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Issue link: https://mms.epubxp.com/i/864774

74 MMS September 2017 mmsonline.com FEATURE 0.001 inch over nominal actually delivered a better process capabilit y index (Cpk). Yet the more specific and compelling problem of a part being scrapped because of one small variable that just happened to go wrong remained beyond the reach of this analysis. A team from a large, well-known manufactur- ing company visited the shop during this time. The members of this team had gotten no further within their own data-driven processes. They, too, analyzed data after the fact. They had math- ematicians looking for predictive trends. Learning this, Mr. DiNardi looked toward the same approach. He thought a team of industrial engineering students might be able to help deter- mine which trends in the data were significant. He even had monitors installed near each machine in preparation for the operators beginning to watch the data and respond to significant trends. But this solution, the complicated solution, simply didn't gel. The simpler solution that Ms. Morlacci and he r te am late r c ame to amounted to this: In a sense, forget the mathematics. Forget the statistics—or at least, forget the idea of predic- tive models based on statistical trends. Instead, recognize that the probing data for a span of parts that all successfully went on to pass inspec- tion provide a picture of a process performing well. The uncertainty of the on-machine probing measurements means that the probed measure- ment for a given machined feature invariably has to fall well within the tolerance band for that feature to assure it will pass inspection later at the coordinate measuring machine. Therefore, this picture of a process performing well at the machine tool consists of data that adhere to a tighter range than the acceptable range in QA. The relationship between probe measurements and specific process variables might still be unk nown, but the probe measurements can provide a trip wire indicating that something significant in the process is beginning to change in an adverse way. Using the probe data in this way—that is, as a picture of good performance at the machine, a probe-measured predictor of likely success—has enabled Ms. Morlacci to develop an on-machine This graph, and the data trend it illustrates, shows the value of the process control system at L&S and how the team here is likely to use it. This particular feature (a machined slot) has a nominal dimensional require- ment of 1.65 ±0.010 inch. However, typical performance runs within a much tighter envelope than this— within the two closer red lines around 1.645 inches. Near the left-hand side is a data point (the seventh part measured in this series) that broke out of the typical performance band. If Mr. Smathers or Ms. Morlacci had been asked to respond to this, he or she likely would have done nothing. That violator proved to be an anomaly; the process continued within the typical band. Then, much later came the point at which some effect (it proved to be tool wear) caused the measurement trend to break out of its typical performance and stay out. The team would have responded as this trend continued, finding and fixing the cause of this trend before the actual tolerance was broken.

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