Qualité de surface et émissions acoustiques en fraisage haute vitesse des alliages d'aluminium

Abstract

Aluminum alloys are widely used in aerospace because of their rigidity and low weight. For better performance in service, parts made of this material must have a good surface finish. This work shows the behavior of the acoustic signal during machining and the prediction of surface quality using the machining parameters and the vision, then to improve productivity in milling. The acoustic emission resulting from the cut varies depending on machining parameters. An appropriate choice of cutting conditions should satisfy the criteria of quality, productivity while respecting the environment and health of operators. Using data recorded experimentally obtained from the signals of acoustic emission shows how the temporal change of the different characteristics of acoustic emission can be used to inspect the stability of the system. Statistical techniques are applied to the effective value RMS (Root Mean Square) of the acoustic signal recorded during the evolution of the tool. The parameters may allow us to monitor the sensitivity of the signal from the machining process are sought to enable the monitoring process. The results over time statistical properties of amplitude and corresponding frequency were presented. A linear relationship between the RMS value of acoustic emission and machining parameters (cutting parameters, type of coating) was established to predict the intensity of sound produced during machining. We studied, among others, the influence of cutting parameters on the quality of the surface of aluminum alloys. An experimental design was used to arrive at empirical models for predicting the roughness and the RMS signal noise. The results obtained are very satisfactory because they show that we can inspect, from the vision quality surface finish in high speed milling. Also from the acoustic emission can control the machining process by optimizing the noise and by inspecting the system stability.

Date	8 Apr 2010
Original language	French
Awarding Institution	École de technologie supérieure
Supervisor	Victor Songmene (Supervisor) & Jean-Pierre Kenné (Co-supervisor)