Laser Cutting Head Control for Heat-Aware Collision-Free Sequencing
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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 and require costly computations, making it difficult for traditional controllers to find an efficient path.
Innovation Solution
A machine learning device with a decision agent uses a neural network to process multi-layer image inputs from sensors, including heat maps and cutting plans, to determine optimal control instructions for the cutting head, incorporating reward functions for collision avoidance, heat optimization, and cutting time optimization, allowing for self-adaptation and continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional next closest available neighbor cutting sequence is used, then the control algorithm is simple, but the workpiece overheating and collision risks increase
Solution Approach 1:
The cutting sequence is dynamically adjusted based on real-time heat distribution feedback from the workpiece. The system continuously monitors temperature fields and modifies the cutting path to avoid overheated zones, transforming a static cutting plan into a dynamic adaptive process that responds to changing thermal conditions
Solution Approach 2:
The system implements a feedback mechanism where heat distribution data from sensors is continuously fed back to the control algorithm. This feedback loop enables the system to learn from thermal patterns and adjust cutting sequences to prevent overheating, creating a closed-loop control system that improves cutting quality and safety
2Manufacturing precision
If optimal cutting sequence considering heat distribution is calculated, then cutting quality improves, but computation time becomes excessive for real-time control
Solution Approach 1:
The system performs preliminary calculations of heat distribution patterns and cutting sequences in advance, before actual cutting begins. By pre-computing optimal paths based on anticipated thermal behavior, the system avoids real-time computation delays while maintaining cutting quality through pre-planned heat-aware sequencing
Solution Approach 2:
The control algorithm simplifies the thermal field model by changing parameters from continuous finite element analysis to discrete temperature zone representations. This parameter transformation reduces computational complexity from NP-hard to manageable levels, enabling real-time implementation while preserving essential heat distribution characteristics for quality cutting
3Productivity
If cutting head moves quickly to reduce cycle time, then productivity increases, but collision risk with tilted parts increases
Solution Approach 1:
The cutting sequence dynamically adapts to the real-time positions and orientations of previously cut parts. The system continuously updates its model of part tilting and adjusts cutting paths to maintain safe distances, enabling high-speed operation without compromising collision avoidance through real-time dynamic repositioning
Data Source
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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).