Machine Vision and Industrial Automation

Machine Vision Platform Gives Manufacturers the Big Picture

May 13, 2017
The new Vision platform collects data from multiple machine-vision systems for simplified quality analysis across the plant floor.

Sciemetric Instruments Inc. announces the launch of its QualityWorX Vision platform, which offers comprehensive image data management and analytics for machine vision systems used in smart factories. The platform can collect data from multiple assembly lines, enabling more effective automated quality control not limited to finding the source of defects and optimizing production speed.

“By adding Vision capability to our QualityWorX platform, we are helping manufacturers optimize their existing machine vision investments, and make that data do more,” says Mathew Daniel, Sciemetric’s Vice President of Operations.

The system has a high capacity for collecting and storing image datasets from one or more machine vision stations in production real time. Information is collected and archived in a centralized database, and can be analyzed with the other datasets pertaining to a specific part or assembly. The platform’s processing power makes it reliable to eliminate silos and keep plants running smoothly.

The platform is designed to analyze data in ways that are useful to manufacturers and QA managers. It generates consolidated product birth histories that layout image overlay information, scalar data, and digital process signatures for easy product tracking across multiple assembly lines. It can also launch, calibrate, and set limits for machine vision stations. By producing SPC histograms and trend data, it facilitates upper and lower specification verification. It can also use image data to detect leaks, ensure safe fastening, and perform other diagnostics.

Finally, QualityWorX Vision is designed for compatibility with major data and image collection vendors like Cognex and Keyence. It can be used to carry out selective warranty recalls, quickly figure out root causes, and issue resolution from a single platform.  

About the Author

Leah Scully | Associate Content Producer

Leah Scully is a graduate of The College of New Jersey. She has a BS degree in Biomedical Engineering with a mechanical specialization.  Leah is responsible for Machine Design’s news items that cover industry trends, research, and applied science and engineering, along with product galleries. Visit her on Facebook, or view her profile on LinkedIn

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