Data-driven insight to ready the production line for Industry 4.0
A key principle of any digital journey for a manufacturer, whether or not they are in the automotive supply chain, comes down to making effective use of production data. Learn how to ready your production line for Industry 4.0 with data-driven insights in this article, contributed by Sciemetric's Manufacturing IT Manager, Patrick Chabot.
Take the guesswork out of finding your limits
SPC can catch a problem, but only signature analysis can fix it fast
Joe Ventimiglio discusses how SPC (statistical process control) remains an effective means for manufacturers to spot production problems, but for effective quality management and to quickly trace root cause, you need more – digital process signature analysis.
Fundamentals of press-fit monitoring: How to locate defects during press-fit or joining operations
Smart cameras: Unlocking the ROI trapped in the black box
A smart camera or machine vision system can do so much more than basic, "dumb" cameras—but many manufacturers don't take advantage of their full capability in terms of data analysis. That’s where the opportunity arises to generate a strong return on your machine vision investment, unlocking the full potential of the "black box".
How to improve valve tappet set monitoring: A case study
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.
Improve manufacturing defect detection using digital process signatures
10 considerations for effective defect detection during dispense operations
5 tips to improve production line efficiency in 2022
Is machine vision data part of your IIoT strategy? It should be.
Machine vision images and related data can be used for much more than basic pass/fail determination during the process cycle. We get into how this data can be collected, correlated and analyzed will all other production data as part of a comprehensive IIoT strategy.