Laser Machining Condition Control for Plasma-Reduced Cutting
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Solution Overview
Problem
Laser beam cutting machining often results in the generation of plasma, leading to rough surfaces and adhesion of dross due to the close proximity of the nozzle and workpiece, which complicates setting optimal machining conditions.
Innovation Solution
A machining condition adjustment device that utilizes a high-flow speed assist gas to reduce plasma generation by positioning the workpiece at a specific location where the assist gas flow speed is maximized, and employs machine learning to adjust laser beam machining conditions, such as the gap between the nozzle and workpiece, to optimize cutting quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the nozzle and workpiece are positioned close to each other for high-speed cutting, then cutting speed is improved, but plasma generation increases causing rough surfaces and dross adhesion
Solution Approach 1:
The patent changes the parameter of assist gas flow velocity by positioning the workpiece at the Mach disk location where flow velocity is maximized. This velocity optimization reduces plasma generation while maintaining high-speed cutting capability, resolving the contradiction between productivity and surface quality
Solution Approach 2:
The patent introduces a new dimensional consideration by focusing on the axial flow velocity distribution of assist gas rather than just the radial distance between nozzle and workpiece. By identifying the Mach disk position along the gas flow axis, the patent finds an optimal positioning that simultaneously achieves high cutting speed and low plasma generation
2Stress or pressure
If the nozzle and workpiece are positioned close to each other, then pressure on the workpiece is maximized, but plasma generation increases causing rough surfaces and dross adhesion
Solution Approach 1:
The patent shifts from optimizing pressure alone to optimizing flow velocity by positioning at the Mach disk. This parameter change reveals that maximum flow velocity occurs at a different position than maximum pressure, and this velocity-based optimization reduces plasma generation while maintaining effective cutting pressure
3Manufacturing precision
If machine learning is used to dynamically adjust machining conditions, then cutting quality is improved, but device complexity increases
Solution Approach 1:
The patent implements feedback control by using machine learning to process detection results in real-time and dynamically adjust machining conditions. This closed-loop system continuously optimizes cutting quality by adapting to varying workpiece conditions, material properties, and plasma generation levels
Solution Approach 2:
The machine learning model enables the system to self-optimize by automatically learning the relationship between machining parameters and plasma generation from historical data. The system performs self-adjustment without requiring external intervention, reducing the need for manual tuning and expert knowledge
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 solution effectively reduces plasma generation and improves cutting quality by dynamically adjusting machining conditions based on real-time data and machine learning algorithms, accommodating variations in material and plate thickness.
Implementation Method 1
a shock wave is generated from a mouth of the nozzle 5 so as to form a wall, which re-converges the assist gas 6 so as to maximize a flow speed of the assist gas 6 at position (3)
Data Source
AI summary
A machining condition adjustment device adjusts laser beam machining conditions for a laser beam machining device to carry out laser beam machining of a workpiece, produces each of state variables including machining condition data, workpiece data, and plasma generation amount data and determination data including plasma generation amount determination data, and learns adjustment action for the laser beam machining conditions with respect to an amount of plasma generated in the laser beam machining of the workpiece under prescribed laser beam machining conditions, with use of the produced state variables and the produced determination data.