Review Open Access

A State-of-the-art Review on the Intelligent Tool Holders in Machining

Intelligent and Sustainable Manufacturing. 2024, 1(1), 10002; https://doi.org/10.35534/ism.2024.10002
Qinglong An 1, *    Jie Yang 1,    Junli Li 2,    Gang Liu 2,    Ming Chen 1,    Changhe Li 3,   
1
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2
School of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
3
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
*
Authors to whom correspondence should be addressed.

Received: 19 Nov 2023    Accepted: 19 Dec 2023    Published: 29 Dec 2023   

Abstract

In the manufacturing process, in addition to the properties of material itself, the quality of a product is directly related to the cutting process. Cutting force and cutting heat are two crucial factors in cutting processing. Researchers can analyze various signals during cutting process, such as cutting force signal, vibration signal, temperature signal, etc., which can regulate force and temperature, optimize the cutting process, and improve product quality. Therefore, it is very important to pay attention to various signals in cutting process. Meanwhile, good-quality signal data sets will greatly reduce time, resource and labor costs for subsequent use or analysis of researchers. Therefore, how to collect high-quality signals effectively and accurately is the first step. At present, researchers prefer to use various sensors to collect signals. With the advancement of science and technology, intelligent tool holder appears in researchers’ vision. It integrates multiple systems such as sensors, data collection, data transmission, and power supply on the tool holder. It replaces traditional wired sensors, and it is highly interactive with CNC machine tools. This paper will carry out a systematic review and prospect from three aspects: the structural design of the intelligent tool holder, the signal monitoring technology of the intelligent tool holder, and the tool condition monitoring of the intelligent tool holder.

References

1.
Liao Z, la Monaca A, Murray J, Speidel A, Ushmaev D, Clare A, et al. Surface integrity in metal machining-Part I: Fundamentals of surface characteristics and formation mechanisms.  Int. J. Mach. Tools Manuf. 2021, 162, 103687. [Google Scholar]
2.
La Monaca A, Murray JW, Liao Z, Speidel A, Robles-Linares JA, Axinte DA, et al. Surface integrity in metal machining-Part II: Functional performance.  Int. J. Mach. Tools Manuf. 2021, 164, 103718. [Google Scholar]
3.
Guzeev VI, Pimenov DY. Cutting force in face milling with tool wear.  Russ. Eng. Res. 2011, 31, 989–993. [Google Scholar]
4.
Zhu L, Liu C. Recent progress of chatter prediction, detection and suppression in milling.  Mech. Syst. Sig. Process. 2020, 143, 106840. [Google Scholar]
5.
Pimenov DY. Experimental research of face mill wear effect to flat surface roughness.  J. Frict. Wear 2014, 35, 250–254. [Google Scholar]
6.
Zhu K, San Wong Y, Hong GS. Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results.  Int. J. Mach. Tools Manuf. 2009, 49, 537–553. [Google Scholar]
7.
Cai G, Chen X, Li B, Chen B, He Z. Operation reliability assessment for cutting tools by applying a proportional covariate model to condition monitoring information.  Sensors 2012, 12, 12964–12987. [Google Scholar]
8.
Dou J, Jiao S, Xu C, Luo F, Tang L, Xu X. Unsupervised online prediction of tool wear values using force model coefficients in milling.  Int. J. Adv. Manuf. Technol. 2020, 109, 1153–1166. [Google Scholar]
9.
Zhu K. Big data oriented intelligent tool condition monitoring system. In Intelligent Machining Systems: Modelling, Monitoring and Informatics; Springer International Publishing: Cham, Switzerland, 2021; pp. 361–381.
10.
Li X, Liu X, Yue C, Liang SY, Wang L. Systematic review on tool breakage monitoring techniques in machining operations.  Int. J. Mach. Tools Manuf. 2022, 176, 103882. [Google Scholar]
11.
Wang SM, Yu HJ, Liu SH, Chen DF. An on-machine and vision-based depth-error measurement method for micro machine tools.  Int. J. Precis. Eng. Manuf. 2011, 12, 1071–1077. [Google Scholar]
12.
Kong D, Chen Y, Li N, Duan C, Lu L, Chen D. Relevance vector machine for tool wear prediction.  Mech. Syst. Sig. Process. 2019, 127, 573–594. [Google Scholar]
13.
Stavropoulos P, Papacharalampopoulos A, Vasiliadis E, Chryssolouris G. Tool wear predictability estimation in milling based on multi-sensorial data.  Int. J. Adv. Manuf. Technol. 2016, 82, 509–521. [Google Scholar]
14.
