Live Acquisition · Research
Intelligent Process Monitoring for Weld Defect Detection in GMAW
Technische Universität Chemnitz · Aug 2025 - Aug 2026
Problem
In welding, quality is normally verified only after the weld is complete, through post-process inspection. That is slow and costly, and defects in the weld seam - porosity, slag inclusion, burn-through, irregular penetration - are caught only once the component is already produced, where they can compromise the strength and service life of the whole structure. Industry needs to detect and correct issues during welding itself.
Approach
The weld result depends on a set of process features - current, voltage, wire feed rate, gas flow rate, arc distance, travel speed, gas-nozzle angle and more. I developed a Python framework that connects to multiple hardware systems to capture these in real time: an NI USB DAQ with a signal-conditioning unit for current/voltage, a Baumer monochrome industrial camera, and an Optris thermal camera - all synchronized by timestamp. The framework supports configurable sampling rates, multi-rate logging and auto-trigger on arc voltage, with a GUI for live waveform display and camera preview. The architecture is built to extend to additional sensors as the project progresses.
Result
- Scales measurement from a 10 Hz baseline to 20 kHz high-frequency acquisition
- Synchronized multi-sensor fusion - electrical, vision and thermal data, timestamp-aligned
- Generates structured, analysis-ready datasets for ML-based autonomous welding control and real-time weld-seam analysis
- Validated against NI PXIe and FlexLogger via time-aligned waveform and RMS analysis
Tools & Methods
PythonNI USB DAQSignal ConditioningBaumer CameraOptris ThermalMulti-rate SamplingGMAW
● Live acquisition GUI - synchronized V/I, camera + thermal preview, up to 20 kHz
