JUL-AUG 2017

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se rial number in a different silo. Any ef- fort at data retrieval and correlation to troubleshoot a quality issue took days with custom query tools. And let's not overlook a clear archival policy. This will govern the size and type of storage media you require. For exam- ple, you might set the following policies: l Production data must be accessible for a maximum of 12 months l Raw images should be kept for a pe riod of six months l Archival image data must be kept for a period of three years For true manufacturing insight, you need vision You cannot fix what you have not mea- sured, and you cannot measure un- less you collect, correlate, and analyze the right data. What is the right data? It should be clear by now that this is a trick question—all the data related to a part or assembly is the right data. Collecting it is seldom a problem. Discrete manu- facturers have been collecting parts data in various ways from their produc- tion line processes and equipment for decades. But not all data is the same, nor is it useful in the same way. Manufacturers today have at their dis- posal affordable tools that allow them to harness the true power of all their data, and that includes vision. Applying Manufacturing 4.0 principles to your machine vision data will give you even greater insight into production processes and tests, to achieve higher standards of quality and efficiency and troubleshoot problems faster when warranty claims come through the door. n ABOUT THE AUTHOR Mathew Daniel ( is vice president of operations at Sciemetric Instruments, where he manages service and installation, product development, and manufacturing and quality. Daniel oversees many of the manufacturing data and analytics implementations provided to large manufacturers, helping them to organize and maximize a return from their production data. View the online version at 36 INTECH JULY/AUGUST 2017 WWW.ISA.ORG SPECIAL SECTION Figure 2. View statistical data on your image-derived scalar data. Analyze the image- based profile (or waveform) data to chart trends and adjust processes, such as dispense systems or robotic stations. In this image, the consistency and accuracy of bead location and width during dispensing operations is being analyzed and trended. Station ID: n Useful for station performance indica- tors, such as part count and first-time yield n Can be one or more cameras working together at one or more stations Traceability information: n Serial number – unique part ID n Model variant – useful if specification limits change with the part Tasks: n Useful if cameras are capturing images associated with a particular function being performed on the part Images: n One or more images associated with one or more tasks Digital process signatures, if supported: n Trace data, such as bead width, loca- tion, or profile data from a reference point "Feature" value: n The mathematical function that deter- mines pass or fail n Storing upper/lower specification limits What kind of vision data can you collect? Figure 1. Collect and organize all relevant image data. The surface of the part of the im- age data under visual inspection should be broken into regions, each with a correspond- ing feature node. This makes it possible to collect and index for easy analysis of differ- ent types of data from each region of the part, such as scalar values derived from the images and the digital process signatures. This data can then be used to create different reporting visuals, such as histograms of the machine vision-generated measurements for each part and time-based trends of when measurements were taken.

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