Sun H, Zhang J, Mo R, Zhang X. In-process tool condition forecasting based on a deep learning method.  Robot. Comput. Integr. Manuf. 2020, 64, 101924. [Google Scholar]
15.
Yu X, Lin X, Dai Y, Zhu K. Image edge detection based tool condition monitoring with morphological component analysis.  ISA Transact. 2017, 69, 315–322. [Google Scholar]
16.
Zhu K, Yu X. The monitoring of micro milling tool wear conditions by wear area estimation.  Mech. Syst. Sig. Process. 2017, 93, 80–91. [Google Scholar]
17.
Boing D, Castro FL, Schroeter RB. Prediction of PCBN tool life in hard turning process based on the three-dimensional tool wear parameter.  Int. J. Adv. Manuf. Technol. 2020, 106, 779–790. [Google Scholar]
18.
Dutta S, Pal SK, Mukhopadhyay S, Sen R. Application of digital image processing in tool condition monitoring: A review.  CIRP J. Manuf. Sci. Technol. 2013, 6, 212–232. [Google Scholar]
19.
Kuljanic E, Sortino M, Totis G. Multisensor approaches for chatter detection in milling.  J. Sound Vibr. 2008, 312, 672–693. [Google Scholar]
20.
Abellan-Nebot JV, Romero Subirón F.  A review of machining monitoring systems based on artificial intelligence process models.  Int. J. Adv. Manuf. Technol. 2010, 47, 237–257. [Google Scholar]
21.
Bhattacharyya P, Sengupta D, Mukhopadhyay S. Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques.  Mech. Syst. Sig. Process. 2007, 21, 2665–2683. [Google Scholar]
22.
Jamshidi M, Rimpault X, Balazinski M, Chatelain JF. Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining.  Int. J. Adv. Manuf. Technol. 2020, 106, 3859–3868. [Google Scholar]
23.
Wang G, Yang Y, Li Z. Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.  Sensors 2014, 14, 21588–21602. [Google Scholar]
24.
Jun CH, Suh SH. Statistical tool breakage detection schemes based on vibration signals in NC milling.  Int. J. Mach. Tools Manuf. 1999, 39, 1733–1746. [Google Scholar]
25.
Lei Z, Zhu Q, Zhou Y, Sun B, Sun W, Pan X. A GAPSO-enhanced extreme learning machine method for tool wear estimation in milling processes based on vibration signals.  Int. J. Precis. Eng. Manuf. Green Technol. 2021, 8, 745–759. [Google Scholar]
26.
26. Fu Y, Zhang Y, Gao H, Mao T, Zhou H, Sun R, et al. Automatic feature constructing from vibration signals for machining state monitoring.  J. Intell. Manuf. 2019, 30, 995–1008. [Google Scholar]
27.
Bhuiyan MSH, Choudhury IA, Dahari M, Nukman Y, Dawal SZ. Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring.  Measurement 2016, 92, 208–217. [Google Scholar]
28.
Ren Q, Balazinski M, Baron L, Jemielniak K, Botez R, Achiche S. Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling.  Inf. Sci. 2014, 255, 121–134. [Google Scholar]
29.
Ren Q, Baron L, Balazinski M, Botez R, Bigras P. Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission.  Appl. Soft Comput. 2015, 31, 14–24. [Google Scholar]
30.
Kothuru A, Nooka SP, Liu R. Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling.  Int. J. Adv. Manuf. Technol. 2018, 95, 3797–3808. [Google Scholar]
31.
Li X. Detection of tool flute breakage in end milling using feed-motor current signatures.  IEEE/ASME Trans. Mechatron. 2001, 6, 491–498. [Google Scholar]
32.
Zhou Y, Sun W. Tool wear condition monitoring in milling process based on current sensors.  IEEE Access 2020, 8, 95491–95502. [Google Scholar]
33.
Altintas Y, Yellowley I, Tlusty J. The detection of tool breakage in milling operations.  J. Eng. Ind. 1988, 110, 271–277. [Google Scholar]
34.
Pan T, Zhang J, Yang L, Zhao W, Zhang H, Lu B. Tool breakage monitoring based on the feature fusion of spindle acceleration signal.  Int. J. Adv. Manuf. Technol. 2021, 117, 2973–2986. [Google Scholar]
35.
Shao H, Shi X, Li L. Power signal separation in milling process based on wavelet transform and independent component analysis.  Int. J. Mach. Tools Manuf. 2011, 51, 701–710. [Google Scholar]
36.
