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30 SMT007 MAGAZINE I JULY 2021 aware of surroundings, seeing issues develop- ing out of the corner of our eyes, that we would remember and address. Without the ability to use our eyes, another form of visibility is needed. We need to gather data. e history of gathering and trusting data from manufactur- ing has been fraught with challenges—thank- fully now being resolved through the increased use of the plug-and-play IPC-CFX standard, which drives IIoT data exchange across the whole shop floor. Of course, data is not vis- ibility; it must be contextualized through the IIoT-based MES layer, which builds event information based on disparate data trails, together with known configuration informa- tion, material conditions, work-order perspec- tives, etc., to create the live digital twin of the manufacturing operation. is visibility is our digital peripheral vision without us interacting directly. It's not incorrect to say that the method to improve reliability is to be data-driven, but is very much a simplification. Raw data, propri- etary data, and even IPC-CFX are not solu- tions in themselves, including measurements and inspections. Any data that is gathered must be processed in some way in order to create value. Machine learning famously processes raw machine data measurements to refine and improve the operation. is is incorrect; value comes from the interpretation of results, by another party, such that the determina- tion of whether an escape is really a defect or not, for example the result of an inspection by AOI. Qualification and contextualization bring the value. SMT line closed-loop solu- tions are another example, where soware provides qualification of variation in inspec- tion data coming from one or more machines, then applying corrections and compensation to other machines to keep variation under control. However, it is incorrect to believe that the value of such solutions comes solely from the raw data. Every data point is contex- tualized by analytic soware, based on such things as the dimensions of the PCB, the use of different nozzles for different materials, etc. e type of correction is based on the knowl- edge of the product, work-order, and materi- als which do, of course, change. A difference in the material vendor, representing variation potentially in the size, shape, or features of that material, could lead to the closed-loop so- ware making a false call. Holistically, further information needs ultimately to come from MES to understand the complete context of the operation. ese two simple examples illustrate the use of contextualized data to make decisions that impact reliability; in other words, preventing defects from occurring not only during manu- facturing, but out into the market. Using trend analysis and refinement in the understanding of the threat that variation represents, a whole slew of hands-on actions is prevented that, in a defect-ridden process, compound qual- ity risk geometrically. ere are many other areas where hands-off, data-driven manufac- turing ensures that no "out of control" condi- tion is reached to the extent that human action is required. Six Sigma is the leading example of a statistical tool that detects in real-time whether a complex series of data points will remain within control limits, or whether there is a chance that the limits could be exceeded in the near future. Six Sigma is therefore a good engine to be used for "AI-based" monitoring. e problem again is that, as with the case of raw data, the use of Six Sigma is only a means to an end, a tool that needs to be used, with algorithms needed that enable it to be effective for use. Without the ability to use our eyes, another form of visibility is needed. We need to gather data.

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