The article presents an experimental stand to assess the state of punch in the process of sheet blanking. Blanking trials were carried out on an eccentric press. During all the trials, there were recorded signals of acoustic emission (AE) that accompanied the process of blanking. For the recorded AE signals, the methodology of data preparation and analysis was presented. On that basis, the results of the assessment of the state of the punch were presented, and they employed five methods of visualization: Andrews curves, Principal Components Analysis, Linear Discriminant Analysis, a modified method of Stochastic Neighbor Embedding and Sammon Mapping. The aim of the work was to assess the possibility of using visualization methods to predict the condition of the tool on the basis of acoustic emission signals in processes carried out in extremely short times.
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Exploratory Data Analysis techniques are recognized as useful tools in outlier detection through visual representations. One limitation of this direction is the lack of studies concerning the reliability of the visual interpretation. In this paper we propose a method that combines an Exploratory Data Analysis technique, Andrews curves, with a statistical approach which can be applied to automatically classify the data. Using a simulation study we show that the results provided by the Andrews curves approach are markedly superior to the estimates distance test (the best proposed method for detecting outliers revealed in the literature) for the crossover bioequivalence design.
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