Laser Cutting Assist Gas Shift Control Using Dross Learning
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Solution Overview
Problem
Existing laser machining methods face challenges in achieving optimal cutting quality by determining the shift amount of assist gas relative to the laser beam, particularly when different cutting quality requirements are needed for different sides of a workpiece.
Innovation Solution
A machine learning apparatus that observes machining condition data and measurement data of dross dimension at the cutting spot to learn the shift amount of assist gas in relation to cutting quality, allowing for precise adjustment of the gas beam's position relative to the laser beam.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the assist gas is shifted from the laser beam to satisfy cutting quality requirements, then cutting quality is improved, but the shift amount is difficult to determine precisely
Solution Approach 1:
The patent implements a feedback mechanism where the learning apparatus observes machining condition data and measurement data of dross dimension, then learns and determines the optimal shift amount. The system continuously refines the shift amount based on observed cutting quality outcomes, creating a closed-loop control system that improves precision through iterative learning from actual machining results.
Solution Approach 2:
The learning apparatus performs self-learning by automatically observing machining conditions and dross dimensions, then autonomously determining the optimal shift amount without requiring external manual intervention. The system serves itself by using its own operational data to improve its control parameters, enabling automatic optimization of the assist gas shift amount.
2Productivity
If manual determination of shift amount is used, then device complexity is reduced, but productivity and precision are compromised
Solution Approach 1:
The patent replaces manual mechanical determination methods with an automated machine learning system. Instead of relying on operators to manually adjust and determine shift amounts, the system uses computational learning algorithms that process machining data and automatically determine optimal parameters, substituting human mechanical operations with intelligent automated systems.
Solution Approach 2:
The learning apparatus serves multiple functions: it observes machining conditions, measures dross dimensions, learns from the data, and determines shift amounts. This multi-functional system consolidates what would otherwise require separate manual operations into a single integrated apparatus that handles the entire optimization process automatically.
Data Source
AI summary
A machine learning apparatus able to obtaining an optimal shift amount of an assist gas. The machine learning apparatus comprises a state-observation section configured to observe machining condition data included in a machining program given to the laser machine, and measurement data of a dimension of dross generated at a cutting spot of the workpiece when the machining program is executed, as a state variable representing a current state of an environment in which the workpiece is cut; and a learning section configured to learn the shift amount in association with cutting quality of the workpiece, using the state variable.