Franco-Gasca LA, Herrera-Ruiz G, Peniche-Vera R, de Jesús Romero-Troncoso R, Leal-Tafolla W. Sensorless tool failure monitoring system for drilling machines.  Int. J. Mach. Tools Manuf. 2006, 46, 381–386. [Google Scholar]
37.
Peng Y. Empirical model decomposition based time-frequency analysis for the effective detection of tool breakage.  J. Manuf. Sci. Eng. 2006, 128, 154–166. [Google Scholar]
38.
Baek DK, Ko TJ, Kim HS. Real time monitoring of tool breakage in a milling operation using a digital signal processor.  J. Mater. Process. Technol. 2000, 100, 266–272. [Google Scholar]
39.
Hsueh YW, Yang CY. Tool breakage diagnosis in face milling by support vector machine.  J. Mater. Process. Technol. 2009, 209, 145–152. [Google Scholar]
40.
Brito LC, da Silva MB, Duarte MAV. Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data.  J. Intell. Manuf. 2021, 32, 127–140. [Google Scholar]
41.
Kannatey-Asibu E, Yum J, Kim TH. Monitoring tool wear using classifier fusion.  Mech. Syst. Sig. Process. 2017, 85, 651–661. [Google Scholar]
42.
Li G, Wang Y, He J, Hao Q, Yang H, Wei J. Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM.  Int. J. Adv. Manuf. Technol. 2020, 110, 511–522. [Google Scholar]
43.
Painuli S, Elangovan M, Sugumaran V. Tool condition monitoring using K-star algorithm.  Expert Syst. Appl. 2014, 41, 2638–2643. [Google Scholar]
44.
He Z, Shi T, Xuan J, Li T. Research on tool wear prediction based on temperature signals and deep learning.  Wear 2021, 478, 203902. [Google Scholar]
45.
Li X, Liu X, Yue C, Liu S, Zhang B, Li R, et al. A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion.  Measurement 2021, 185, 110072. [Google Scholar]
46.
Wang J, Li Y, Zhao R, Gao RX. Physics guided neural network for machining tool wear prediction.  J. Manuf. Syst. 2020, 57, 298–310. [Google Scholar]
47.
Wang J, Yan J, Li C, Gao RX, Zhao R. Deep heterogeneous GRU model for predictive analytics in intelligent manufacturing: Application to tool wear prediction.  Comput. Ind. 2019, 111, 1–14. [Google Scholar]
48.
Xu W, Miao H, Zhao Z, Liu J, Sun C, Yan R. Multi-scale convolutional gated recurrent unit networks for tool wear prediction in intelligent manufacturing.  Chin. J. Mech. Eng. 2021, 34, 53. [Google Scholar]
49.
An Q, Tao Z, Xu X, El Mansori M, Chen M. A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network.  Measurement 2020, 154, 107461. [Google Scholar]
50.
Liu M, Yao X, Zhang J, Chen W, Jing X, Wang K. Multi-sensor data fusion for remaining useful life prediction of machining tools by IABC-BPNN in dry milling operations.  Sensors 2020, 20, 4657. [Google Scholar]
51.
Wu J, Su Y, Cheng Y, Shao X, Deng C, Liu C. Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system. Appl. Soft Comput. 2018, 68, 13–23. [Google Scholar]
52.
Zhou JT, Zhao X, Gao J. Tool remaining useful life prediction method based on LSTM under variable working conditions.  Int. J. Adv. Manuf. Technol. 2019, 104, 4715–4726. [Google Scholar]
53.
Byrne G, Dornfeld D, Inasaki I, Ketteler G, König W, Teti R. Tool condition monitoring (TCM)—the status of research and industrial application.  CIRP Ann. 1995, 44, 541–567. [Google Scholar]
54.
Kurada S, Bradley C.  A review of machine vision sensors for tool condition monitoring.  Comput. Ind. 1997, 34, 55–72. [Google Scholar]
55.
Teti R, Jemielniak K, O’Donnell G, Dornfeld D. Advanced monitoring of machining operations.  CIRP Ann. 2010, 59, 717–739. [Google Scholar]
56.
Teti R, Mourtzis D, D’Addona DM, Caggiano A. Process monitoring of machining.  CIRP Ann. 2022, 71, 529–552. [Google Scholar]
57.
Sick B. On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mech. Syst. Sig. Process. 2002, 16, 487–546. [Google Scholar]
58.
