JUL-AUG 2017

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FAST FORWARD l Data from sophisticated visions systems is often managed and used in simplistic ways. l Data silos scattered throughout the plant that contain images and only the most basic of image data are of limited value. l Using vision data holistically helps achieve highly intelligent, agile, information-driven factories that can respond rapidly to change. data for a specific part or assembly. This results in a comprehensive birth history record that can be quickly searched and cross-referenced. What can you do with a birth history record that in- cludes vision data? l Greater insight into station performance: Quick ly highlight stations that are falling be- hind in part count or in yield. This can be par- ticularly useful for parallel station comparisons. l Feature trend analysis: Set limits fast, and track time-of-day or product variances by model. l Part failure process analysis: Review data from multiple processes to trace the root cause of a failed part. This is critical to quickly ad- dressing and mitigating the issue. Take, for example, the failure of a seal in a joint. In a true Manufacturing 4.0 setting, you can take a comprehensive look at the joint's leak test re sults, fastening data, dispense data, real- time video image, and real-time video bead data to quickly identify and address the issue, as well as determine if any other parts are at risk of the same failure. l Selective recall: This correlated data analysis then allows for a selective and targeted quar- antine or recall. Why recall thousands of units and suffer the consequential impact to your bottom line and brand reputation if only a few are at risk? l Proof of compliance: Image and image data that can be recalled by a part's serial number make it easy to provide evidence that the part was built to specification and that manufac- turing and test processes were under control. l Tracking limit changes and playing the num- bers: Generate feature trend reports to ana- lyze the effect of new limit settings. You can also use all your historic data to play "what if?" and run simulations to understand the ef fect of new limit settings. How vision data must be managed to support these activities Many plants are already using centralized data collection, management, and analysis tools for other datasets (e.g., digital process signatures, sca- lars) from processes and test stations on the line. Bringing machine vision images and data into this fold is the next logical step. For plants that are looking at a system for centralized data collection, management, and analysis for the first time, it only makes sense to get it right from the start and ensure machine vision is not a forgotten part of the equation. Either scenario requires an architec- ture for data transfer, be it a wireless network or a fixed Ethernet connection, and an archive. It begins with a software gateway Reliable, real-time im- age data transfer and accessibility requires a software gateway. This could be a software application running on a Windows-based system. It can be locat- ed on the plant floor on a PC or on an application server running multiple instances of the software in a virtual machine environment. The gateway should have the functionality to: l Manage cameras and camera connections: Manage multiple cameras, save all camera soft- ware or "jobs" on the gateway station, show live camera status, and run calibration routines. l Collect and transform images/data: This in- cludes converting the supplied image format of the specific camera to the appropriate for- mat for your database, merging images and overlays with the data format, and compress- ing images for more efficient storage. l Manage those files: Manage image and im- age data file transfer (e.g., mapped folders, file transfer protocols, transmission control proto- col listeners), temporarily or permanently store raw image files for later retrieval and analysis, and ensure data surety by caching data, because cameras and machine vision systems typically have limited or no local data storage. l Make it all get along: This includes "hand- shaking" between programmable logic con- trollers, to ensure the 100 percent reliability of all data transfers, error handling, and system health, including retrying for missing data. User-friendly archive An archive is not a backup of your data. It is a means of keeping the production data reposi - tory at a consistent size with the flexibility to "slice" the data into meaningful buckets—such as blocks of one, two, or three months, to make it easy to rotate older data into longer term stor- age. This archive must be capable of storing terabytes of data in a way that supports easy and intuitive search and retrieval for rapid analysis. The most effective way to accomplish this is for every image and its related data to be indexed by the serial number of the related part or assembly. As mentioned earlier, this makes it easy to cor- relate with all the other data relevant to that unit. We have worked in plants with DIY and vendor- specific databases that stored some data by date and time stamp in one silo and other data by SPECIAL SECTION INTECH JULY/AUGUST 2017 35

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