IIoT / Smart Manufacturing
Tearing down an engine to find a problem when it fails an end-of-line test is costly and time-consuming. It’s much better to identify a quality issue upstream on the production line where it occurs. Learn how an automaker used digital process signatures to adjust their fuel rail insertion parameters to catch faulty insertions before they reach the end of the line.
The traditional approach to testing weld integrity is often destructive pull-test, with its shortcomings. Perfectly good assemblies may be ruined, while faulty ones can still slip through and be shipped for use. With digital process signature analysis, however, all the metrics that identify problems can measured to catch defective parts.
The valve tappet setting application can have a huge impact on the overall quality of an engine. If the valve tappets are not adjusted and tightened within precise parameters during manufacturing, it can cause premature wear and excessive engine noise during operation. Learn how Sciemetric helped an automotive manufacturer boost quality and repeatability with an efficient solution for the valve tappet set station.
Many manufacturers continue to focus on data collection rather than data utilization. They don't yet have the modern data analytics tools in place that will allow them to squeeze from their part data the actionable insight that is crucial to meeting Industry 4.0 benchmarks they are seeking for quality, yield and traceability. Learn why digital process signatures are your first step to meet Industry 4.0 objectives.
A chief goal when it comes to optimizing any leak test is to ensure if the cycle time of the test can keep up with the pace of production. One factor in optimizing the test so that it can cycle as many parts as quickly as possible with an acceptable range of repeatability and reliability is test pressure. Consider the following factors when identifying proper test pressure for your leak test.
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.
It’s when manufacturers are faced with a recall or warranty claim that they realize data collection alone has little value if they can’t easily access and analyze it. Learn how we helped one manufacturer of agricultural machinery use their data to improve their root cause analysis time from weeks to minutes!