Modern Machine Shop

SEP 2017

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mmsonline.com September 2017 MMS 71 FEATURE D oes the future have to look futuristic? Do its systems have to perform in a complex way? Questions like these are germane to many of the topics most relevant to the advance of manu- facturing today. Take "automation," for example. The idea is of ten associated with robots and entirely unattended processes. But too much bias toward that association might leave a shop over- engineering its automation solution, undervaluing the many oppor tunities to automate that are simpler than a sophisticated system. And "data-driven-manufacturing" falls into that category as well. Lauren Morlacci knows this. She is an industrial engineer with L&S Machine Co. in Latrobe, Penn- sylvania, a job she earned immediately after col- lege in part thanks to her success with a team of other University of Pittsburgh students who helped L&S develop a data-driven manufacturing system. This system saves cost for this machining busi- ness by using data at the machine tool to predict potential machining errors before they occur, The Data Describe a Good Day This shop established a data-driven process for catching machining errors before they occur. This proved straightforward to do once the shop refined its understanding of what the data are really showing. BY PE TE R Z E LI N S K I L&S Machine's Lauren Morlacci first came to the company when she was in college as part of an industrial engineering intern team. Her team helped to develop and implement the simple system for using data that ultimately proved effective at anticipating and preventing errors in the machining of nuclear- industry fuel nozzles. avoiding the danger of expensive par ts being scrapped because of problems discovered after machining in Quality Assurance (QA). In pursuing this aim, there is no shortage of data at the machine tool that might be used. Indeed, she says, "It was intimidating to look at all the data and know where to begin." Of all the measurements that could be captured at the machine and all the conclusions they might suggest, what trends and what markers were really valuable? How could the shop respond to the right data, and respond to changes quickly enough to make meaningful and effective process corrections? L&S itself had struggled with questions like these for years. According to company President Rob DiNardi, a previous engineering team from a dif ferent university did not deliver a workable solution, but the failure was on L&S rather than this team. "We told them what we thought a data- driven solution would consist of," he says, and that imagined solution was too complicated. It involved finding statistical trends and linking those trends to likely errors and needed corrections. Ms. Morlacci's group was then the second engineering team working with L&S on this prob- lem, and her group was given the luxury of a more open range of possibilities as to how to employ the data, and how even to understand what it is that the data show. The realization she and her teammates ultimately

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