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.
There is a tendency to think about the line in terms of individual process, with separate solutions for force-distance monitoring, leak testing, torque testing, etc., rather than as a whole—but it doesn't have to be this way. With one common solution, like the sigPOD, one platform can do it all. Learn how commonality on the production line helps manufacturers save time and money.
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!