Diagnosing Extrusion Process Based on Displacement Signal and Simple Decision Tree Classifier

Abstract

The article presents an extensive analysis of the literature related to the diagnosis of the extrusion process and proposes a new, unique method. This method is based on the observation of the punch displacement signal in relation to the die, and then approximation of this signal using a polynomial. It is difficult to find in the literature even an attempt to solve the problem of diagnosing the extrusion process by means of a simple distance measurement. The dominant feature is the use of strain gauges, force sensors or even accelerometers. However, the authors managed to use the displacement signal, and it was considered a key element of the method presented in the article. The aim of the authors was to propose an effective method, simple to implement and not requiring high computing power, with the possibility of acting and making decisions in real time. At the input of the classifier, authors provided the determined polynomial coefficients and the SSE (Sum of Squared Errors) value. Based on the SSE values only, the decision tree algorithm performed anomaly detection with an accuracy of 98.36%. With regard to the duration of the experiment (single extrusion process), the decision was made after 0.44 s, which is on average 26.7% of the extrusion experiment duration. The article describes in detail the method and the results achieved.

Comparison of the Performance of Machine Learning Techniques in the Prediction of Employee

“…A popular ANN is the Multilayer Perceptron (MLP) and it is made up of neurons termed perceptron [32]. MLP is mathematically represented in Equation 4 [33,34,35].…”

“…T P + T N T P + F P + F N + T N (7) The statistic used to assess a model's capacity to forecast true negatives in each accessible category is called specificity, which is expressed mathematically in Equation 8 [35].…”