Time â frequency domain analysis of acoustic emission sginal for milling process - Mohd Shafawi, Che Ibrahim (2012)
This project is to investigate using the time-frequency domain analysis acoustic emission (AE) signal for the milling process. The objective of this project is to study the properties of acoustic emission signal during the machining process at different surface quality using the time frequency localization method. This thesis describes the pattern of graph that being shown from different of machining parameter. Several steps being followed to ensure that the experiment will run. First step to run the experiment is to design of experiments. Before running the experiment, the materials be facing to get parallel surface and several parameters of machining being chosen to get the variations of surface roughness. The materials will clamp at the table of the milling machine and the sensor will be placed on the materials. To remove the gaps between the sensor and the workpiece, the function of grease be used. To check whether the AE system will detect the signal, pencil break test will be done. This action can produce AE signals that like as the experiment will be run. For the cutting tools, the carbide coated cutting tools being used and for the materials using Haynes 188 as the experimental material. To get the variation of surface roughness, parameter that being selected before being used. Different parameters give different AE signal and the surface roughness also varies. When all the data collected, it can be used in manufacturing field. And the result that obtained is for the smooth the range of the Ra is about from 0.270 Î¼m until 0.384 Î¼m. And for the medium surface quality, it from 1.370 Î¼m to 2.058Î¼m. For the rough surface, the Ra is about 6.033 to 7.042 Î¼m. In STFT windows, the graph looks to their pattern of the colour of the graph. From the colour at the graph, colour that more dominant shows the high value of amplitude. STFT windows are most suitable to view of the times, frequency and the amplitude of the signal and that show it the most suitable method to analyze the condition monitoring of machining.