Investigations on the effects of tool wear on chip formation mechanism and chip morphology using acoustic emission signal in the microendmilling of aluminum alloy

2014 ◽  
Vol 77 (5-8) ◽  
pp. 1499-1511 ◽  
Author(s):  
M. Prakash ◽  
M. Kanthababu ◽  
K. P. Rajurkar
Keyword(s):  
Acoustic Emission ◽  
Aluminum Alloy ◽  
Tool Wear ◽  
Formation Mechanism ◽  
Acoustic Emission Signal ◽  
Chip Formation ◽  
Chip Morphology ◽  
Emission Signal ◽  
Chip Formation Mechanism ◽  
On Chip

Research on Chip Formation Mechanism of Laser-Assisted Machining of Fused Silica Based on Variable Laser Angle

2020 ◽  
Author(s):  
Pengfei Pan ◽  
Huawei Song ◽  
Junfeng Xiao ◽  
Zuohui Yang ◽  
Guoqi Ren ◽  
...  
Keyword(s):  
Formation Mechanism ◽  
Chip Formation ◽  
Incident Angle ◽  
Chip Morphology ◽  
Fused Silica ◽  
Machining Process ◽  
Laser Assisted Machining ◽  
Chip Formation Mechanism ◽  
On Chip ◽  
Cutting Model

Abstract Laser-assisted machining (LAM) is a promising technology for improving the machinability of hard-to-cut materials. In this study, based on the finite element method (FEM), a cutting model of thermally coupled non-uniform temperature field is established. The chip formation mechanism of fused silica during the laser-assisted machining process is explored from the aspects of laser power and laser incident angle. The results show that as the laser incident angle increases, the continuity of the chip increases gradually. An annular tool holder that can adjust the angle between the laser beam and the tool was designed. And the similar chip morphology obtained by variable-angle cutting experiments verified the effectiveness of the cutting model. Moreover, fracture chips and continuous banded chips are found in both simulation and experiment, which implies that the cutting mechanism works under a hybrid mode of brittle fracture and plastic deformation in the LAM process.

Burr formation and correlation with cutting force and acoustic emission signals

2016 ◽  
Vol 231 (3) ◽  
pp. 399-414 ◽  
Author(s):  
Seyed Ali Niknam ◽  
Victor Songmene
Keyword(s):  
Acoustic Emission ◽  
Cutting Force ◽  
Formation Mechanism ◽  
Acoustic Emission Signal ◽  
Cutting Forces ◽  
Burr Formation ◽  
Slot Milling ◽  
Emission Signal ◽  
Burr Size ◽  
Milling Operation

The principle objective of this work is to present a methodology to evaluate the correlation between burr size attributes (thickness and height) and information computed from acoustic emission and cutting forces signals. In the proposed methodology, cutting force and acoustic emission signals were recorded in each cutting test, and each recorded original acoustic emission signal was segmented into two sections that correspond to steady-state cutting process (cutting signal) and cutting tool exit from the work part (exit signal). The dominant acoustic emission signal parameters including AEmax and AErms were computed from each segmented acoustic emission signal. The maximum values of directional cutting forces (FX, FY and FZ) were also measured in each trial. The experimental verification was conducted on slot milling operation which has relatively more complicated burr formation mechanism than that in many other traditional machining operations. Among slot milling burrs, the top-up milling side burrs and exit burrs along up milling side were largest and thickest burrs which were studied in this work. To evaluate the correlation between signal information and burr size, the computed signal information (5 parameters) and their interaction effects (10 parameters) were used to construct the input parameters of the multiple regression fitted models. Statistical methods were then used to assess the adequacy of individual input parameters and signal information. Using the acoustic emission and cutting force signals information in the input layer of multiple regression models, a high correlation was observed between the predicted and observed values of burr size. It was exhibited that due to complex burr formation mechanism in milling operation and strong interaction effects between cutting process parameters, no systematic relationship can be formulated between the milling burrs.

The influence of the cooling conditions on the cutting tool wear and the chip formation mechanism

2016 ◽  
Vol 24 ◽  
pp. 107-115 ◽  
Author(s):  
Radoslaw W. Maruda ◽  
Grzegorz M. Krolczyk ◽  
Piotr Nieslony ◽  
Szymon Wojciechowski ◽  
Mariusz Michalski ◽  
...  
Keyword(s):  
Tool Wear ◽  
Formation Mechanism ◽  
Chip Formation ◽  
Cutting Tool ◽  
Cutting Tool Wear ◽  
Cooling Conditions ◽  
Chip Formation Mechanism

Identification Method of Tool Wear Based on Locally Linear Embedding and Support Vector Machine

2012 ◽  
Vol 246-247 ◽  
pp. 1289-1293
Author(s):  
Zheng Qiang Li ◽  
Peng Nie ◽  
Shu Guo Zhao
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
Support Vector ◽  
Emission Signal ◽  
Identification Method ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Data Points ◽  
Linear Embedding

Aiming at the nonlinear characteristics of the tool wear Acoustic Emission signal, tool wear state identification method is proposed based on local linear embedding and vector machine supported. The local linear embedding algorithm makes high dimensional information down to low dimension feature space through commutation, and thus to compress the data for highlighting signal features. This algorithm well compensates for the weakness of linear dimension reduction failing to find datasets nonlinear structure. In this paper, acoustic emission signal is firstly made by phase space reconstruction. Using local linear embedding method, the high dimension space mapping data points are reflected into low-dimensional space corresponding data points, then extracting tool wear state characteristics, and using vector machine supported classifier to identify classification of the tool wear conditions. Experimental results show that this method is used for the exact recognition of the tool wear state, and has widespread tendency.

Wavelet Packet analyses of Acoustic Emission Signal for Tool Wear in High Speed Milling

2013 ◽  
Vol 589-590 ◽  
pp. 600-605
Author(s):  
Shun Xing Wu ◽  
Peng Nan Li ◽  
Zhi Hui Yan ◽  
Li Na Zhang ◽  
Xin Yi Qiu ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
High Speed ◽  
Feature Vector ◽  
Wavelet Packet ◽  
High Speed Milling ◽  
Band Energy ◽  
Emission Signal ◽  
Frequency Band Energy

Tool wear condition monitoring technology is one of the main parts of advanced manufacturing technology and is a hot research direction in recent years. A method based on the characteristics of acoustic emission signal and the advantages of wavelet packets decomposition theory in the non-stationary signal feature extraction is proposed for tool wear state monitoring with monitor the change of acoustic emission signal feature vector. In this paper, through the method, firstly, acoustic emission signal were decomposed into 4 layers with wavelet packet analysis, secondly, the frequency band energy of the have been decomposed signal were extracted, thirdly, the frequency band energy that are sensitive to tool wear were selected as feature vector, and then the corresponding relation between feature vector and tool wear was established , finally, the state of the tool wear can be distinguished according to the change of feature vector. The results show that this method can be feasibility used to monitor tool wear state in high speed milling.

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