Gas Nozzle Image Recognition for Timely Replacement in Metal Cutting
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Solution Overview
Problem
Current cutting systems require labor-intensive and often late or premature replacement of gas nozzles, leading to reduced cutting quality and inefficient operations due to manual inspection by operators.
Innovation Solution
A machining apparatus equipped with an electronic camera and pattern recognition module, utilizing a trained neural network for automatic detection of nozzle state and type, along with a lighting system for improved image quality, enables autonomous and reliable nozzle replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual inspection by operators is used to check gas nozzle wear, then labor cost and time consumption increase, but the system lacks automation
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated optical inspection system. An electronic camera captures images of the gas nozzle, and a pattern recognition module automatically analyzes the images to detect nozzle wear and determine nozzle type, eliminating the need for manual operator inspection while maintaining high accuracy.
Solution Approach 2:
The patent creates a digital copy (image) of the gas nozzle using an electronic camera. This digital representation is then analyzed by the pattern recognition module, allowing the system to inspect the nozzle without physical contact or manual handling, thereby automating the inspection process.
2Manufacturing precision
If operators perform late replacement of gas nozzles based on manual inspection, then cutting quality deteriorates, but replacement timing is difficult to determine
Solution Approach 1:
The patent implements a feedback mechanism where the pattern recognition module continuously monitors the gas nozzle condition and provides real-time information about nozzle wear and type. This feedback enables the system to determine the optimal replacement timing automatically, ensuring cutting quality is maintained while avoiding premature or delayed replacement.
Solution Approach 2:
The patent performs preliminary detection of nozzle wear and type before actual wear affects cutting quality. By continuously monitoring the nozzle condition and identifying wear patterns early, the system can schedule replacement at the optimal time, preventing quality deterioration while minimizing replacement frequency.
3Reliability
If prophylactic replacement of gas nozzles is performed early, then cutting quality is maintained, but resource efficiency decreases
Solution Approach 1:
The pattern recognition module provides continuous feedback on the actual nozzle condition, allowing the system to replace nozzles based on their actual wear state rather than following a fixed preventive schedule. This condition-based replacement strategy maintains cutting quality while optimizing resource utilization by replacing nozzles only when necessary.
4Manufacturing precision
If wrong nozzle type is used due to manual change, then cutting quality deteriorates, but manual inspection cannot prevent errors
Solution Approach 1:
The patent replaces manual nozzle identification and selection with an automated optical recognition system. The electronic camera captures the nozzle, and the pattern recognition module automatically identifies the nozzle type, eliminating human error in nozzle selection while streamlining the change operation.
Solution Approach 2:
The system performs self-identification of the nozzle type through automatic image analysis. The pattern recognition module independently determines the correct nozzle type without requiring operator knowledge or manual verification, enabling error-free nozzle changes while simplifying the operation.
Data Source
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AI summary
The present invention relates to a metal machining apparatus (100) having a gas nozzle (101) for generating a gas jet and having a nozzle exit opening (131) on one end on the outside; an electronic camera (103) for acquiring a digital image (105) of the end of the gas nozzle (101) with the nozzle exit opening (131); and a pattern recognition module (115) for mapping the digital image (105) to at least one nozzle pattern from the group of nozzle state (109-1, 109-2, 109-3) and/or nozzle type.