Laser Machining Head Collision Avoidance Using Optical Sensing
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Solution Overview
Problem
Existing laser machining technologies face challenges in collision avoidance, such as cut parts tipping over or being flung, leading to unsatisfactory results and increased downtime, as current methods like leaving micro-bridges, intelligent cutting contours, and fragmentation of inner contours are inefficient and unreliable.
Innovation Solution
A method using optical sensors, such as CMOS cameras, to monitor the machining space, capture images, detect changes, recognize upright objects, and control the laser machining head's movement to avoid collisions by combining planned cutting paths with real-time sensor data, employing deep neural networks for rapid image processing and collision prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optical sensors and deep neural networks are used for real-time collision detection, then collision avoidance reliability is improved, but device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical collision detection methods with optical sensing and deep neural network-based image processing. Cameras capture images of the workpiece and cutting area, and deep neural networks analyze these images in real-time to detect upright objects that may cause collisions, substituting mechanical sensors with optical-field detection methods.
Solution Approach 2:
The patent uses optical copying through camera imaging to create visual representations of the physical workpiece and cutting environment. These image copies are then processed by deep neural networks to detect potential collision risks, allowing the system to analyze the workspace without physical contact and enable reliable collision avoidance.
2Measurement precision
If real-time image monitoring and deep neural network processing are implemented, then collision detection precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by continuously capturing images of the workpiece and cutting area before collisions occur. The deep neural networks process these pre-captured images in real-time to detect upright objects and predict potential collision risks, allowing the system to take preventive action before actual collisions happen.
Solution Approach 2:
The patent implements continuous image monitoring and processing throughout the laser cutting operation. Cameras continuously capture the workspace, and deep neural networks continuously analyze these images to maintain constant awareness of potential collision risks, ensuring uninterrupted real-time detection precision without significant time loss.
3Device complexity
If traditional collision prevention methods like micro-bridges or fragmentation are used, then device complexity is reduced, but productivity decreases
Solution Approach 1:
The patent replaces mechanical collision prevention methods (such as leaving micro-bridges or fragmenting inner contours) with optical sensing and AI-based detection. This substitution eliminates the need for complex mechanical workarounds while maintaining simple device architecture, thereby preserving productivity without increasing mechanical complexity.
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 effectively prevents collisions, reduces downtime, and provides real-time visualization of the cutting area, enabling efficient operation and reliable collision avoidance in laser machining tools.
Implementation Method 1
Monitoring a workpiece in the machining space with at least one optical sensor; Capturing images of the workpiece
Data Source
AI summary
The invention relates to a method for collision avoidance of a laser machining head (102) in a machining space (106) of a laser machining tool (100), having the steps of: —Monitoring a workpiece (112) in the machining space (106) with at least one optical sensor; —Capturing images of the workpiece (112); —Detecting a change in an image of the workpiece (112); —Recognising whether the change comprises an object standing upright relative to the workpiece (112); —Checking for a collision between the upright object and the laser machining head (102) based on a predetermined cutting plan and/or the current position (1016) of the laser machining head; —Controlling the drives for moving the laser machining head (102) for collision avoidance in case of recognised risk of collision.