Wong SY, Chuah JH, Yap HJ. Technical data-driven tool condition monitoring challenges for CNC milling: a review.  Int. J. Adv. Manuf. Technol. 2020, 107, 4837–4857. [Google Scholar]
59.
Sayyad S, Kumar S, Bongale A, Kamat P, Patil S, Kotecha K. Data-driven remaining useful life estimation for milling process: sensors, algorithms, datasets, and future directions.  IEEE Access 2021, 9, 110255–110286. [Google Scholar]
60.
Zhu D, Zhang X, Ding H. Tool wear characteristics in machining of nickel-based superalloys.  Int. J. Mach. Tools Manuf. 2013, 64, 60–77. [Google Scholar]
61.
Sun S, Brandt M, Dargusch MS. Characteristics of cutting forces and chip formation in machining of titanium alloys.  Int. J. Mach. Tools Manuf. 2009, 49, 561–568. [Google Scholar]
62.
Ozturk E, Ozkirimli O, Gibbons T, Saibi M, Turner S. Prediction of effect of helix angle on cutting force coefficients for design of new tools.  CIRP Ann. 2016, 65, 125–128. [Google Scholar]
63.
Albrecht A, Park SS, Altintas Y, Pritschow G. High frequency bandwidth cutting force measurement in milling using capacitance displacement sensors.  Int. J. Mach. Tools Manuf. 2005, 45, 993–1008. [Google Scholar]
64.
Jun MB, Ozdoganlar OB, DeVor RE, Kapoor SG, Kirchheim A, Schaffner G. Evaluation of a spindle-based force sensor for monitoring and fault diagnosis of machining operations. Int. J. Mach. Tools Manuf. 2002, 42, 741–751. [Google Scholar]
65.
Zhao Y, Zhao Y, Liang S, Zhou G.  A high performance sensor for triaxial cutting force measurement in turning.  Sensors 2015, 15, 7969–7984. [Google Scholar]
66.
Shaw MC, Cookson JO. Metal Cutting Principles; Oxford University Press: New York, NY, USA, 2005.
67.
Rizal M, Ghani JA, Nuawi MZ, Haron CHC. Development and testing of an integrated rotating dynamometer on tool holder for milling process.  Mech. Syst. Sig. Process. 2015, 52, 559–576. [Google Scholar]
68.
Xiao C, Ding H, Cheng K, Chen S. Design of an innovative intelligent turning tool with application to real-time cutting force measurement.  Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2015, 229, 563–568. [Google Scholar]
69.
Totis G, Sortino M.  Development of a modular dynamometer for triaxial cutting force measurement in turning.  Int. J. Mach. Tools Manuf. 2011, 51, 34–42. [Google Scholar]
70.
Kim JH, Chang HK, Han DC, Jang DY, Oh SI. Cutting force estimation by measuring spindle displacement in milling process.  CIRP Ann. 2005, 54, 67–70. [Google Scholar]
71.
Ma L, Melkote SN, Castle JB. PVDF sensor-based monitoring of milling torque. Int. J. Adv. Manuf. Technol. 2014, 70, 1603–1614. [Google Scholar]
72.
Stoney R, O’Donnell GE, Geraghty D. Dynamic wireless passive strain measurement in CNC turning using surface acoustic wave sensors. Int. J. Adv. Manuf. Technol. 2013, 69, 1421–1430. [Google Scholar]
73.
Gierlak P, Burghardt A, Szybicki D, Szuster M, Muszyńska M. On-line manipulator tool condition monitoring based on vibration analysis.  Mech. Syst. Sig. Process. 2017, 89, 14–26. [Google Scholar]
74.
Plaza EG, López PN. Surface roughness monitoring by singular spectrum analysis of vibration signals. Mech. Syst. Sig. Process. 2017, 84, 516–530. [Google Scholar]
75.
Chen Y, Li H, Hou L, Wang J, Bu X. An intelligent chatter detection method based on EEMD and feature selection with multi-channel vibration signals. Measurement 2018, 127, 356–365. [Google Scholar]
76.
Liu H, Tang S, He S, Li B, Mao X, Peng F. A method of measuring tool tip vibration in turning operations. Int. J. Adv. Manuf. Technol. 2016, 85, 1325–1337. [Google Scholar]
77.
Chung TK, Yeh PC, Lee H, Lin CM, Tseng CY, Lo WT, et al. An attachable electromagnetic energy harvester driven wireless sensing system demonstrating milling-processes and cutter-wear/breakage-condition monitoring. Sensors 2016, 16, 269. [Google Scholar]
78.
