Detection process approach of tool wear in high speed milling

Kious, Mecheri; Ouahabi, Abdeldjalil; Boudraa, Malika; Serra, Roger; Cheknane, Ali

doi:10.1016/j.measurement.2010.08.014

Cited by 71 publications

(31 citation statements)

References 16 publications

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“…Due to use different db wavelets, we can get different effects. And db4 wavelet relative to other wavelets has the shortest time window, and better time resolution, so we use db4 wavelet to denoise signals [15] . Because the selection and quantification of threshold is directly related to the quality of signal denoising, the paper selects the default threshold to denoise signals.…”

Section: Tool Wear State Recognitionmentioning

confidence: 99%

Research on Cutting Tool Wear Based on Fractional Brownian Motion

Liang¹

2015

IJMEA

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Abstract:Cutting tool wear is a very complex process. Various factors have a direct or indirect effect on cutting tool wear, resulting in uncertainty, so it is difficult for experimental data and result to have good stability. However, Vibration analysis is a very important means for condition monitoring and fault diagnosis. This paper aims to study the methods of tool vibration signal processing, pattern recognition and trend prediction. Collected on tool vibration signal at different times, wavelet noise reduction is used to pretreat the vibration signals. Then, for the self-similar vibration signals, we propose the fractional Brownian motion (FBM) theory with long-range dependence (LRD). Combined with Wigner-Ville spectrum, characteristic parameter can be extracted, so the cutting tool wear state can be determined according to fractal dimension and average slope of the fitting curve of the logarithm power spectrum. Finally, we use FBM model to predict the trend of tool vibration signals. Experiments show that the methods have a good effect on tool wear state recognition and trend prediction.

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Section: Tool Wear State Recognitionmentioning

confidence: 99%

Research on Cutting Tool Wear Based on Fractional Brownian Motion

Liang¹

2015

IJMEA

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“…Many authors have studied the strong correlation that exists between cutting forces and tool wear [11,12]. Most of the researchers have used flank wear to establish this correlation [13,14].…”

Section: Proposed Model For Tool Wear Considerationmentioning

confidence: 99%

Estimation of Cutting Forces and Tool Wear Using Modified Mechanistic Models in High Performance Turning

Fernández-Abia

Barreiro

Lacalle³

et al. 2014

Materials Forming, Machining and Tribology

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In this chapter a model for prediction of cutting forces when turning austenitic stainless steels following the approach of mechanistic models is presented. Mechanistic models, also called semi-empiric, use empiric laws based on the tool geometry and coefficients obtained by experimentation. These coefficients implicitly pick up characteristic data about part and tool materials or tool geometry. Therefore, a series of machining tests is required to calculate these coefficients for each pair tool-workpart, commonly known as characterization tests. This chapter is structured as follows. In Sect. 3.1 a model for prediction of cutting forces without considering the effect of tool wear is presented. This model allows to estimate with reasonable precision the cutting forces. This model can be used, for example, in the scope of machine-tools and fixtures design or optimization of cutting tool geometry. In Sect. 3.2 this model is extended to include the effect of tool wear. So, the model can be used in the scope of monitoring techniques. Both models were developed for turning AISI 303 austenitic stainless steels at high-speed cutting conditions.

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“…5 & 6. Using such statistical parameters as mean, Root Mean Square and variance, it can be observed that the variance values provided more relevant information on the evolution of the milling cutter wear than the values of other parameters [5], [6]. World The variance evolution (Fig.…”

Section: A Temporal Analysismentioning

confidence: 99%

Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

Kious

Aissa

Ameur

et al. 2014

AMM

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Abstract-The wear of cutting tool degrades the quality of the product in the manufacturing processes. The on line monitoring of the cutting tool wear level is very necessary to prevent the deterioration of the quality of machining. Unfortunately there is not a direct manner to measure the cutting tool wear on line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission etc…. In this work, a neural network system is elaborated in order to estimate the flank wear from the cutting force measurement and the cutting conditions.

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Detection process approach of tool wear in high speed milling

Cited by 71 publications

References 16 publications

Research on Cutting Tool Wear Based on Fractional Brownian Motion

Research on Cutting Tool Wear Based on Fractional Brownian Motion

Estimation of Cutting Forces and Tool Wear Using Modified Mechanistic Models in High Performance Turning

Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

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