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The information generated by machine vision systems puts a whole new spin on the term “big data.” The raw image files output from vision systems are huge, and manufacturers can generate terabytes of image data in a month—even in a week! So, what do you do with all this data? Read more to find out!

Are your machine vision needs on the production line better served with a smart camera, or a “dumb,” more basic one? Before you make the investment, consider these factors that can dictate which option is better for a given situation.

With each new year, many of us take time to reflect on our past and set goals for the future. Why not apply the same exercise to your production line? To help you improve your production line in 2019, we have provided the following tips, chock full of insights from our experts.

Many manufacturers face the costly problem of a lagging leak test cycle time. With constant pressures on production, line managers have no choice but to run parallel test stations to maintain production quotas. More often than not, the answer is simple—use your leak test data. Here’s how one customer did it.

Machine vision images and data are a valuable part of the Manufacturing 4.0 equation. The problem is that machine vision images and data are often trapped in silos across the plant floor, with images stored in formats that make them difficult to access and analyze. With the right data management strategy, you can make this data accessible to your team so it can drive value.

Learn about the successes we’ve helped our customers achieve—and how we can help you achieve the same success on your production lines!

Remember that Jurassic Park character, Nedry, who built an IT system no one else could manage? Joe Ventimiglio discusses how machine builders can avoid this mess by not trying to scratch-build a data-driven quality assurance system for a customer.

Machine builders are always struggling to stay on budget, on schedule and on spec with their manufacturer customers. Joe Ventimiglio discusses how incorporating the data-driven quality management of Industry 4.0 into their builds can save them a lot of grief.

We give a primer on how comprehensive data collection from across the plant floor, coupled with digital process signature analysis, can achieve the sometimes contradictory goals of reduced production costs and improved quality.

Does it make more sense to improve the overall yield of a line, or work to reduce scrap and rework rates to boost first-time yield? We crunch some numbers and illustrate the difference that digital process signature analysis can make.