What can go wrong will
Even on a modern manufacturing line equipped to collect and analyze process data, something can always slip through the cracks to derail quality and efficiency. This is only to be expected. But what if a key assembly process still rests entirely with a human being manually completing a task? There is no obvious data trail, no process monitoring in real-time. What then? In this article, Sciemetric's Manufacturing IT Manager, Patrick Chabot discusses the importance of having a strategy for continuous improvement on the line.
Vision Systems Design announces 2018 Innovators Awards
Sciemetric's QualityWorX Vision software won the Gold 2018 Innovators Award from Vision Systems Design. Read more about the award-winning solution and the awards in this article.
Case study: Shaping data, ensuring uptime
Industry 4.0 technologies are not merely nice to have. Business pressures are forcing these kinds of investments for many manufacturers. ROI can be rapid when you consider the cumulative cost of downtime, warranty claims, and scrap and rework that can be avoided with the right technology investment. Learn how Sciemetric helps manufacturers embrace Industry 4.0 in this article by Sciemetric's Aaron Alberts for the Smart Industry Forum.
Electric vehicles present unique testing challenges
Rob Plumridge, Sciemetric leak application engineer, discusses best practices for leak testing e-vehicle batteries and how the 3520 Series Leak Test can be used for this purpose, with Assembly Magazine.
Effective data analysis
In this feature published by Automation Magazine, Dave Mannila, senior product manager at Sciemetric, talks about how manufacturers can and should use their production data for greater visibility into production processes – to reduce cycle times, control costs and improve productivity across the plant. With today’s tools, a modest investment can generate an ROI in a matter of months and yield millions in annual savings.
How to stay competitive in a connected revolution
In this feature published by Machine Design Magazine, Derek Kuhn, Sciemetric’s senior vice-president, argues why machine builders can’t ignore the growing use of process data by manufacturers to drive quality assurance, greater automation, efficiency and profitability. Manufacturers want this intelligence incorporated into a line as it is being built, rather than incur the time and expense of procuring equipment, hardware, and software from different vendors and trying to integrate it all together.
Collecting data: Why machine vision matters as part of your IIoT strategy
Whether you’re a machine shop, a job shop, or a contract manufacturer in the supply chain of a major OEM, your success today rests with how machine vision data comes together with all the rest of your production data. In this article, Sciemetric's Manufacturing IT Manager, Patrick Chabot discusses how to be more competitive and break machine vision out of its silo.
Adding vision to process monitoring
Today’s Motor Vehicles editor Rob Schoenberger talks with Mathew Daniel, Sciemetric’s VP of Operations, about the network architecture and best practices manufacturers must adopt to make more effective use of their machine vision images and data to drive quality and contain defects.
Eight reasons why you should manage your machine vision data
In this feature with Vision Systems Design, Mathew Daniel, Sciemetric’s VP of Operations, explores how bringing images and image data into the serialized birth history record for each part in production opens the door to advanced analytics for process improvement and traceability for defect containment. It just takes careful planning to determine how best to collect, store and manage this data.
Harvesting data for Industry 4.0 profitability
Derek Kuhn, Sciemetric’s senior vice-president, takes to the pages of APMA Lead, Reach, Connect to explain why auto manufacturers must look beyond data related to the performance of machines and business processes if they wish to succeed in a sector that is rapidly being redefined by Industry 4.0 and Manufacturing 4.0 principles. They must also collect, serialize and analyze the reams of production data generated by the process and test stations on the line.