Effects of the pipe-joints on acoustic emission wave propagation velocity

In jointed-pipes, a variety of different acoustic emission (AE) waves can be generated by way of mode conversion, wave reflection and wave transmission from a joint. This can lead to interference waves as resulting complicated signals propagate along the pipe structure. To find out how the joint affects AE wave propagation in the jointedpipes, experiments were conducted on a thin-walled copper pipes connected with two types of joint, compression and soldered. By using wavelet packet decomposition (WPD) analysis and the time-of-flight method, the apparent velocity of AE waves near the joints could be estimated as a narrow frequency band individually. Results confirmed that the wave velocities determined near a joint were influenced by not only the wave reflection but also the wave transmission. The measured wave velocity was least affected by those for the wave in the low frequency band (<125 kHz). It was also observed that a compression or soldered joint behaved like a low-pass filter to the transmitted AE signal

Expert system for tool wear monitoring in blanking

A description is given of a simple yet powerful expert system created using the CRYSTAL shell which is able to monitor the potential and functional failures of the tool and the monitoring equipment. The techniques of feature extraction, selection and classification using the Bayesian rule are presented. Finally supervised learning, necessary when new situations are encountered, is also discussed

Acoustic emission and vibration for tool wear monitoring in single-point

This paper proposes an implementation of calibrated acoustic emission (AE) and vibration techniques to monitor progressive stages of flank wear on carbide tool tips. Three cutting conditions were used on workpiece material, type EN24T, in turning operation. The root-mean-square value of AE (AErms) and the coherence function between the acceleration signals at the tool tip in the tangential and feed directions was studied. Three features were identified to be sensitive to tool wear: AErms, coherence function in the frequency ranges 2.5-5.5 kHz and 18-25 kHz. Belief network based on Bayes’ rule was used to integrate information in order to recognise the occurrence of worn tool. The three features obtained from the three cutting conditions and machine time were used to train the network. The set of feature vectors for worn tools was divided into two equal sub-sets: one to train the network and the other to test it. The AErms in term of AE pressure equivalent was used to train and test the net work to validate the calibrated acoustic. The overall success rate of the network in detecting a worn tool was high with low error rate

Overhung-boring bars: The performance of undamped and damped bars under static and dynamic conditions when machining metals

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The work of the author was to investigate the static, dynamic and machining behaviour of some new designs of slug damped boring bars with a 10 to 1 overhang ratio. The bars were mounted on a centre lathe. The static behaviour of a boring bar in relation to the geometric form errors that might be produced during boring was studied both analytically and experimentally. Specifically, two types of errors were considered, namely, a) errors that arise on entry of the boring tool into the workpiece) known as the "bell-mouth" errors; and b) reproducibility of eccentricity errors, known as the "copying" errors. The theory for "bell-mouth" errors did not seem to fit the results well; however, the theory did prove that such errors could exist. The theory for “copying” errors agreed remarkably well with the results provided that the initial eccentricity was small compared with the depth of cut. The dynamical behaviour of the slug damped boring bar was modelled by a mathematical analogue. Despite its inability to properly account for the compressibility effect of the gaseous damping fluid, the model revealed the possibility of design improvements. In consequence, the optimally-tuned slug-damped tungsten-bunged bar was conceived, Manufactured and tested along with a solid bar for comparison purposes, a slug-damped recessed bar and a slug-damped steel-bunged bar. The machining behaviour of a boring bar was studied in terms of the maximum depth of cut that it could cope before the occurrence of chatter. At first, a stability model was developed based on the mathematical analogue formulated in the study of the dynamical behaviour. But since this analogue did not fit the results accurately, a second and more precise model was set up using the frequency response obtained from dynamic experiments instead. The concept of negative damping coefficient was used; and a one-to-one correspondence between the asymptotic value of the negative damping coefficient and the limiting depth of cut was found to exist. By virtue of this, it is in principle possible to predict the limiting depth of cut of any machine tool system whose frequency response characteristics are known. Compared with other bars tested, the optimally-tuned tungsten-bunged bar was found to have the best dynamic and machining characteristics as reflected in the limiting depth of cut of 0.10511 (2.67 mm) to 0.110" (2.79 mm) at the feed of 0.0065"/rev (0.165 mm/rev) and the speed of 500 rprn on a 3.5" dia. bore (140 m/min) of EN8 steel. By constrast, the solid bar was hardly able to cut stably even at the light cut of 0.005".Science Research Counci

Effects of the size of the measured surface on the performance of an air cone-jet sensor for in-process inspection

This paper investigates the effects of the size of the measured surface on the performance of an air-jet sensor using 2-D finite element method. The modeling and experimental results have shown that in the measuring range of 1.5 mm to 4.5 mm with a nozzle of diameter of 6 mm, the output of the cone-jet is not significantly affected by the size change from 10 mm to 14 mm. It also proved that this particular sensor is not suitable for measuring an object with a size less than 9 mm

Continuous wavelet transform and neural network for condition monitoring of rotodynamic machinery

This paper describes a novel method of rotodynamic machine condition monitoring using a wavelet transform and a neural network. A continuous wavelet transform is applied to the signals collected from accelerometer. The transformed images are then extracted as unique characteristic features relating to the various types of machine conditions. In the experiment, four types of machine operating conditions have been investigated: a balanced shaft; an unbalanced shaft, a misaligned shaft and a defective bearing. The back propagation neural network (BPNN) is used as a tool to evaluate the performance of the proposed method. The experimental results result in a recognition rate of 90 percent