Machine Learning in High Volume Media Manufacturing

TL;DR

This paper presents a scalable machine learning-based system that enhances failure detection in high-volume manufacturing by adapting to design changes and operational variations, outperforming traditional rule-based methods.

Contribution

It introduces a novel hybrid program combining rule-based and machine learning approaches for scalable, adaptive failure detection in high-volume manufacturing environments.

Findings

Successfully deployed at scale in a manufacturing setting.

Improved failure detection accuracy over traditional rule-based systems.

Adapted to design changes and operational variations effectively.

Abstract

Errors or failures in a high-volume manufacturing environment can have significant impact that can result in both the loss of time and money. Identifying such failures early has been a top priority for manufacturing industries and various rule-based algorithms have been developed over the years. However, catching these failures is time consuming and such algorithms cannot adapt well to changes in designs, and sometimes variations in everyday behavior. More importantly, the number of units to monitor in a high-volume manufacturing environment is too big for manual monitoring or for a simple program. Here we develop a novel program that combines both rule-based decisions and machine learning models that can not only learn and adapt to such day-to-day variations or long-term design changes, but also can be applied at scale to the high number of manufacturing units in use today. Using the current state-of-the-art technologies, we then deploy this program at-scale to handle the needs of ever-increasing demand from the manufacturing environment.