Tool wear condition monitoring based on wavelet transform and improved extreme learning machine

Abstract

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.

Recent Advances on Machine Learning Applications in Machining Processes

“…The correct selection of input data determines the capability of the deployed model. Typical choices in machining are the measurements of the process forces [12,13], accelerations [14,15], vibrations or acoustic emission sensors [16,17] (2-3). Furthermore, the availability of the inner sensors or process parameters allow simple and reliable applications [18,19], including all the data available during the production.…”

Fault diagnosis of rolling bearing based on multimodal data fusion and deep belief network

“…Typical statistical analysis methods include wavelet transform, empirical model decomposition and principal component analysis. 1–3 These methods can make use of a small amount of vibration signal data to quickly get the characteristic indexes and comprehensive diagnosis results. However, these methods are usually effective for specific targets, and it is difficult to extract general features for multiple fault diagnosis under different working conditions.…”

Tool Wear Conditions Monitoring Based on Kernel Smoothing Density and Deep Learning

“…Machining process by material removal become a very hot point in the manufacturing industry and plays a significant role in ensuring production quality [1][2][3][4]. There is a growing need for rapid, direct and mass production of important products from super alloys in aerospace, automotive, biomedical and military applications.…”