Laser Cutting Head Path Control for Collision and Heat Management
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Solution Overview
Problem
Laser cutting machines face challenges in optimizing the cutting sequence to avoid collisions with tilted parts and overheating issues, which are complex due to dynamic changes in the cutting process, making it difficult for traditional controllers to find an efficient path that minimizes processing time and heat distribution problems.
Innovation Solution
A machine learning device with a decision agent uses sensor signals, particularly optical data from infrared cameras, to dynamically calculate control instructions for the cutting head, incorporating a reward function to optimize cutting path, collision avoidance, and heat distribution, utilizing a neural network that learns from experience to improve the machining process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional next closest available neighbor cutting sequence is used, then the cutting process is simple to control, but collision risk with tilted parts increases and processing time is not optimized
Solution Approach 1:
The system continuously monitors the cutting process state including part positions and tilting conditions, using this feedback to dynamically adjust the cutting sequence and avoid collisions with tilted parts while optimizing processing time
Solution Approach 2:
The cutting sequence is made dynamic rather than static, allowing real-time adjustments based on observed part positions and tilting states, enabling the system to adapt to changing conditions and avoid collisions
2Manufacturing precision
If complex heat distribution optimization is implemented, then cutting quality improves, but computation time increases making it impossible for typical machine controllers
Solution Approach 1:
Heat distribution patterns and optimal cutting sequences are pre-calculated and stored for different workpiece configurations, allowing the controller to quickly retrieve and apply appropriate sequences without performing complex real-time computations
Solution Approach 2:
The system applies simplified heat management rules that address the most critical overheating scenarios without performing complete finite element simulations, achieving sufficient cutting quality with reduced computational burden
3Productivity
If dynamic cutting sequence optimization is implemented, then processing time is reduced and heat distribution is improved, but the system complexity increases
Solution Approach 1:
A dedicated machine learning device acts as an intermediary between the workpiece and the laser cutting head, performing the complex optimization computations and sequence planning while the main machine controller executes the generated instructions, distributing system complexity appropriately
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 reduces collision risks and heat accumulation, leading to improved cutting quality and efficiency by dynamically adapting the cutting sequence based on real-time observations, enhancing the overall performance of the laser cutting process.
Implementation Method 1
sensor signals, particularly optical data from infrared cameras
Data Source
AI summary
In one aspect the invention relates to a method for calculating control instructions (CI) for controlling a cutting head (H) of a laser machine (L) for cutting a set of contours in a workpiece. The method comprises reading (S71) an encoded cutting plan (P), and continuously determining a state (S73) relating to the processing of the workpiece by the laser machine (L) by means of a set of sensor signals (sens). Further, the method provides a computer-implemented decision agent (DA), which dynamically calculates an action (a) for the machining head (H) to be taken next and based thereon providing control instructions (CI) for executing the processing plan (P) by accessing a trained model with the encoded cutting plan (P) and with the determined state (s).