Problem
High-volume inspection does not tolerate fragile image pipelines. Acquisition, calibration, lighting, vendor tooling, and computer vision logic must stay stable while production conditions change.
Back to systemsConfigured imaging setups and calibration flows for production use. Worked across industrial camera and inspection vendors instead of being locked to one toolchain. Used CI/CD, AWS CLI, Kubernetes, and IoT contexts where the vision system touched modern infrastructure.
Northvolt
Industrial Machine Vision Systems
Machine Vision Engineer / Feb 2024 - Jun 2025
Delivered industrial imaging work across line-scan, area-scan, calibration, OpenCV analysis, and vendor vision platforms under real production constraints.
Line scanArea scanOpenCV
My Role
Worked hands-on across imaging setup, calibration, industrial vision platforms, and C++/Python computer vision workflows, with focus on practical reliability over lab-only demos.
Impact
A production-grounded machine-vision skill set that combines image quality, algorithmic judgment, vendor tooling, and deployment awareness.
System Architecture
- Line-scan and area-scan sensors capture production imagery.
- Calibration and imaging configuration stabilize measurement and inspection inputs before algorithms are trusted.
- OpenCV-based analysis supports inspection, validation, and debugging.
- Industrial platforms connect inspection output into wider production workflows.
Tools
OpenCVC++PythonKeyenceCognexDr. Schenk
Implementation Highlights
The useful work sits between camera signal and production decision.
Next Step