Reinforcement Learning Control for Laser Scan Wait Time
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Solution Overview
Problem
Current CFRP cutting technologies using ultrashort pulsed lasers require multiple scans and significant wait times to avoid thermal effects, leading to low production efficiency and prolonged machining times, despite efforts to optimize machining conditions.
Innovation Solution
A machine learning device and method that utilize reinforcement learning to optimize laser scan wait times based on imaging data and machining accuracy, selecting optimal wait times to minimize machining time while maintaining high accuracy through an actor-critic method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple laser scans are performed to complete CFRP cutting, then machining quality is improved, but machining time increases due to required wait times between scans
Solution Approach 1:
The patent implements a feedback mechanism where the system monitors thermal effects during laser scanning and dynamically adjusts wait times between scans. Sensors detect temperature changes and feed this information back to the control system, which optimizes the interval between multiple scans to prevent thermal damage while minimizing total machining time.
Solution Approach 2:
The wait time between laser scans is made dynamic rather than static. The system continuously adapts the wait time based on real-time thermal condition monitoring, material properties, and scan parameters. This dynamic adjustment allows the system to maintain machining quality across multiple scans while reducing unnecessary waiting periods.
2Manufacturing precision
If wait time is extended between laser scans, then thermal effects are reduced and machining accuracy is maintained, but production efficiency decreases
Solution Approach 1:
The system changes multiple parameters simultaneously to optimize the balance between accuracy and efficiency. These include adjusting laser power, scan speed, wait time intervals, and pulse duration based on material type and desired outcome. By coordinating these parameter changes, the system achieves high machining accuracy without excessive wait times that would reduce productivity.
Solution Approach 2:
The system performs preliminary actions by pre-calculating optimal wait times based on material properties, laser parameters, and desired machining quality. Before actual machining begins, the system determines the optimal scan sequence and wait time schedule, allowing for efficient execution that maintains accuracy without unnecessary delays.
3Object-affected harmful factors
If ultrashort pulsed laser is used for CFRP cutting, then thermal effects are reduced, but multiple scans are required leading to low production efficiency
Solution Approach 1:
The patent employs periodic laser scanning with optimized intervals. Instead of continuous scanning, the system uses periodic pulses with carefully controlled wait times between them. This periodic action allows thermal effects to dissipate between scans while maintaining the benefits of ultrashort pulsed laser, and the optimized period reduces the total number of scans needed for complete cutting.
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 approach effectively reduces machining time by minimizing wait times between laser scans while ensuring high machining accuracy, enhancing production efficiency in CFRP cutting processes.
Implementation Method 1
A known CFRP cutting technology uses an ultrashort pulsed laser (e.g., femtosecond pulsed laser with pulse widths in femto (10−15) seconds) and allows for reduced thermal effects in high quality machining, micromachining, ablation machining
Data Source
AI summary
A machine-learning device performs machine-learning under machining conditions including at least a waiting time of laser emission for controlling machining of a subject to be machined in a laser machining apparatus, and comprises: an action output unit which selects, as an action, a machining condition from a plurality of machining conditions, and outputs the action to the laser machining apparatus; a state acquisition unit which acquires, as state information, image data obtained by imaging a machined state of the subject that has been machined by the action; a reward calculation unit which calculates a reward on the basis of the waiting time of the laser emission and the machining accuracy of the machining state calculated on the basis of at least the acquired state information; and a learning unit which performs machine-learning on the machining conditions on the basis of the acquired state information and the calculated reward.