DYNA JOURNAL ENGINEERING

Key words:

microtaladrado, monitorización de estado de la herramienta, extracción de rasgos, transformada rápida de Fourier, transformada de Hilbert Huangmicrodrilling, tool condition monitoring, feature extraction, fast Fourier transform, hilbert-Huang transform

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

ABSTRACT:
Introduction. The paper presents a study on the signals of a microdrilling process in order to extract features that can be correlated with the cutting tool condition, providing a base for further developments of indirect cutting tool monitoring systems. Materials and methods. In a microdrilling process of a tungsten-cooper alloy, with TiAlN-coated tools, signals of forces and vibrations were measured online. Three different tools with diameters of 0.1, 0.5 and 1.0 mm, respectively, were used and five consecutive holes were elaborated with each of them. In each hole were measured not only the before mentioned signals but also the temperature and dimensional variations of the cutting tool. Results. Measured signals were processed by using time-domain statistics, Fourier fast transform and Hilbert-Huang transform for extracting features. Correlation analyses between the obtained features and the number of elaborated holes were then carried out, in order to identify which of these features can be used for estimating the tool condition. Discussion. Time-domain statistics features did not show perceptible correlation with the tool usage level. On the contrary, fast Fourier transform and Hilbert-Huang transform offered very interesting outcomes, because some of the analyzed featured showed a relationship with the number of elaborated holes. Nevertheless, the results cannot be considered conclusive due to the small number of experiments and, therefore, the research must be continued. In this sense, it is proposed, as a future development of this work, the integration of the different signals by using artificial intelligence techniques such as neural networks or neuro-fuzzy systems.