The Future of Machine Learning on Pipeline Corrosion
The Future of Machine Learning on Corrosion. How One Team is Automating the Industry: From Pipeline Corrosion and Beyond
Rebecca A. Bickham
Machine learning is something you encounter daily whether you realize it or not. From smart phones, apps, Siri and Alexa, online advertisements, and selfdriving cars, you potentially encounter artificial intelligence (AI) thousands of times per day. But did you ever consider how machine learning could influence the corrosion industry? Joseph Mazzella and Tom Hayden of Engineering Director, Inc. (EDI) (Evanston, Illinois, USA) are doing just that. “EDI is a consulting firm specializing in developing, implementing, measuring, and administrating lean business processes and strategies, through the effective use of information technology, AI, and geographical information systems [GIS],” says Mazzella, who is CEO of the company. “We have a keen focus on the corrosion industry.”
While Mazzella’s background is in corrosion, operations, and sales engineering, Hayden’s is in software development with a background in consumer technology, having been an early employee at Facebook and GrubHub. They may seem like an unlikely partnership, but when Mazzella went searching for weather data for a corrosion research project with a North American pipeline operation, he crossed paths with Hayden, who was operating an open source data library for processing National Oceanic and Atmospheric Administration weather feeds, and the rest is history.
Podcast: The Future of Machine Learning on Corrosion
8/28/2020 12:00 PM
In this podcast, NACE members Joe Mazzella and Tom Hayden of Engineering Director, Inc. (Evanston, Illinois, USA) join the MP Interview Series to discuss the work they are doing to combat corrosion through the use of machine learning.
They provide a brief overview of their project that involves estimating corrosion growth rates in underground pipelines; details about the computer vision app they are creating; and an update on NACE task group TG 589.
More information on their work is available in the August 2020 issue of Materials Performance (MP).
By admin|2020-09-03T21:08:15+00:00October 15th, 2017|News|