Cutting Parameters and Material Classification Using Multinomial Logistic Regression
Keywords:Manufacturing, Monitoring, Acceleration, Sound, Temperature, Supervised Machine Learning
In the context of the new industrial revolution - Industry 4.0, the smart factory concept brought, to manufacturing, the idea of using large amounts of data acquired from a machining process and a set of mathematical techniques, discovering correlations, patterns, or trends in this database. Thus, machine tools are the focus of research in order to monitor and analyze the quality of the machining process based on data from embedded sensors. Based on this strategy, a signature process was created, that consists in capturing behavior patterns of a machine, such as machining conditions, machining quality, or tool wear. This article deal with a comparison between three Multinomial Logistic Regressions: the first using only time domain data, the second using only frequency domain data, and finally, the third using time and frequency domain data to identify the pattern of feed rate, depth of cut, and material being machined. It was observed that the methods had a precision of 96.25%, 37.92%, and 99.58%, respectively, showing that this methodology has great predictive efficiency and could be used to monitor the cutting parameters and material studied in this paper.
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