End-of-line Testing for Safety and Quality with Machine Learning

Safety-critical systems are pervasive throughout our society with everyday objects such as airplanes, cars, trains, or medical devices. The requested functionality and expectations from these systems are growing rapidly and consequently, they become more complex. The complexity is usually handled by breaking the system into manageable smaller components and parts. Factories then integrate these parts into the final product. However, while some complexity can be managed
by this divide & conquer strategy, the assembly is still a challenging task. End-of-line testing provides the quality assurance to ensure that no defective product leaves the factory.
In this project, the research team and Acerta develop new learning-based algorithms to assist in end-of-line testing to detect defective product before it leaves the factory. A sequence of interns concentrate on developing classification technology, refining prediction mechanisms, and improving the analytics infrastructure.

Shailja Thakur
Mahmoud Salem
Faculty Supervisor: 
Mark Crowley
Partner University: