The demand for quality information of various welding methods is increasing in line with the trend of new material welding, the increase of micro and ultra-precision products, and the increasing demand for eco-friendly and energy-saving welding processes.
In particular, the MONITECH team wanted to implement AI technology, to establish a high-precision welding monitoring system for guaranteed laser welding quality of lithium-ion batteries, which are exploding in demand along with the rapid growth of the electric vehicle market.
Data Collection & Modelling
Welding Quality monitoring
AI algorithm development
API based integration system
Dashboard / Visualization
Maintenance
Technological Challenges
The laser/ultrasonic welding monitoring system
should deliever accurate and constant quality analysis from various power sources.
Reliable Quality Monitoring
Application of deep learning modeling by combining Monitech’s knowhow on exiting quality monitoring methods and Ellexi’s Confusion Matrix analysis on various sensor data, such as UV, IR, light, high-speed thermal image, to secure high defect detecting precision and minimize false alarms.
1
Multi-sensor compatibility
Overcome limitations of statistical and wavelength data analysis on existing multi-sensor (optical rapid + thermal image, vision + rapid thermal image etc.) configuration-based quality monitoring system by applying deep ledrning technology.
2
Platform with scalability
Provide modulation of AI models by its welding method (arc, spot, ultrasonic, laser etc.) and RestAPIs for interoperability.
3
Road Map
M
-
Philo-AD Installation / Data Collection
M+3 / M+4
-
Model Verification
-
Abnormal behavior visualization
-
Security Risk behavior adaptive learning
M+5 / M+6
-
Platform system configuration
M+1 / M+2
-
Time series Data Modeling
Key Features
Pattern Monitoring (weld quality monitoring from UV/IR signals)
1
Detection of weld defects based on charges in process parameter
2
Process Visualization
3
AI weld quality judgement algorithm (reference adjustments)
4
Real-time laser welding quality management in mass production line
5
The Result
Performance Improvement
90%
above
Welding Result Recall
Achieved 99% recall when applied to the lead welding process of BMA (Battery Module Assembly) line
3s
within
Judgement after receiving welding
It took less than a second from data measurement to derivation of quality monitoring result
100%
API Integration
AI modules fully integrated with EV battery laser welding quality monitoring system
Fast grasp of welding quality judgment through visualization
