Intelligent recognition of milling tool wear status based on variational auto-encoder and extreme learning machine

2022 ◽  
Author(s):  
Bo Liu ◽  
Hongkun Li ◽  
Jiayu Ou ◽  
Zhaodong Wang ◽  
Wei Sun
Keyword(s):  
Tool Wear ◽  
Extreme Learning Machine ◽  
Milling Tool ◽  
Learning Machine ◽  
Intelligent Recognition

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

2019 ◽  
Vol 234 (5) ◽  
pp. 1057-1068 ◽  
Author(s):  
Soufiane Laddada ◽  
Med. Ouali Si-Chaib ◽  
Tarak Benkedjouh ◽  
Redouane Drai
Keyword(s):  
Wavelet Transform ◽  
Tool Wear ◽  
Extreme Learning Machine ◽  
Cutting Tools ◽  
Remaining Useful Life ◽  
Machining Process ◽  
Non Linear ◽  
Useful Life ◽  
Learning Machine ◽  
Wear Condition

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.

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