DYNA JOURNAL ENGINEERING

Key words:

Rugosidad, emisiones acústicas, aprendizaje-automático, taladrado, Roughness, acoustic-emission, machine-learning, drilling

Article type:

COLABORACION/COLLABORATION DAMR

Section:

COLLABORATIONS

Within the framework of industry 4.0, the aim is to develop collaborative human-machine environments in order to achieve greater adaptability to the variability of cutting processes by making efficient use of available resources. For this, the use of the existing information in the data obtained from the cutting processes is fundamental. The control and visualization of scientific parameters (acoustic emissions, cutting powers, vibrations, shear forces...) related to industrial parameters (tool wear, roughness, microstructure...) in drilling processes is of great importance. Drilling processes are carried out in the final stages of the production of a part, which often results in a critical operation. Using an experimental setup, where both internal signals and the acoustic emissions signal are acquired and through the use of automatic learning algorithms, a qualitative estimate of the quality of the hole made is performed. Given the demands of sectors in which it is necessary to check the roughness of the machined surface and taking into account the requirements to be met by manufacturing companies, obtaining an indicator of the state of the machined surface is an advantage in terms of decision-making.

Keywords: Roughness, acoustic-emission, machine-learning, drilling