Xie Z, Li J, Lu Y. An integrated wireless vibration sensing tool holder for milling tool condition monitoring. Int. J. Adv. Manuf. Technol. 2018, 95, 2885–2896. [Google Scholar]
79.
Bleicher F, Schörghofer P, Habersohn C. In-process control with a sensory tool holder to avoid chatter. J. Mach. Eng. 2018, 18, 16–27. [Google Scholar]
80.
Guo K, Zhao Y, Zan Z, Sun J. Development and testing of a wireless rotating triaxial vibration measuring tool holder system for milling process.  Measurement 2020, 163, 108034. [Google Scholar]
81.
Zhang P, Gao D, Lu Y, Ma Z, Wang X, Song X. Cutting tool wear monitoring based on a smart toolholder with embedded force and vibration sensors and an improved residual network.  Measurement 2022, 199, 111520. [Google Scholar]
82.
Basti A, Obikawa T, Shinozuka J. Tools with built-in thin film thermocouple sensors for monitoring cutting temperature. Int. J. Mach. Tools Manuf. 2007, 47, 793–798. [Google Scholar]
83.
Cui Y, Zhang B, Ding W, Yan C, Liu Y. Research on the cutting tool with intelligent transient temperature measuring system. J. Mech. Eng. 2017, 53, 174–180. [Google Scholar]
84.
Huang S, Tao B, Li J, Fan Y, Yin Z. Estimation of the time and space-dependent heat flux distribution at the tool-chip interface during turning using an inverse method and thin film thermocouples measurement.  Int. J. Adv. Manuf. Technol. 2018, 99, 1531–1543. [Google Scholar]
85.
Wright PK, Dornfeld D, Hillaire RG, Ota NK. A Wireless Sensor for Tool Temperature Measurement and Its Integration within a Manufacturing System; Laboratory for Manufacturing and Sustainability, UC Berkeley: Berkeley, CA, USA, 2006.
86.
Kerrigan K, Thil J, Hewison R, O’Donnell GE. An integrated telemetric thermocouple sensor for process monitoring of CFRP milling operations.  Procedia CIRP 2012, 1, 449–454. [Google Scholar]
87.
Yaldız S, Ünsaçar F, Sağlam H, Işık H. Design, development and testing of a four-component milling dynamometer for the measurement of cutting force and torque.  Mech. Syst. Sig. Process. 2007, 21, 1499–1511. [Google Scholar]
88.
Yaldız S, Ünsaçar F. A dynamometer design for measurement the cutting forces on turning.  Measurement 2006, 39, 80–89. [Google Scholar]
89.
Suprock CA, Nichols JS. A low cost wireless high bandwidth transmitter for sensor-integrated metal cutting tools and process monitoring.  Int. J. Mechatron. Manuf. Syst. 2009, 2, 441–454. [Google Scholar]
90.
Rizal M, Ghani JA, Nuawi MZ, Haron CHC. An embedded multi-sensor system on the rotating dynamometer for real-time condition monitoring in milling. Int. J. Adv. Manuf. Technol. 2018, 95, 811–823. [Google Scholar]
91.
Zhao Y, Zhao Y, Wang C, Liang S, Cheng R, Qin Y, et al. Design and development of a cutting force sensor based on semi-conductive strain gauge. Sensors Actuators A Phys. 2016, 237, 119–127. [Google Scholar]
92.
Zhao Y, Xiaohui GE, Zhao Y. Research on high precision dynamic cutting force self-perception intelligent tool.  J. Mech. Eng. 2019, 55, 178–185. [Google Scholar]
93.
Qin Y, Zhao Y, Li Y, Zhao Y, Wang P. A high performance torque sensor for milling based on a piezoresistive MEMS strain gauge. Sensors 2016, 16, 513. [Google Scholar]
94.
Qin Y, Zhao Y, Li Y, Zhao Y, Wang P. A novel dynamometer for monitoring milling process.  Int. J. Adv. Manuf. Technol. 2017, 92, 2535–2543. [Google Scholar]
95.
Zhang P, Gao D, Lu Y, Wang F, Liao Z. A novel intelligent toolholder with embedded force sensors for milling operations. Mech. Syst. Sig. Process. 2022, 175, 109130. [Google Scholar]
96.
Chen YL, Chen F, Li Z, Zhang Y, Ju B, Lin H. Three-axial cutting force measurement in micro/nano-cutting by utilizing a fast tool servo with a smart tool holder.  CIRP Ann. 2021, 70, 33–36. [Google Scholar]
97.
