The electrical current signal can be seen as a useful signal, disturbed by a mechanical noise signal or other measurement noises. In this case, noise reduction methods such as the Wiener filter can provide solutions to detect mechanical defects. It will be assumed that the electrical signals are decorated with the rotation of the rotor and therefore of any mechanical event related to the rotation. This can be verified in the case of an asynchronous machine. This property is important for estimating the Wiener filter whose basic assumption is that the useful signal must be decorated with noise.

The analysis of vibrations generated during conventional turning is one of the reliable means of determining the condition of sensitive components. In this paper, it is clearly presented the necessary steps to extract the indicators in order to be able to detect tool wear during the main three phases of use (running-in, stabilized wear, accelerated wear). It is clearly demonstrated that there is a relationship between the evolution of wear and the measured quantities (vibrations) during machining. To achieve this, we have carried out several measurement campaigns using metal carbide plate tools. Thus, the vibrations generated during the machining operations were recorded along a single axis on the machine tool using a single axial accelerometer positioned on the turret in the vertical direction, perpendicular to the cutting force. The processing of these signals in both the time and frequency domain has proven that vibrations can indeed be used to detect the level of wear.

In this work, it is a question of the cutting tool wear monitoring in mechanical turning. We did this monitoring in three phases which correspond to the life of our tool. To achieve this objective of improving monitoring, we have used a processing method (EMD) that breaks down a large signal into small signals (IMFs). The cut up or processed signals are yet applied in the temporal (RMS) and frequency (Spectrum) indicators in order to monitor the evolution of the tool in relation to its degradation and to check the reliability of the indicators. The obtained results will be optimized in an on-line monitoring system and incorporated into a microcontroller dealing with its three phases, in order to make the comparison of informations each time they are generated by the machine.

Currently with the evolution of manufacturing technology such as high speed machining additive manufacturing... where mechanical parts are mass produced in industrial production, and the workload of traditional manual control is heavy and imprecise, the control efficiency is low, a contactless control system based on image processing is presented. The control system hardware includes a light source, optical microscope and computer, the control system adopts the transmitted illumination to highlight the contour function of mechanical parts. The pretreatment is first done to the image captured by the Matlab software, then threshold segmentation and edge contour extraction are conducted; and finally, to improve geometric element detection accuracy, which is based on the Hough transformation algorithm. The results show the validity of the method and the feasibility of the algorithm in the system, it improves the efficiency of the measuring system and performs the measurement in line without contact of mechanical parts.

This work is based on the analysis of vibrations generated during the classical shooting. The main objective is to improve the surveillance of the cutting device usury during its three phases of life. This analysis aims to demonstrate if there is a relation between the evolution of the usury and the measured length(vibrations)during the machining. To succeed , we have made many great campaigns by using tools in platelet and the monobloc in metallic carbureted engine. Thus, generated vibrations during the machining operations have been registered, following two axes on the machine tool thanks to the mono axial accelerometer situated on the turret. The first is oriented following the vertical direction x (radial), the second following z(axial) and parallel to cut effort. These directions have been qualified of privileged directions . however signals have been treated via two methods ; temporal method based on statistic indicators (RMS, kurtosis, skewness, variance, variance, average) and frequential method. Furthermore,, we’ll measure the electrical power and we have visualized and measured the width of the usury thanks to an optic microscope. Finally, the proposed methods in this work have permitted to determine the vibrating level of the signal and pertinent indicators permitting a surveillance of the tool usury of the classical turning cut.