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Advanced Imaging Magazine

Updated: July 8th, 2008 05:26 PM CDT

Marketing Machine Vision for Test and Measurement

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ROUNDTABLE

April 2004

Marketing Machine Vision for Test and Measurement

by Barry Mazor

The Participants

Tim Ludy is Product Marketing Manager at Data Translation, Marlboro, MA. (www.datatranslation.com) Kyle Voosen is Vision Product Manager at National Instruments, of Austin , TX . (www.ni.com) Dr. Phil Heil is Director of Information Technology at DVT Corporation, Duluth , GA.(www.dvtsensors.com)

The value of machine vision for test and measurement applications has long since been demonstrated?but, as in so much marketing, that doesn't mean that the value needn't be explained all over again to customers. How some key players are facing that challenge is the subject of this month's Roundtable discussion.

WHERE'S THE VALUE?

We've heard two schools of thought on one matter, over the years: For meeting test and measurement needs, do you see (and talk about) machine vision as more of an increased production value (rework like scrap, fewer errors etc.) or more of an increase in capacity value (faster testing, outright production gains) ? and why so?

Tim Ludy, Data Translation: If I have to pick one, I would say it's production value first. You cannot, or perhaps should not ramp up capacity unless you are doing it correctly to begin with.

Kyle Voosen, National Instruments: Using machine vision as part of a larger test and measurement system can have a very significant impact on production value. For example, when building electronics, there are several assembly steps that occur before the in-circuit and functional tests. Unfortunately, when a defect is found during test at the end of the production line, every product that was built with it has potential for the same defect. This could lead to rework and potential scrap. Machine vision can be used to implement localized process inspection, finding these defects (allowing timely corrective action) well before final testing.

Phil Heil, DVT: If either, it helps more with production value. An example of this would be machine vision controlling robots. Machine vision helps with throughput if it automates work previously performed by humans.

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