Force based tool wear monitoring system for milling process based on relevance vector machine

Wang, Guofeng; Yang, Yinping; Xie, Qinglu; Zhang, Yanchao

doi:10.1016/j.advengsoft.2014.02.002

Cited by 56 publications

(26 citation statements)

References 20 publications

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“…The relative sharp tools at the initial stage of machining under dry cutting conditions produce low cutting forces [34] that lead to the more moderate R Z index. With the increasing number of cutting process, the rounded tool edge generated higher friction coefficient and widen the contact area among the tool/chip and tool/workpiece interface [29,35,36]. Since the friction force increased drastically, will yield a significant increment in the cutting force components.…”

Section: Z-rotation Methodsmentioning

confidence: 99%

Cutting tool wear progression index via signal element variance

Kasim

Nuawi

Ghani

et al. 2019

JMES

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This paper presents a new statistical-based method of cutting tool wear progression in a milling process called Z-rotation method in association with tool wear progression. The method is a kurtosis-based that calculates the signal element variance from its mean as a measurement index. The measurement index can be implicated to determine the severity of wear. The study was conducted to strengthen the shortage in past studies notably considering signal feature extraction for the disintegration of non-deterministic signals. The Cutting force and vibration signals were measured as a tool of sensing element to study wear on the cutting tool edge at the discrete machining conditions. The monitored flank wear progression by the value of the RZ index, which then outlined in the model data pattern concerning wear and number of samples. Throughout the experimental studies, the index shows a significant degree of nonlinearity that appears in the measured impact. For that reason, the accretion of force components by Z-rotation method has successfully determined the abnormality existed in the signal data for both force and vibration. It corresponds to the number of cutting specifies a strong correlation over wear evolution with the highest correlation coefficient of R2 = 0.8702 and the average value of R2 = 0.8147. The index is more sensitive towards the end of the wear stage compared to the previous methods. Thus, it can be utilised to be the alternative experimental findings for monitoring tool wear progression by using threshold values on certain cutting condition.

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Section: Z-rotation Methodsmentioning

confidence: 99%

Cutting tool wear progression index via signal element variance

Kasim

Nuawi

Ghani

et al. 2019

JMES

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“…However, it still requires more effort in milling, where tools and material removal processes are more complex. A tool condition machine (SVM) [30] and others [31][32][33]. The special network structure makes the machine learning model also able to achieve ideal classification accuracy when the data set is small, but they are not suitable for data samples with big size.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient

et al. 2019

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Tool wear and breakage are inevitable due to the severe stress and high temperature in the cutting zone. A highly reliable tool condition monitoring system is necessary to increase productivity and quality, reduce tool costs and equipment downtime. Although many studies have been conducted, most of them focused on single-step process or continuous cutting. In this paper, a high robust milling tool wear monitoring methodology based on 2-D convolutional neural network (CNN) and derived wavelet frames (DWFs) is presented. The frequency band of high signal-to-noise ratio is extracted via derived wavelet frames, and the spectrum is further folded into a 2-D matrix to train 2-D CNN. The feature extraction ability of the 2-D CNN is fully utilized, bypassing the complex and low-portability feature engineering. The full life test of the end mill was carried out with S45C steel work piece and multiple sets of cutting conditions. The recognition accuracy of the proposed methodology reaches 98.5%, and the performance of 1-D CNN as well as the beneficial effects of the DWFs are verified.

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“…Approaches to tool wear monitoring proposed so far fall into two broad categories: indirect and direct methods. Indirect monitoring methods estimate the wear by measuring variables such as cutting forces [2,3,4], vibration [5,6] or acoustic emission [7], that are somehow correlated to tool wear stages. However, the relationship between tool wear and the observed variables depends on the cutting conditions which, in general, are not known in advance.…”

Section: Introductionmentioning

confidence: 99%

A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques

García-Ordás

Alegre

González-Castro

et al. 2016

Int J Adv Manuf Technol

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In this paper we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORCHIZ, a novel shape based descriptor computed from the wear region image. A new Insert dataset with 212 images of tool wear has been created to evaluate our approach. It contains two subsets: one with images of the main cutting edge, and the other one with the edges that converge to it (called Insert-C and Insert-I, respectively). The experiments were conducted trying to discriminate between two (Low-High) and three (Low-Medium-High) different wear levels and the classification stage was carried out using a Support Vector Machine (SVM). Results show that B-ORCHIZ outperforms other shape descriptors (aZIBO and ZMEG) achieving accuracy values between 80.24% and 88.46% in the different scenarios evaluated. Moreover, a hierarchical cluster analysis was performed, offering prototype images for wear levels, which may help researchers and technicians to understand how the wear process evolves. These results show a very promising opportunity for wear monitoring automation in edge profile milling processes.

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Force based tool wear monitoring system for milling process based on relevance vector machine

Cited by 56 publications

References 20 publications

Cutting tool wear progression index via signal element variance

Cutting tool wear progression index via signal element variance

An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient

A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques

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