RT Conference Proceedings
T1 Experimental methodology for the integrated online monitoring in the dry machining of light alloys
A1 Herrera-Fernández, Manuel José
A1 Trujillo-Vilches, Francisco Javier
A1 Martín-Béjar, Sergio
A1 Bermudo-Gamboa, Carolina
A1 Sevilla-Hurtado, Lorenzo
K1 Sistemas expertos
AB Machining is one of the most used manufacturing processes for components in the aeronautical andautomotive industry. Usually, these parts have very demanding quality requirements. One of the mainproblems in improving the performance of these processes is the large number of variables involved,such as the cutting forces, cutting temperature, tool wear, chattering or power consumption, amongothers, as well as the synergy between them. The control and supervision of the cutting process areimportant aspects to consider in improving the machining performance. Monitoring can be carried outboth offline and online. However, the current trend in the industry is carrying out online monitoring,which reduces assembly and disassembly times and makes the decision process faster.Nevertheless, there are difficulties involved in the monitoring of various signals simultaneously,obtained through different devices, regarding their synchronization and integration. This step is crucial,in order to make a correct interpretation of the process evolution in real time and to make decisionsabout the parameters involved in it. The aim is to achieve an early reaction, through corrective actions,minimizing costs and unnecessary time.Hence, in this work, an experimental methodology has been designed and developed to facilitate thecapture, interpretation and joint analysis of several machining output signals. This methodology hasfocused on dry machining of light alloys for aeronautical use. To do this, various turning tests havebeen carried, collecting and analyzing the signals from several devices (dynamometer, thermographiccamera, laser vibrometer, among others). In addition, this methodology can be applied to collect dataand feed an expert system, based on machine learning, that allow predicting the behavior of severaloutput variables based on the values of the cutting parameters applied (cutting speed, feed and depth ofcut).
YR 2023
FD 2023
LK https://hdl.handle.net/10630/27342
UL https://hdl.handle.net/10630/27342
LA eng
NO Campus de Excelencia Internacional Andalucía Tech.AIRBUS
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026