Review of tool condition monitoring methods in milling processes

Zhou, Yuqing; Wang, Xue

doi:10.1007/s00170-018-1768-5

Cited by 261 publications

(157 citation statements)

References 90 publications

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“…In the former, the actual value of a wear parameter such as the flank, the rake or the crater is directly quantified by optical devices, geometrical assessment or measuring electrical resistance, to name a few. Tool condition monitoring is a vast field of research that has been carried out over several decades, and a good number of publications have been selected and featured in [9,10], paving the way for the exploration of better monitoring models. An example of a direct tool wear monitoring approach using computer vision and ML techniques has been reported in [11].…”

Section: Tool Wear Monitoringmentioning

confidence: 99%

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Terrazas

Martínez-Arellano

Benardos

et al. 2018

JMMP

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The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%.

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Section: Tool Wear Monitoringmentioning

confidence: 99%

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Terrazas

Martínez-Arellano

Benardos

et al. 2018

JMMP

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“…[21]. Various real-time monitoring instruments exist, such as dynamometers, current sensors, vibrations sensors and acoustic emissions [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Among many methods of online tool wear monitoring[21][22][23], the cutting forces measurements have been taken into consideration. Experiments on machining performed to determine the durability of cutting tools for uniform cutting parameters: v c = 25 m/min; f = 100 m/min; a p = 1 mm and a e = 8 mm were analysed in detail taking into account the amplitude of the components of cutting force.…”

mentioning

confidence: 99%

Influence of machining parameters on the polymer concrete milling process

Kępczak

Zgórniak

Lajmert

et al. 2020

Int J Adv Manuf Technol

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The paper examines the wear of end mill cutters, the parameters of a workpiece's surface layer and the values of cutting forces during the machining of polymer concrete, which is one of the most difficult-to-cut materials. Increased abrasive wear of the active cutting edges is a result of the heterogeneous structure of this material, and it has a negative effect on the cutting process, the durability of the cutting tools as well as the roughness of the workpiece. It is very important to determine the machining parameters for this material from a tool durability and quality of machining point of view. The research was divided into two stages. In the first stage, the appropriate machining parameters were determined based on the measurement of the components of the cutting force. In the second stage, durability tests of four end mill cutters were carried out. During the tests, the component cutting forces, tool wear and surface roughness were measured. The highest tool durability was recorded for the monolithic cemented carbide cutter with coating NC Mill G9F42120N 4F. Tools made of high-speed steel should not be used for machining polymer concrete, as they exhibited the highest intensity of tool wear. The rapid loss of machinability of these tools leads to a rapid increase in cutting force and the roughness of the machined surface. From an economic point of view, cemented carbide end mill cutters with coatings seem to be the appropriate choice for machining polymer concrete.

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“…Tool wear monitoring is an important aspect in the PHM area. During the machining process, the tool wear occurs, which affects the quality of the machined surface and may lead to the risk of the machine tool damage [2]. Tool wear measuring methods can be categorized into direct and indirect methods.…”

Section: Introductionmentioning

confidence: 99%

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

Xie

Huang

et al. 2019

Symmetry

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To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios.

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Review of tool condition monitoring methods in milling processes

Cited by 261 publications

References 90 publications

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Influence of machining parameters on the polymer concrete milling process

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

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