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REAL TIME WITH... IPC APEX EXPO 2022 SHOW & TELL MAGAZINE I I-CONNECT007 179 Interview by Nolan Johnson Nolan Johnson catches up with Rachael Temple, who shares her first-time impressions of the show. Nolan Johnson: How does it feel to be back at APEX EXPO? Rachael Temple: Well, this is my first time here. It's kind of crazy. Lots of booths, lots of companies here. It's really exciting. I know this is the first time back since 2020 for everyone else. It's good to see all the companies that came out here. I know there are not many attendees and not as much as we'd prefer, but we're still getting good leads, meeting new people, and checking out competi- tion as well. Johnson: So, it seems like a good first impression, even though this is your first show here. Temple: Yes. I would say. Johnson: What do you want to take away? At the end of the week, when you go back home after being here, what will make it feel like it was a suc- cessful trip for you? Temple: The big thing for me is to learn— learning about all the differ- ent services, machines, the people, connections. I want to see where our company fits into others, how we can provide services and products for them, how they can work for us, and just basically get out there. Johnson: What are your impressions of being in this industry? We've gone through a long period of time where it was just not cool to be in manufac- turing. Your thoughts on that? Temple: Well, I'd say it's a lot to learn because I didn't study any of this. I didn't know about any of this. They don't really have this option at some of these schools to get into this industry. So, it's opened this whole realm, this whole world to me that I never even knew about, even though I'm part of my fam- ily company now and my dad has been involved. I had no idea it was this broad. I had no idea the number of companies and people involved and it's pretty cool to be out here and experience this and it's definitely interesting. Johnson: Awesome. Well thank you, Rachael, I appreciate that. decision-making. Traditional ways of data interpretation are labor intensive and time consuming. Failure to accurately and precisely translate data will lead to subjective "opinion" or "speculation-based" decision-making. In this paper, general opportunities are reviewed for the application of machine learn- ing (ML) algorithms and methods to the test data troubleshooting process. A method is developed for analyzing data and identify- ing patterns that are consistent with poorly performing units. is method uses a "quasi- supervised" learning technique to identify drivers of variance within a dataset, visualize the trends among the primary drivers of vari- ance, and establish some screening limits based on those trends. e method employs principal components analysis (PCA) to review patterns, trends, and uses some knowledge of better or worse performing groups. e output is a set of screening limits that characterize parts likely to have similar performance. e method provides clear knowledge, visualization, and understanding of the trends that are driv- ing failures or poor performers. In addition, it does not require the rigorous data capture that a true supervised learning method does. is method can be used on any dataset with observations in the rows and attributes/ Voices of the Show: Rachael Temple, Alltemated

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