Ma L, Melkote SN, Morehouse JB, Castle JB, Fonda JW, Johnson MA. Thin-film PVDF sensor-based monitoring of cutting forces in peripheral end milling. J. Dyn. Sys. Meas. Control 2012, 134, 051014. [Google Scholar]
98.
Chen X, Cheng K, Wang C. Design of a smart turning tool with application to in-process cutting force measurement in ultraprecision and micro cutting. Manuf. Lett. 2014, 2, 112–117. [Google Scholar]
99.
Xie Z, Lu Y, Li J. Development and testing of an integrated intelligent tool holder for four-component cutting force measurement. Mech. Syst. Sig. Process. 2017, 93, 225–240. [Google Scholar]
100.
Stoney R, Donohoe B, Geraghty D, O’Donnell GE. The development of surface acoustic wave sensors (SAWs) for process monitoring. Procedia CIRP 2012, 1, 569–574. [Google Scholar]
101.
Wang C, Cheng K, Chen X, Minton T, Rakowski R. Design of an instrumented intelligent cutting tool and its implementation and application perspectives. Intell. Mater. Struct. 2014, 23, 035019. [Google Scholar]
102.
Tognazzi F, Porta M, Failli F, Dini G. A preliminary study on a torque sensor for tool condition monitoring in milling. In AMST’05 Advanced Manufacturing Systems and Technology; Springer: Vienna, Austria, 2005.
103.
Dini G, Tognazzi F. Tool condition monitoring in end milling using a torque-based sensorized toolholder. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2007, 221, 11–23. [Google Scholar]
104.
Totis G, Wirtz G, Sortino M, Veselovac D, Kuljanic E, Klocke F. Development of a dynamometer for measuring individual cutting edge forces in face milling. Mech. Syst. Sig. Process. 2010, 24, 1844–1857. [Google Scholar]
105.
Wu F, Li Y, Guo B, Zhang P. The design of force measuring tool holder system based on wireless transmission. IEEE Access 2018, 6, 38556–38566. [Google Scholar]
106.
Schuster A, Rentzsch H, Ihlenfeldt S. Energy self-sufficient, multi-sensory tool holder for sensitive monitoring of milling processes. Procedia CIRP 2023, 117, 80–85. [Google Scholar]
107.
Suprock CA, Fussell BK, Hassan RZ, Jerard RB. A low cost wireless tool tip vibration sensor for milling. In Proceedings of the ASME 2008 International Manufacturing Science and Engineering Conference, Evanston, IL, USA, 7–10 October 2008; pp. 465–474.
108.
Matsuda R, Shindou M, Furuki T, Hirogaki T, Aoyama E. Monitoring Method of Process Temperature and Vibration of Rotating Machining Tool with a Wireless Communication Holder System.  Mater. Sci. Forum 2016, 874, 519–524. [Google Scholar]
109.
Ramsauer C, Bleicher F. New method for determining single cutting edge breakage of a multi-tooth milling tool based on acceleration measurements of an instrumented tool holder.  J. Mach. Eng. 2021, 21, 67–77. [Google Scholar]
110.
Guo K, Sun J. An integrated wireless vibration sensing tool holder for milling tool condition monitoring with singularity analysis. Measurement 2021, 174, 109038. [Google Scholar]
111.
Kerrigan K, O’Donnell GE. Temperature measurement in CFRP milling using a wireless tool-integrated process monitoring sensor. Int. J. Autom. Technol. 2013, 7, 742–750. [Google Scholar]
112.
Le Coz G, Marinescu M, Devillez A, Dudzinski D, Velnom L. Measuring temperature of rotating cutting tools: Application to MQL drilling and dry milling of aerospace alloys. Appl. Therm. Eng. 2012, 36, 434–441. [Google Scholar]
113.
Rizal M, Ghani JA, Nuawi MZ, Haron CHC. A wireless system and embedded sensors on spindle rotating tool for condition monitoring. Adv. Sci. Lett. 2014, 20, 1829–1832. [Google Scholar]
114.
Guha A, Li H, Sun Z, Ma C, Werschmoeller D, Li X. Wireless acquisition of temperature data from embedded thin film sensors in cutting insert. J. Manuf. Processes 2012, 14, 360–365. [Google Scholar]
115.
Campidelli AF, Lima HV, Abrão AM, Maia AA. Development of a wireless system for milling temperature monitoring. Int. J. Adv. Manuf. Technol. 2019, 104, 1551–1560. [Google Scholar]
116.
