Tool wear mechanisms in machining

Olortegui-Yume, Jorge A.; Kwon, Patrick

doi:10.1504/ijmmm.2007.015469

Cited by 17 publications

(9 citation statements)

References 25 publications

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“…As pointed in the literature [19], tool wear roughly follows a certain pattern, from initial wear through normal wear to severe wear. In the normal wear stage, the rough surface of the cutting edge is worn off, the working pressure becomes even, and the tool wear is relatively slow.…”

Section: Data Preprocessing and System Trainingmentioning

confidence: 83%

Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network

2023

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In order to guarantee machining quality and production efficiency and as well as to reduce downtime and cost, it is essential to receive information about the cutting tool condition and change it in time if necessary. To this end, tool condition monitoring systems are of great significance. Based on the data fusion technology of multi-sensor integration and the learning ability of deep residual network, a tool wear monitoring system is proposed in this paper. For data acquisition, multiple sensors are used to collect vibration, noise and acoustic emission signals in the machining process. For signal processing, the data fusion method of angular summation of matrices is used, so that the multi-source data are fused into two-dimensional pictures. Then, the feature extraction module of the proposed system uses the pictures as input and takes advantage of the deep residual convolutional network in extracting the deep features from the pictures, and finally the identification module completes the recognition of the tool wear type. To verify its feasibility, a testing bed is built on a CNC milling machine, and multi-sensor data are collected during the machining process of a simple workpiece with one of the two selected cutting tools each time. The proposed system is trained using the collected data and then is used to monitor the wear condition of a new cutting tool by collecting real-time data under similar condition. The results show that the accuracy of wear state recognition is as high as 90%, which verifies the feasibility of the proposed tool wear monitoring system.

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Section: Data Preprocessing and System Trainingmentioning

confidence: 83%

Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network

2023

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“…From the perspective of the tool wear mechanism [60] and the wear curve (figure 10), the rough surface of the tool is worn away in the normal wear stage, so the working pressure on the tool's surface is reduced and becomes uniform. Therefore, normal wear becomes slower, which is the effective working stage of the tool.…”

Section: Optimization Experimentsmentioning

confidence: 99%

A novel approach to tool condition monitoring based on multi-sensor data fusion imaging and an attention mechanism

Zeng

Liu

2021

Meas. Sci. Technol.

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Tool wear does great damage to product quality and machining efficiency. In this paper, a new tool condition monitoring (TCM) method based on a multi-sensor data fusion imaging and attention mechanism is proposed to indirectly measure and monitor tool wear. First, the multi-sensor signals collected during the operation of the machine tool are encoded and fused. A novel triangular matrix of angle summation method is proposed to fuse multi-sensor time-series signals at the data layer and to image them as two-dimensional images. The method used here effectively retains the detailed characteristics of the data, and also effectively retains the internal time relationship of the signal. In addition, a deep residual network with a convolution block attention module is used to extract the deep features from the encoded image and identify the wear stage of the tool. The proposed deep-learning network is then applied to TCM. It selects information in the channel and spatial domains based on its attention mechanism, and increases network depth by combining residual blocks to realize deep feature extraction. Compared with traditional machine learning methods, this end-to-end deep learning model does not rely on feature engineering, which requires a lot of expert knowledge and artificial experience. In our experimental study, a public data set was used to train the model, and the results showed that the accuracy of the model proposed in this paper was as high as 94%. To further verify its feasibility and effectiveness, the proposed model was also compared with other methods.

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“…titanium alloys, hardened steel) and cutting conditions is the value lower than 1. The cutting ratio λ L is a physical index of machinability identifying the material's susceptibility to plastic deformations in specific cutting conditions [1,22].…”

Section: Investigation Of Parameters At the Workpiece/tool Interface mentioning

confidence: 99%

Investigation of contact phenomena in turning using tools made of low-alloy high-speed steels

Jaworski

Trzepieciński

2017

Teh. vjesn.

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INVESTIGATION OF CONTACT PHENOMENA IN TURNING USING TOOLS MADE OF LOW-ALLOY HIGH-SPEED STEEL Jan Jaworski, Tomasz TrzepiecińskiOriginal scientific paper In this paper, the results of investigations of the temperature field distribution and tool wearing during C60 steel turning are presented. Abrasive wear resistance tests were carried out for cutters made of low-alloy high-speed steel. Steel samples were heat treated, and then were subjected to ion nitriding, TiN coating, or to complex treatment. The influence of cutting speed on the value of basic parameters characterising the phenomena in the tool-workpiece interface i.e., cutting ratio, friction coefficient, and length of contact zone, is analysed. An increase of the rake angle value causes an increase in the value of the friction coefficient. Furthermore, the value of the friction coefficient decreases as the thickness of the cut layer increases. Investigations of the durability of cutting edges confirm that to define the working conditions of the tool, resistance against hardness loss with increasing temperature at temperatures higher and lower than the tempering temperature is the most significant. Keywords: cut layer; cutting ratio; high-speed steels; turning; wear; wear resistance Ispitivanje fenomena dodira kod tokarenja primjenom alata od niskolegiranog brzoreznog čelikaIzvorni znanstveni članak U ovom se radu daju rezultati ispitivanja raspodjele temperaturnog polja i trošenja alata tijekom tokarenja čelika C60. Provedena su ispitivanja otpornosti na abrazivno trošenje tokarskih noževa izrađenih od niskolegiranog brzoreznog čelika. Uzorci čelika su toplinski obrađeni i zatim podvrgnuti ionskom nitriranju, oblaganju TiN ili kompleksnoj obradi. Analiziran je utjecaj brzine rezanja na vrijednost osnovnih parametara karakterističnih za pojave u graničnoj površini alat-obradak, t.j. rezni omjer, faktor trenja i dužina zone dodira. Porast vrijednosti kuta nagiba dovodi do porasta vrijednosti faktora trenja. Nadalje, vrijednost faktora trenja se smanjuje kako raste debljina reznog sloja. Ispitivanja trajnosti rezne oštrice potvrđuju da je u definiranju radnih uvjeta alata, najvažnija otpornost na gubitak tvrdoće kod porasta temperature na temperaturama višim i nižim od temperature popuštanja.

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Tool wear mechanisms in machining

Cited by 17 publications

References 25 publications

Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network

Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network

A novel approach to tool condition monitoring based on multi-sensor data fusion imaging and an attention mechanism

Investigation of contact phenomena in turning using tools made of low-alloy high-speed steels

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