Increase effectiveness and efficiency by optimizing your test stations
Leak test is one of the most problematic tests on the food machine production line. The more data you can collect and analyze from each test cycle, the greater the insight you have to increase the station’s reliability and efficiency.
First you have to equip the test station with the right “smarts.”
Software is a key component. Yes, you can improve the electronics, but it’s the software that allows you to achieve better pressure regulation, measure with greater accuracy to smaller increments, and most importantly, gain insight from the digital process signatures or “waveforms” generated by the test. With the right data management software, these signatures can be converted into histograms to easily distinguish a good part from bad, establish a range of acceptable deviation and quickly trace the root cause of a quality issue.
Studio waveform trend using Sciemetric Studio
Sciemetric Studio software presents the visualization of data through waveform overlay, allowing you to easily identify process deviation and root cause of failure.
See our post, 8 ways we can help you improve your leak test, for more on what this will allow you to achieve.
Set your data management and analytics system up for success
Collecting data is just the beginning—to thrive in the Industry 4.0 environment, you actually have to be able to do something with that data.
That means collecting the right data from each cycle of each process and test station on the line, serializing that data into a birth history record for each part and managing it in a single central database. Only then, can a quality team begin to unlock useful and actionable insight from production data.
Data that can’t be utilized in this way has no value, no matter how much of it there is. It’s just noise, taking up storage capacity—a literal waste of space.
Learn more about making the most of your data with the right analytics tools.
Boost your IIoT strategy with machine vision images and data
When it comes to collecting and managing the right data, don’t forget about those machine vision images and datasets that are too often left to languish in silos across the production line.
Machine vision software
Centralized access and serialized machine vision data lets you
find the information you're looking for quickly with Sciemetric Studio.
Images and vision data are just as useful as process signatures and scalars when it comes to gaining the insight a team needs to raise the bar on quality, efficiency and first-time yield.
Learn more about the importance of vision data as part of your IIoT strategy.
Make sure you’re taking advantage of the full potential of your technology
The first step in an Industry 4.0 strategy is to make sure existing technology investments are being used to their full potential.
Take a digital image from a smart camera already on the line, used to capture each cycle of a sealant dispense operation. The vision system can take that image and break down the sealant bead into thousands of regions. Metrics like the width of the bead and the offset from expected center position can be measured in each region. These unique scalar measurements can be stitched together as an array to provide a digital process signature (think a plot graph of measurement versus segment number). This can show how the dispense measurements vary across all the segments.
Here’s a deeper dive on how scalars taken from an image become more valuable than the image itself.
Set your production line up to capitalize on the era of smart manufacturing
Smart manufacturing is no longer in the future—it’s here. Nothing recapped in this post is theoretical; it’s become routine for progressive manufacturers that realize the value of making the necessary technology investments to embrace, and profit from, Industry 4.0.
The result is a more proactive approach to product quality. Engineers have quick insight into trends and patterns that reveal the “how” and “why” of decreases in yield and quality and can apply refinements to test limits or other upstream quality control benchmarks. This drives quality gains, reduces scrap and rework and warranty claims, and accelerates the commissioning of new lines and new plants.
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