Mikołajczyk T, Nowicki K, Kłodowski A, Pimenov DY. Neural network approach for automatic image analysis of cutting edge wear. Mech. Syst. Sig. Process. 2017, 88, 100–110. [Google Scholar]
117.
Mikołajczyk T, Nowicki K, Bustillo A, Pimenov DY. Predicting tool life in turning operations using neural networks and image processing. Mech. Syst. Sig. Process. 2018, 104, 503–513. [Google Scholar]
118.
Zhou Y, Sun B, Sun W. A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling. Measurement 2020, 166, 108186. [Google Scholar]
119.
Pimenov DY, Bustillo A, Wojciechowski S, Sharma VS, Gupta MK, Kuntoğlu M. Artificial intelligence systems for tool condition monitoring in machining: Analysis and critical review. J. Intell. Manuf. 2023, 34, 2079–2121. [Google Scholar]
120.
Korkmaz ME, Gupta MK, Li Z, Krolczyk GM, Kuntoğlu M, Binali R, et al. Indirect monitoring of machining characteristics via advanced sensor systems: A critical review. Int. J. Adv. Manuf. Technol. 2022, 120, 7043–7078. [Google Scholar]
121.
Mohamed A, Hassan M, M’Saoubi R, Attia H. Tool condition monitoring for high-performance machining systems—A review. Sensors 2022, 22, 2206. [Google Scholar]
122.
Lei Y, Li N, Guo L, Li N, Yan T, Lin J. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mech. Syst. Sig. Process. 2018, 104, 799–834. [Google Scholar]
123.
Kong D, Chen Y, Li N. Gaussian process regression for tool wear prediction. Mech. Syst. Sig. Process. 2018, 104, 556–574. [Google Scholar]
124.
Xie Z, Li J, Lu Y. Feature selection and a method to improve the performance of tool condition monitoring. Int. J. Adv. Manuf. Technol. 2019, 100, 3197–3206. [Google Scholar]
125.
Guo K, Yang B, Wang H, Sun J, Lu L. Singularity analysis of cutting force and vibration for tool condition monitoring in milling. IEEE Access 2019, 7, 134113–134124. [Google Scholar]
126.
Yang B, Guo K, Liu J, Sun J, Song G, Zhu S, et al. Vibration singularity analysis for milling tool condition monitoring. Int. J. Mech. Sci. 2020, 166, 105254. [Google Scholar]
127.
Xie Z, Lu Y, Chen X. A multi-sensor integrated intelligent tool holder for cutting process monitoring. Int. J. Adv. Manuf. Technol. 2020, 110, 853–864. [Google Scholar]
128.
Bleicher F, Ramsauer CM, Oswald R, Leder N, Schoerghofer P. Method for determining edge chipping in milling based on tool holder vibration measurements. CIRP Ann. 2020, 69, 101–104. [Google Scholar]
129.
Gent S, Gert O, Schörghofer P, Ramsauer CM, Bleicher F, Leder N, et al. Maintenance interval monitoring and cutting edge breakout detection using an instrumented tool. In Proceedings of the 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart, Germany, 6–9 September 2022.
130.
Öztürk T, Sarıkaya E, Weigold M. Sensor-integrated tap holder for process uncertainty detection based on tool vibration and axial length compensation sensors. Int. J. Adv. Manuf. Technol. 2021, 117, 1905–1914. [Google Scholar]
131.
Letot C, Serra R, Dossevi M, Dehombreux P. Cutting tools reliability and residual life prediction from degradation indicators in turning process: A case study involving four approaches. Int. J. Adv. Manuf. Technol. 2016, 86, 495–506. [Google Scholar]
132.
Xu L, Huang C, Li C, Wang J, Liu H, Wang X. Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining. J. Intell. Manuf. 2021, 32, 77–90. [Google Scholar]
133.
McParland D, Baron S, O’Rourke S, Dowling D, Ahearne E, Parnell A. Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models. J. Intell. Manuf. 2019, 30, 1259–1270. [Google Scholar]
134.
Javed K, Gouriveau R, Li X, Zerhouni N. Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model. J. Intell. Manuf. 2018, 29, 1873–1890. [Google Scholar]
135.
Li H, Wang W, Li Z, Dong L, Li Q. A novel approach for predicting tool remaining useful life using limited data. Mech. Syst. Sig. Process. 2020, 143, 106832. [Google Scholar]
136.
