Welding Gun Tip Polishing Control Using AI Robot Learning
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Solution Overview
Problem
Conventional tip inspection systems for welding guns in production lines face challenges such as delayed production, increased costs, and reduced reliability due to the need for separate working times for polishing and inspection, limited degree of freedom of the welding gun, and disparate sensor data management for multiple robots, leading to inefficient tip polishing and potential welding defects.
Innovation Solution
A system and method utilizing an artificial intelligence neural network to manage and determine the polishing state of welding gun tips in real time by collecting and learning tip polishing data from multiple robots, adjusting polishing pressure and time, and alerting for replacement, thereby reducing production line stops and consumable costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional tip inspection apparatus using a non-contact sensor is used to inspect the polishing state of the tip, then the polishing state can be determined, but a separate working time is required to move the polishing surface to the inspection position and return it, causing production delays
Solution Approach 1:
The patent combines the inspection function with the welding gun itself by embedding a sensor in the welding gun tip that directly measures the tip surface during welding operations. This eliminates the need for separate inspection positioning movements while maintaining measurement capability, thereby resolving the contradiction between inspection accuracy and production speed.
2Measurement precision
If the polishing surface of the tip is moved to a position for light beam irradiation to enable inspection, then the polishing state can be measured, but the limited degree of freedom of the welding gun prevents proper positioning
Solution Approach 1:
The patent replaces the mechanical positioning system (moving the polishing surface to a specific position for light beam irradiation) with an embedded sensor system that can measure the tip surface in-situ during normal welding operations. This eliminates the need for complex positioning movements while maintaining measurement capability.
3Reliability
If the number of tip inspection devices is increased to match the increased number of welding robots, then all robots can be monitored, but the cost of installation and operation increases
Solution Approach 1:
The patent makes each welding gun self-inspecting by embedding the sensor within the gun itself, allowing a single type of device to serve multiple welding robots. This universal solution eliminates the need for separate inspection devices for each robot, reducing overall system complexity and cost while maintaining full coverage.
Solution Approach 2:
The welding gun performs its own inspection through the embedded sensor, eliminating the need for external inspection devices. This self-service approach reduces the total number of devices required while ensuring continuous monitoring of all welding guns.
4Reliability
If tip inspection is performed frequently to ensure quality, then welding defects can be prevented, but production time is lost and consumable costs increase
Solution Approach 1:
The embedded sensor enables continuous or near-continuous inspection of the tip surface during normal welding operations, eliminating the need for periodic stopping and separate inspection cycles. This maintains welding quality while maximizing production time utilization.
Data Source
AI summary
A system managing a polishing state of tips of a welding gun of each welding robot installed in a production line of a vehicle includes: a robot controller storing tip polishing data including the number of polishing of the tips and a polishing amount of the tips generated after each tip dressing of the welding gun; and a server collecting the tip polishing data from the robot controller to store the collected data according to robot identification information of the robot and learning the store data through artificial neural network to generate reference data determining the polishing state of the tips corresponding to the robot identification information. The robot controller sets artificial neural network of the robot based on the reference data and determines whether a polishing state of the tips according to the number of polishing and the polishing amount of the tips is normal.