, eliminating the need for manual post-cutting inspection and enabling continuous production monitoring.
Solution Approach 2:
The system implements real-time feedback by continuously monitoring cutting quality parameters during the machining process and providing immediate quality assessment results. This allows the system to detect quality deviations as they occur and enable timely corrective actions, rather than waiting for manual inspection after cutting is complete.
2Reliability
If photodiodes are used for process monitoring, then plasma cut detection is possible, but cutting quality recognition and comprehensive quality assessment are not achieved
Solution Approach 1:
The patent combines multiple sensing modalities into a unified system: photodiodes for detecting plasma cuts and process stability, optical sensors for capturing process lighting, and imaging systems for visual inspection of the cutting zone. These multiple sensors feed into a integrated machine learning model that comprehensively assesses cutting quality, overcoming the limitations of individual sensor types.
Solution Approach 2:
The system monitors multiple process parameters simultaneously (process lighting intensity, image features, photodiode signals) and uses a trained neural network to transform these raw sensor readings into meaningful quality assessments. The machine learning model learns the complex relationships between various process parameters and cutting quality outcomes, enabling precise quality recognition that simple threshold-based photodiode monitoring cannot achieve.
3Ease of manufacture
If classical feature extraction methods are used, then process monitoring can be implemented, but the system is not robust against material properties, ambient light, and process variations
Solution Approach 1:
The patent replaces classical feature extraction methods (manual engineering of features) with a machine learning-based approach where the system automatically learns robust features from raw sensor data. The trained neural network adapts to different material properties, lighting conditions, and process variations by learning from training data, making the system inherently more robust without requiring manual adjustment of feature extraction parameters.
Solution Approach 2:
The machine learning model transforms the approach from fixed feature extraction to adaptive parameter learning. Instead of using predetermined features that may not generalize well, the system learns optimal feature representations from training data covering various material types, lighting conditions, and process variations, enabling reliable quality assessment across diverse operating conditions.
4Measurement precision
If comprehensive process sensors are installed, then cutting quality can be monitored, but device complexity and system cost increase
Solution Approach 1:
The patent designs the sensor system to serve multiple functions: photodiodes detect both plasma cuts and general process stability, optical sensors capture both process lighting for quality assessment and visual information for defect detection, and the machine learning model integrates multiple sensor inputs into a unified quality assessment. This multi-functional approach reduces the need for dedicated sensors for each measurement task, thereby reducing overall system complexity.
Solution Approach 2:
The system merges multiple sensing functions into an integrated platform where a single optical sensing system performs both quality assessment and defect detection, and where machine learning algorithms fuse data from multiple sensor types (photodiodes, optical sensors, cameras) into a comprehensive quality evaluation. This integration reduces the number of separate sensor systems needed and simplifies the overall device architecture.
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
The method provides reliable, real-time quality estimation and automatic optimization of the cutting process, reducing material waste and improving cutting quality by identifying issues such as burrs and slag formation, and enabling immediate corrective actions.
Implementation Method 1
providing at least one captured first signal sequence with a first feature from the machining zone; providing at least one captured second signal sequence with a second feature from the machining zone
Implementation Method 2
By means of optical sensors (photodiodes), the so-called thermal lighting or process lighting can be captured during machining
Data Source
AI summary
A method for process monitoring of a laser machining process for estimating a machining quality is dicloses. The method may include steps, which are carried out in real time during the machining process of providing at least one captured first signal sequence with a first feature form a machining zone, providing at least one captured second signal sequence with a second feature from the machining zone, and accessing a trained neural network with at least the recorded first and second signal sequences in order to calculate a result for estimating the machining quality.