Sciemetric Instruments has released QualityWorX Vision, a data-management and analytics platform that harnesses machine-vision-image data to drive quality and productivity.
Machine vision is a multibillion-dollar market, as manufacturers increasingly turn to using this technology for automated quality inspection. With the Industry 4.0 trend toward using data for more than basic traceability, the challenge becomes how best to handle terabytes of images and image datasets in production real time, according to Sciemetric. QualityWorX Vision manages and uses this data, enabling manufacturers to optimize their investments in machine-vision inspection.
Images and image data can be collected and archived in a centralized database, from either a single station or an entire production line. More importantly, this image data can then be analyzed with the other datasets that pertain to a specific part or assembly. The result is real-time insights that empower smarter decision making, says the company.
QualityWorX Vision enables quality and manufacturing engineers to:
- Collect and store images, including image overlay information, along with their scalar data and digital process signatures. No more walking down to the production line with a USB stick to get the data.
- Launch, calibrate and set limits for machine-vision stations faster with access to images, SPC histograms and trend data for quick upper and lower specification verification.
- Eliminate silos by collecting data from a single machine-vision station, multiple vision stations or all stations on the plant floor (e.g., leak test, fastening systems, in-process test stations, etc.) into one consolidated part-history record. No other image-data system on the market has this flexibility.
- Collect data from major image vendors such as Cognex and Keyence into one system, with more on the way.
- Carry out selective warranty recalls, faster root-cause analysis and issue resolution through advanced data analytics and access to consolidated birth histories with images, scalar data and digital-process signatures.
Image data can now be used in tandem with other datasets such as scalars and digital-process signatures to drive continuous quality improvements for higher first-time yields, and to reduce scrap and rework rates and warranty claims.
