Quality Control: Modernizing Industrial Automation

#automation #industrial-automation #machine-learning #quality-control #IAS #IEEE #IES #APS

The IEEE AP-S Dedan Kimathi University Student Branch Chapter and the joint IEEE IES/IAS Strathmore Student Branch Chapter present Session 4 of the Industrial Automation Webinar Series- Quality Control: Modernizing Industrial Automation.

Factories are moving from traditional control approaches toward AI-driven ones, but making computer vision and machine learning work on a live production line is very different from making it work in a notebook. This session looks at what that takes in practice, from constrained edge hardware to real-time inspection.

Our speaker, Mr. Lenny Ng’ang’a of the Japan International Cooperation Agency (JICA) and Robotics Dojo, has led research in industrial Wire Arc Additive Manufacturing, developed custom robotics platforms, and designed automated control infrastructure.

Topics to be covered:

• Present vs. future automation approaches: traditional control compared with the adoption of AI
• Running computer vision and ML models on constrained edge hardware (microcontrollers, edge TPUs, embedded compute) compared with state-of-the-art hardware
• Automated defect detection, real-time quality control, and visual inspection on factory lines
• Real-time inference challenges: latency, power constraints, and sensor-to-model pipelines

This session is for anyone interested in industrial automation, robotics, or applied AI, whether for coursework, research, or industry work.