W ramach realizacji zadania RID „Zastosowanie metod AI, VR i IoE w diagnostyce, nadzorowaniu i sterowaniu procesów w Przemyśle 4.0” powstała publikacja wygłoszona podczas konferencji International Conference of Computational Methods in Engineering Science – CMES’19, Kazimierz Dolny 21-23.11.2019. Artykuł został opublikowany w materiałach IOP Conference Series: Materials Science and Engineering, Volume 710, Number 1, indeksowanych w bazie SCOPUS (https://www.scopus.com/sourceid/19700200831)
T.Żabiński, G. Piecuch, R. Żyła, S. Prucnal, Milling process diagnosis using Computational Intelligence Methods, CMES 19, Kazimierz Dolny 21-23.11.2019, IOP Conference Series: Materials Science and Engineering, Volume 710, Number 1, https://iopscience.iop.org/article/10.1088/1757-899X/710/1/012043
Abstract
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.
