Identification of tool wear using acoustic emission signal and machine learning methods

Twardowski, Paweł; Tabaszewski, Maciej; Pikuła, Martyna Wiciak –; Felusiak-Czyryca, Agata

doi:10.1016/j.precisioneng.2021.07.019

Cited by 78 publications

(32 citation statements)

References 23 publications

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“…The deformation, fracture and phase change of solid materials cause the rapid release of strain energy, and acoustic emission is the stress elastic wave. Thus, acoustic emission features with a higher amplitude can be monitored when the tool is broken [64][65][66]. Acoustic emission is not subject to mechanical interference and propagates much higher than the characteristic frequency caused by machining.…”

Section: Acoustic Emissionmentioning

confidence: 99%

Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Wang

Hou

et al. 2021

Applied Sciences

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Tool wear monitoring is of great significance for the development of manufacturing systems and intelligent manufacturing. Online tool condition monitoring is a crucial technology for cost reduction, quality improvement, and manufacturing intelligence in modern manufacturing. However, it remains a difficult problem to monitor the status of tools online, in real-time and accurately in the industry. In the research status of mainstream technology, the convolution neural network may be a good solution to this problem, based on the appropriate sensor system and correct signal processing methods. Therefore, this paper outlines the state-of-the-art systems encountered in the open access literature, focusing on information collection, feature selection–extraction technologies based on deep convolutional neural networks, and monitoring network architecture and modeling methods. Based on typical cases, this paper focuses on the application of the convolution neural network in tool wear monitoring. From the application results, it is feasible and reliable to apply convolution neural networks in tool wear monitoring. Additionally, it can improve the prediction accuracy, which is of great significance for the future development of technology. This paper can be a guide for the researchers and manufacturers in the area of tool wear monitoring for explaining the latest trends and requirements.

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Section: Acoustic Emissionmentioning

confidence: 99%

Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Wang

Hou

et al. 2021

Applied Sciences

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“…Researchers have used ML with the AE signals to predict the tool wear of aluminum ceramic composite with 10% SiC (Twardowski et al, 2021). The prediction error was seen to be less than 6%.…”

Section: Sensors In Cutting Tool Tribologymentioning

confidence: 99%

“…To understand the wear mechanism of cutting tool, various technologies like AI and ML have been used. Researchers have used ML with the AE signals to predict the tool wear of aluminum ceramic composite with 10% SiC (Twardowski et al , 2021). The prediction error was seen to be less than 6%.…”

Section: Sensors and Tribologymentioning

confidence: 99%

Sensors and tribological systems: applications for industry 4.0

Rouf

Raina

Haq

et al. 2021

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Purpose The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment. Design/methodology/approach A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included. Findings Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition. Originality/value The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

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“…More and more satisfactory results are noticeable due to artificial intelligence (AI) application, where the algorithms are related with "learning" nonlinear dependencies between input and output data. For example, one of the primary AI applications in machining is tool wear or surface roughness prediction based on phenomena occurring in machining, such as cutting forces, vibrations, or acoustic emission [4][5][6][7][8]. The correlation between those quantities with tool wear allows tool identification in real time and simultaneously eliminates optional downtimes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

et al. 2022

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The dynamic development of new technologies enables the optimal computer technique choice to improve the required quality in today’s manufacturing industries. One of the methods of improving the determining process is machine learning. This paper compares different intelligent system methods to identify the tool wear during the turning of gray cast-iron EN-GJL-250 using carbide cutting inserts. During these studies, the experimental investigation was conducted with three various cutting speeds vc (216, 314, and 433 m/min) and the exact value of depth of cut ap and federate f. Furthermore, based on the vibration acceleration signals, appropriate measures were developed that were correlated with the tool condition. In this work, machine learning methods were used to predict tool condition; therefore, two tool classes were proposed, namely usable and unsuitable, and tool corner wear VBc = 0.3 mm was assumed as a wear criterium. The diagnostic measures based on acceleration vibration signals were selected as input to the models. Additionally, the assessment of significant features in the division into usable and unsuitable class was caried out. Finally, this study evaluated chosen methods (classification and regression tree, induced fuzzy rules, and artificial neural network) and selected the most effective model.

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Identification of tool wear using acoustic emission signal and machine learning methods

Cited by 78 publications

References 23 publications

Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Sensors and tribological systems: applications for industry 4.0

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

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