Arc welding used at automated workstations in large-scale production systems requires continuous assessment of welded joints quality. There are known classical methods and diagnostic systems based on the observation of welding current or arc voltage, while along with the development of deep learning methods, the interest in diagnostics by the use of images is increasing. The article presents results of research conducted for the process of joining two stainless steel materials (AISI 304 and AISI 316L) of various thicknesses by means of a fillet weld, aimed at developing a method of diagnosing the welding process using a convolutional neural network. Infrared images recorded using two thermovision cameras mounted on a test stand were used to diagnose the process. EWM Tetrix 351 welding machine operating in TIG technology was used as an executive element. Welds were made at different currents and arc welding voltages, as well as at different welding speeds, which had a direct impact on its quality. The solution for binary classification of welded joints (correct or incorrect) with accuracy above 98% was achieved.
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.
The paper presents the results of research aimed at developing a method for hard-to-machine metal alloy milling process diagnosis using computational intelligence methods. To diagnose the process, a signal from an accelerometer mounted on the spindle of a CNC machine was used. The data were recorded during milling of Inconel 625 alloy workpieces, performed by sharp and blunt cutters. The acceleration signal metrics, both in the time and frequency domains were used to develop the classifiers. Based on the experiments, it has been demonstrated that it is possible to effectively diagnose Inconel alloy workpieces milling process using shallow computational intelligence methods (decision trees, k-NN and linear support vector machines). Python was used for data processing and visualisation as well as classifiers development and testing.
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