Using AI, Machine Vision and Edge Computing for Quality Control
Dave Austin, Senior Principal Engineer, Intel Corporation
Manufacturers are embracing Industry 4.0 technologies like AI and machine vision to turn the challenges presented by COVID-19 into opportunities. For example, the manufacturing industry is facing a labor shortage, making it especially difficult to fill specialized roles. Manual weld inspection for automobile and heavy equipment manufacturing requires highly skilled engineers. Sometimes as many as 18 per vehicle inspection. To address this challenge, organizations have begun to pilot automated weld-defect detection solutions in their factories, that rely on AI, machine vision and edge computing. These solutions are capable of spotting defects not visible to the human eye, allowing manufacturers to address defects in real time.This has not only helped these organizations overcome the labor shortage, but also reach new levels of efficiency. The potential applications of AI and machine vision, in combination with edge computing, are almost limitless.
In this session, Dave will share examples of manufacturers that are successfully using AI and machine vision, in combination with edge computing, to improve the welding process in different ways; discuss special considerations and challenges for machine vision in manufacturing settings; and share tips and best practices for manufacturers of all sizes to begin their own AI and machine vision for quality control deployments.
David Austin is a Senior Principal Engineer at Intel, leading AI projects in computer vision and machine learning. His work focuses on deep learning architecture design, traditional computer vision. He has spent 19 years working in manufacturing related data science roles at Intel. David was the 1st place winner in the 2nd Annual YouTube-8M Video Understanding Challenge, and is currently ranked #11 in the world competition rankings out of 112k competitors on the Kaggle data science platform.