Automotive Design and Production

APR 2014

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information would allow a more timely spindle replacement, one that would help assure that the parts produced are within spec. ("The spindle just doesn't stop," he says. The bearings wear, and with the wear can come reduced machining precision, which is certainly not a good thing in bore grinding applications.) While Kurfess says that improving local operations like that with data is good, bigger opportunities involves something more: "We are now generating huge quantities of data in plants that really aren't used except for local functions. What if we could take all of those data sets, aggregate them, and make use of them elsewhere?" This isn't, he points out, a matter of adding more sensors to machines: "They are already instrumented. We have a good foundation." Sure, more sensors may be attached and integrated, but Kurfess points out that what's already there are generating plenty of data. Kurfess suggests that it is a matter of going beyond utilizing the data from a given plant to optimize the cutting process, the speeds and feeds, to fully aggregating the information from multiple plants, be they operated by OEMs, Tier One suppliers or are the proverbial "Mom and Pop" shop. By taking all of this data, it would allow companies to determine precisely what the best practices are. One question arises in relation to this, which is simply why someone would allow their machining data to go to a third party. Kurfess admits that there needs to be a business case related to why someone would readily give up this information, but he cites two examples of how this is happening right now. He says that in many small shops, their photocopying machines are online, such that the copier company knows when the toner cartridge needs to be changed. It is automatically ordered and sent to the shop, which is a convenience for the ofce manager. "What if it was tracking tools that were being used in machines? You could tell when the tools were wearing out, and ordering replacements when needed." "If you provide a service that they use and is of value to them, why would they care if you know what their feeds and speeds are?" The other example is one that is as close as your smart phone. Seemingly everyone has Google Maps on their phones. Kurfess says that most people probably don't pay much attention to the permissions that are being granted to Google when installing the app. Google is granted access to some of the data about the phone. "It knows you are going 60 mph along a certain road so they are able to put the green line on the map," he says. Again, you're giving up data in order to get use. So he thinks that the aggregation and analysis of machining data on a massive scale would permit signifcant gains in productivity. If that's improving machining opera- tions at the macro level, there are also changes that Kurfess thinks will be occurring at the on-the-ground level, as well. "One of the things we haven't been particularly good at," he says, "is using fve-axis machines for fve-axis machining rather than three-axis machining." The reason: "Five-axis machining is a hard concept to deal with." But, he says that CAD/CAM system improvements should make it easier for people to make the most of their equipment. Then there are the seven- and nine- axis machines, those with secondary spindles. Kurfess says that in many instances, these machines are purchased for special-purpose, high- volume parts production. At Georgia Tech, Kurfess and his colleagues are doing work on high-performance computing that will facilitate using multi-axis machines to run a variety of parts. This can lead to a whole realm of opportunities in terms of plant capacity: "If I have a shop with 15 of these machines, how do I, in real time, move things around?" he says, explaining that it would be possible to quickly react to a machine going down or new, high-demand orders coming in with less turbulence in the scheduling. And things do change, even in high- production operations. Back when Kurfess was at Clemson, he and some of his colleagues and students regularly worked with BMW personnel at its plant in Spartanburg, SC. "During my interactions with BMW, they were getting changes on a daily basis in terms of production," he says. Having a handle on data certainly is advantageous, regardless of what is being produced. "We are now generating huge quantities of data in plants that really aren't used except for local functions. What if we could take all of those data sets, aggregate them, and make use of them elsewhere?" AD&P; > April 2014 > FEATURE > Big Data & Metalcutting > Gary S. Vasilash > gsv@autofeldguide.com 30 0414ADP FEATURE Machining.indd 30 3/18/2014 12:17:08 PM

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