In-Process Test
Fastening, Pressing and Industry 4.0
Engineers can do more with force and torque data than simply make “good” or “not good” decisions about assemblies. Sciemetric's CTO, Richard Brine, is featured in this article in Assembly Magazine discussing the applications of fastening and press for Industry 4.0.
Best practices: Electric Vehicle (EV) battery pack leak testing
In this article, Sciemetric's Rob Plumridge identifies the factors that companies can use to determine how to test for quality flaws in lithium-ion battery enclosures.
New Technologies Impress at the Assembly Show
The ASSEMBLY Show 2018 announces the inaugural Product of the Year winner, QualityWorX DataHub from Cincinnati Test Systems and Sciemetric. The DataHub is a simple, cost-effective tool for analyzing leak test data and performing test-to-test comparisons. The product aggregates data from multiple leak test instruments into an analytics database for real-time usage. Learn more in this article.
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.
Gaining The Manufacturing 4.0 Advantage With Data-Driven In-Process Testing
Manufacturing is changing thanks to the increasingly sophisticated and intelligent use of data to make a production line smarter and more efficient. For off-highway and specialty vehicle manufacturers, the 4.0 revolution offers great opportunity to achieve significant cost reductions and grow revenue. In this article, Sciemetric's Product Manager, Dave Mannila, discusses the benefits of data-driven in-process testing.
Data holds the key to refining processes
In the pages of Industrial Technology Magazine, Sciemetric CEO Nathan Sheaff highlights five things manufacturers can easily do with their process data right now to take the guesswork out of limit setting, optimize test cycle times, trace the root cause of defects, predict maintenance requirements, and launch machines and lines faster.
Waveform versus scalar data
Sciemetric product launch manager Robert Ouellette shares with Manufacturing Automation Magazine how digital process signature analysis takes take quality control on the production line to a new level. While SPC and scalar data continue to serve a useful role to monitor and track the health of a production line, it is signature analysis that offers the most effective means to quickly find and address root cause when problems arise.
Quality: Starting with Effective Data Management
Richard Brine, Sciemetric’s CTO, discusses with Industrial Machinery Digest how manufacturers must evolve beyond scalar data collection and analysis alone to rise to the big data challenge posed by Industry 4.0. He outlines the need for centralized collection and analysis of the digital process signatures, or waveforms, generated by every cycle of the process and test stations on the line.
Better Tools for Data Analysis Measurement/Visualization are Improving Yield and Reducing Warranty Claims
Sciemetric CEO Nathan Sheaff talks to the editors of OEM Off-Highway Magazine about how “Industry 4.0” is really about breaking down data silos and making effective use of production data to increase yield, improve quality and optimize processes.
The Birth of In-Process Testing
An automaker’s search for something better than hot test redefined quality on the assembly line with the introduction of Sciemetric's Process Signature Verification (PSV) technology for in-process test monitoring and analysis. In the years that have followed, Sciemetric technology and our customers' adoption of it have evolved with the emergence of Industry 4.0 and the smart factory.