A First Step towards a Tribological Approach to Investigate Cutting Tool Wear

2014 ◽  
Vol 611-612 ◽  
pp. 452-459 ◽  
Author(s):  
Giovenco Axel ◽  
Frédéric Valiorgue ◽  
Cédric Courbon ◽  
Joël Rech ◽  
Ugo Masciantonio
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
304L Stainless Steel ◽  
Fe Modelling ◽  
Wear Model ◽  
Cutting Tool Wear ◽  
The Will ◽  
Preliminary Study ◽  
The Impact ◽  
Macroscopic Parameters

The present work is motivated by the will to improve Finite Element (FE) Modelling of cutting tool wear. As a first step, the characterisation of wear mechanisms and identification of a wear model appear to be fundamental. The key idea of this work consists in using a dedicated tribometer, able to simulate relevant tribological conditions encountered in cutting (pressure, velocity). The tribometer can be used to estimate the evolution of wear versus time for various tribological conditions (pressure, velocity, temperature). Based on this design of experiments, it becomes possible to identify analytically a wear model. As a preliminary study this paper will be focused on the impact of sliding speed at the contact interface between 304L stainless steel and tungsten carbide (WC) coated with titanium nitride (TiN) pin. This experiment enables to observe a modification of wear phenomena between sliding speeds of 60 m/min and 180 m/min. Finally, the impact on macroscopic parameters has been observed.

Cutting tool wear model for tunnel boring machine tunneling in heterogeneous grounds

2021 ◽  
Author(s):  
Zhiyong Yang ◽  
Zhengyang Sun ◽  
Kuanda Fang ◽  
Yusheng Jiang ◽  
Hongji Gao ◽  
...  
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Tunnel Boring Machine ◽  
Wear Model ◽  
Cutting Tool Wear ◽  
Tunnel Boring

scholarly journals Study on the Impact of Cutting Tool Wear on Machine Tool Energy Consumption

2020 ◽  
Vol 14 (3) ◽  
pp. 158-164
Author(s):  
Andrzej Roszkowski ◽  
Paweł Piórkowski ◽  
Wacław Skoczyński ◽  
Wojciech Borkowski ◽  
Tomasz Jankowski
Keyword(s):  
Energy Consumption ◽  
Tool Wear ◽  
Machine Tool ◽  
Cutting Tool ◽  
Cutting Tool Wear ◽  
The Impact

THE EFFECT OF CUTTING TOOL WEAR ON THE RATE OF CORROSION WEAR OF A MACHINED MATERIAL

Tribologia ◽  
2020 ◽  
Vol 289 (1) ◽  
pp. 5-11
Author(s):  
Vyacheslav F. BEZJAZYCHNYI ◽  
Vladislav V. PLESKUN
Keyword(s):  
Surface Layer ◽  
Tool Wear ◽  
Atmospheric Corrosion ◽  
Cutting Tool ◽  
Work Piece ◽  
Roughness Parameters ◽  
Cutting Tool Wear ◽  
Tool Cutting ◽  
The Impact ◽  
Edge Tool

A possible variant for calculated estimation of the degree of the impact of the cutting tool wear on the value of the part’s surface layer wear obtained during processing with the edge tool, due to atmospheric corrosion, is presented. The feature is the evaluation of the wear rate and its numerical value depending on the tool wear, roughness parameters of the work piece surface, and the degree of cold hardening of the surface layer, as well as parameters of the technological machining conditions (cutting conditions, geometry of the tool cutting part, properties of the machined and the tool materials).

Tool Wear Detection Using Time Series Analysis of Acoustic Emission

1989 ◽  
Vol 111 (3) ◽  
pp. 199-205 ◽  
Author(s):  
S. Y. Liang ◽  
D. A. Dornfeld
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Time Series Analysis ◽  
Cutting Tool ◽  
Shear Zones ◽  
Parameter Vector ◽  
Model Parameter ◽  
Cutting Tool Wear ◽  
Series Analysis

This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.

Cutting tool wear in the machining of hardened steels

Wear ◽  
2001 ◽  
Vol 247 (2) ◽  
pp. 152-160 ◽  
Author(s):  
J Barry ◽  
G Byrne
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Hardened Steels ◽  
Cutting Tool Wear

scholarly journals A Hybrid Remaining Useful Life Prediction Method for Cutting Tool Considering the Wear State

2022 ◽  
Author(s):  
Yifan Li ◽  
Yongyong Xiang ◽  
Baisong Pan ◽  
Luojie Shi
Keyword(s):  
Tool Wear ◽  
High Speed ◽  
Cutting Tool ◽  
Bayesian Framework ◽  
Remaining Useful Life ◽  
Physical Models ◽  
Data Driven ◽  
Support Vector ◽  
Wear Model ◽  
Useful Life

Abstract Accurate cutting tool remaining useful life (RUL) prediction is of significance to guarantee the cutting quality and minimize the production cost. Recently, physics-based and data-driven methods have been widely used in the tool RUL prediction. The physics-based approaches may not accurately describe the time-varying wear process due to a lack of knowledge for underlying physics and simplifications involved in physical models, while the data-driven methods may be easily affected by the quantity and quality of data. To overcome the drawbacks of these two approaches, a hybrid prognostics framework considering tool wear state is developed to achieve an accurate prediction. Firstly, the mapping relationship between the sensor signal and tool wear is established by support vector regression (SVR). Then, the tool wear statuses are recognized by support vector machine (SVM) and the results are put into a Bayesian framework as prior information. Thirdly, based on the constructed Bayesian framework, parameters of the tool wear model are updated iteratively by the sliding time window and particle filter algorithm. Finally, the tool wear state space and RUL can be predicted accordingly using the updating tool wear model. The validity of the proposed method is demonstrated by a high-speed machine tool experiment. The results show that the presented approach can effectively reduce the uncertainty of tool wear state estimation and improve the accuracy of RUL prediction.

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