Multisensory based tool wear monitoring for practical applications in milling of titanium alloy

Niu, Boya; Sun, Jie; Yang, Bin

doi:10.1016/j.matpr.2019.12.126

Cited by 31 publications

(12 citation statements)

References 24 publications

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“…A multi-sensor approach, in which the TCM system considers monitoring several process and machine parameters, is preferable to increase the TCM accuracy and reliability [ 59 ]. This has been reflected in the progressive growth of the number of studies focusing on equipping the TCM systems with multi sensors for milling operations [ 1 , 60 , 61 , 62 , 63 , 64 ]. Apart from the externally mounted sensors, modern CNC machines allow real-time data acquisition from their internal sensors and control system, such as spindle speed, feedrate, and spindle motor power feedback that can be used in TCM systems [ 65 ].…”

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

“…When designing a multi-signal TCM system, the acquired data are fused at either the raw signal, feature, or model levels [ 70 ], as shown in Figure 2 . Fusing the acquired signals at the feature level is used in most TCM research, where different features from multiple signals are selected and employed in the tool wear prediction model [ 25 , 64 ]. By fusing the data at the model level, two or more tool wear classifiers are merged to generate a more confident decision using a voting function [ 71 ].…”

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

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Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

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Section: Sensing and Data Acquisitionmentioning

confidence: 99%

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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“…Therefore, estimation models for tool wear and surface roughness should be developed. Recently, various estimation approaches have been proposed, e.g., artificial neural networks [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ], regression [ 9 , 11 , 12 , 13 , 14 , 15 , 16 ], support vector machines [ 17 , 18 , 19 , 20 ], response surface methodology [ 21 , 22 ], random forest [ 18 , 23 , 24 ], and adaptive network-based fuzzy inference systems [ 25 , 26 , 27 ]. In general, the chosen model and data directly affect estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion

Huang

Lee

2021

Sensors

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This paper proposes an estimation approach for tool wear and surface roughness using deep learning and sensor fusion. The one-dimensional convolutional neural network (1D-CNN) is utilized as the estimation model with X- and Y-coordinate vibration signals and sound signal fusion using sensor influence analysis. First, machining experiments with computer numerical control (CNC) parameters are designed using a uniform experimental design (UED) method to guarantee the variety of collected data. The vibration, sound, and spindle current signals are collected and labeled according to the machining parameters. To speed up the degree of tool wear, an accelerated experiment is designed, and the corresponding tool wear and surface roughness are measured. An influential sensor selection analysis is proposed to preserve the estimation accuracy and to minimize the number of sensors. After sensor selection analysis, the sensor signals with better estimation capability are selected and combined using the sensor fusion method. The proposed estimation system combined with sensor selection analysis performs well in terms of accuracy and computational effort. Finally, the proposed approach is applied for on-line monitoring of tool wear with an alarm, which demonstrates the effectiveness of our approach.

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“…However, such characteristics as poor thermal conductivity, strong chemical activity at high temperatures and low elastic modulus, cause widespread problems in machining, such as low machining efficiency, poor surface quality, and severe tool wear, etc. [4][5][6]. These problems restrict badly the application and promotion of titanium alloy materials.…”

Section: Introductionmentioning

confidence: 99%

Research on cutting performance in high-speed milling of TC11 titanium alloy using self-propelled rotary milling cutters

Yujiang

Chen

2021

Int J Adv Manuf Technol

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Titanium alloy materials, with excellent chemical and physical properties, are widely applied to the manufacture of key components in the aerospace industry. Nevertheless, its hard-to-machine characteristic causes various problems in the machining process, such as severe tool wear, difficulty to ensure good surface quality, etc. To achieve high efficiency and quality of machining titanium alloy materials, this paper conducted an experimental research on the high-speed milling of TC11 titanium alloy with self-propelled rotary milling cutters. In the work, the wear mechanism of self-propelled rotary milling cutters was explored, the influence of milling velocity was analyzed on the cutting process, and the variation laws were obtained of milling forces, chip morphology and machined surface quality with the milling length. The results showed that in the early and middle stages of milling, the insert coating peeled off evenly under the joint action of abrasive and adhesive wear mechanisms. As the milling length increased, the dense notches occurred on the cutting edge of the cutter, the wear mechanism converted gradually into fatigue wear, and furthermore coating started peeling off the cutting edge with the occurrence of thermal fatigue cracks on the insert. As the milling length was further extended, the milling forces tended to intensify, the chip deformation worsened, and the obvious cracks occurred at the bottom of chips. Moreover, the rise in milling velocity reduced the tool wear resistance, increased obviously the milling forces and the surface roughness.

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Multisensory based tool wear monitoring for practical applications in milling of titanium alloy

Cited by 31 publications

References 24 publications

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion

Research on cutting performance in high-speed milling of TC11 titanium alloy using self-propelled rotary milling cutters

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