The time is now for manufacturers to start moving toward a smart factory before being left behind by their competitors. But where do they begin? The challenge is that there’s no right answer. To come up with workable solutions, manufacturers must clearly understand the operational problems they face.
For example, many manufacturers still conduct manual defect detection on the production line. This is not only expensive and time consuming, but often leads to inaccurate results. One way to address this issue is to apply machine vision and deep-learning models to smart cameras and automate the quality inspection process.
In this podcast, we will explore changes happening on the factory floor, what makes a smart factory run, and how machine vision and AI improve the product inspection process.
This episode features David Dewhirst, Vice President of Marketing at Mariner, a provider of technology solutions that leverage IoT, AI, and deep learning. Prior to joining Mariner in January 2021, David cofounded marketing agency ThreeTwelve in 2011, where he worked for almost 11 years. At Mariner he leads strategic planning, execution, and oversight of all marketing initiatives.
David answers our questions about:
- (2:15) What is a smart factory?
- (3:56) How manufacturers have adapted to digital transformation
- (7:43) Getting started on the smart-factory journey
- (9:32) Computer vision vs. machine vision
- (13:47) Importance of AI for product defect detection
- (17:15) What to do when there is lack of IT support
- (19:20) Data processing in the cloud and at the edge
- (22.41) Working in a partner ecosystem
A few selections from the conversation:
"You're going to have to do this Industry 4.0 thing because all of your competitors are doing it -- and if all of your competitors are doing it and your're not, you're going to be left behind. So pretty soon, Smart Factory initiatives will just be table stakes."
"Want to know what to work on in your Smart Factory journey? Engineers on the floor will often be aware of day to day problems, and they're good at making those problems go away. But that skill in ameliorating a problem might mask a more serious issue -- and they might actually love to have a solution to those problems if you just asked."
"It's very hard to handle "fuzzy" defect detection problems using arbitrary rules and traditional, binary programming languages. And that's where AI comes in: With Deep Learning techniques, you don't make an arbitrary rule, you train it on pictures of fuzz and pictures of stains and it learns the difference."
Good stuff, right? You can watch Intel's entire product defect detection tech podcast here.
To learn more about defect detection, read Getting the Smart Factory to 20/20 Machine Vision and A Guaranteed Model for Machine Learning. For the latest innovations from Mariner, follow it on Twitter at @MarinerLLC and LinkedIn at Mariner.