In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availability and the productivity. Consequently, developing a robust approach for monitoring tool wear condition is needed to get accurate product dimensions with high quality surface and reduced stopping time of machines. Prognostics and health management has become one of the most challenging aspects for monitoring the wear condition of cutting tools. This study focuses on the evaluation of the current health condition of cutting tools and the prediction of its remaining useful life. Indeed, the proposed method consists of the integration of complex continuous wavelet transform (CCWT) and improved extreme learning machine (IELM). In the proposed IELM, the hidden layer output matrix is given by inverting the Moore–Penrose generalized inverse. After the decomposition of the acoustic emission signals using CCWT, the nodes energy of coefficients have been taken as relevant features which are then used as inputs in IELM. The principal idea is that a non-linear regression in a feature space of high dimension is involved by the extreme learning machine to map the input data via a non-linear function for generating the degradation model. Then, the health indicator is obtained through the exploitation of the derived model which is in turn used to estimate the remaining useful life. The method was carried out on data of the real world collected during various cuts of a computer numerical controlled tool.
Non-destructive evaluation of applied loadings level in mechanical components and structures constitutes a versatile tool to predict the behaviour of their materials in engineering industries and check services. Ultrasonic non-destructive techniques stand advantageously to allow predictions and diagnosis where other measurement methods are difficult or not possible. For analysing the effects of static uniaxial loads, the present work relates to the modification of waveforms of ultrasonic shear waves propagating in statically loaded materials. To predict the behaviour of waveforms of shear ultrasonic waves propagating in media under stress, an analytical method is proposed. The simulated waveforms are reconstructed by using parameters from an experimental signal measured on specimens free of any loading. Assuming the hypothesis of progressive ultrasonic plane waves and taking into account the elastic deformation and the stressed ultrasonic velocity, the time of flight calculation allowed the simulation of shear waveforms for each applied stress. To validate this method, some ultrasonic measurements have been carried out. A linearly polarised shear wave transducer is used for probing C35 steel and 2017A (AU4G) alloy samples of known macroscopic properties. Simulated and measured ultrasonic waveforms are partially in good agreement, in particular for the extra delay effect induced under compressive and tensile elastic loads.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.