Twardowski P, Wiciak-Pikuła M. Prediction of tool wear using artificial neural networks during turning of hardened steel. Materials 2019, 12, 3091. [Google Scholar]
137.
Zhang C, Yao X, Zhang J, Jin H. Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors 2016, 16, 795. [Google Scholar]
138.
Chen N, Hao B, Guo Y, Li L, Khan MA, He N. Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach. Int. J. Adv. Manuf. Technol. 2020, 108, 2091–2101. [Google Scholar]
139.
Wang P, Gao RX. Adaptive resampling-based particle filtering for tool life prediction. J. Manuf. Syst. 2015, 37, 528–534. [Google Scholar]
140.
Hui Y, Mei X, Jiang G, Tao T, Pei C, Ma Z. Milling tool wear state recognition by vibration signal using a stacked generalization ensemble model. Shock Vibr. 2019, 2019, 1–16. [Google Scholar]
141.
Liao X, Zhou G, Zhang Z, Lu J, Ma J. Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm. Int. J. Adv. Manuf. Technol. 2019, 104, 1051–1063. [Google Scholar]
142.
Zhang B, Shin YC. A multimodal intelligent monitoring system for turning processes.  J. Manuf. Processes 2018, 35, 547–558. [Google Scholar]
143.
Olufayo O, Abou-El-Hossein K. Tool life estimation based on acoustic emission monitoring in end-milling of H13 mould-steel.  Int. J. Adv. Manuf. Technol. 2015, 81, 39–51. [Google Scholar]
144.
Caggiano A. Tool wear prediction in Ti-6Al-4V machining through multiple sensor monitoring and PCA features pattern recognition. Sensors 2018, 18, 823. [Google Scholar]
145.
Ferreira FI, de Aguiar PR, Lopes WN, Martins CHR, Ruzzi RDS, Bianchi EC, et al. Inferential measurement of the dresser width for the grinding process automation. Int. J. Adv. Manuf. Technol. 2019, 100, 3055–3066. [Google Scholar]
146.
Sun H, Cao D, Zhao Z, Kang X. A hybrid approach to cutting tool remaining useful life prediction based on the Wiener process. IEEE Transact. Reliab. 2018, 67, 1294–1303. [Google Scholar]
147.
Chen Y, Jin Y, Jiri G. Predicting tool wear with multi-sensor data using deep belief networks. Int. J. Adv. Manuf. Technol. 2018, 99, 1917–1926. [Google Scholar]
148.
Krishnakumar P, Rameshkumar K, Ramachandran KI. Acoustic emission-based tool condition classification in a precision high-speed machining of titanium alloy: a machine learning approach. Int. J. Comput. Intell. Appl. 2018, 17, 1850017. [Google Scholar]
149.
Pandiyan V, Caesarendra W, Tjahjowidodo T, Tan HH. In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. J. Manuf. Processes 2018, 31, 199–213. [Google Scholar]
150.
Nakai ME, Aguiar PR, Guillardi H Jr, Bianchi EC, Spatti DH, D’Addona DM. Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics. Expert Syst. Appl. 2015, 42, 7026–7035. [Google Scholar]
151.
da Silva RHL, da Silva MB, Hassui A. A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals. Mach. Sci. Technol. 2016, 20, 386–405. [Google Scholar]
152.
Drouillet C, Karandikar J, Nath C, Journeaux AC, El Mansori M, Kurfess T. Tool life predictions in milling using spindle power with the neural network technique. J. Manuf. Processes 2016, 22, 161–168. [Google Scholar]
153.
Corne R, Nath C, El Mansori M, Kurfess T. Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling. J. Manuf. Syst. 2017, 43, 287–295. [Google Scholar]
154.
Sahu NK, Andhare AB. Modelling and multiobjective optimization for productivity improvement in high speed milling of Ti–6Al–4V using RSM and GA. J. Braz. Soc. Mech. Sci. Eng. 2017, 39, 5069–5085. [Google Scholar]
155.
Pimenov DY, Bustillo A, Mikolajczyk T. Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. J. Intell. Manuf. 2018, 29, 1045–1061. [Google Scholar]
156.
Bustillo A, Reis R, Machado AR, Pimenov DY. Improving the accuracy of machine-learning models with data from machine test repetitions. J. Intell. Manuf. 2022, 33, 203–221. [Google Scholar]
157.
Xu L, Huang C, Li C, Wang J, Liu H, Wang X. A novel intelligent reasoning system to estimate energy consumption and optimize cutting parameters toward sustainable machining. J. Clean. Prod. 2020, 261, 121160. [Google Scholar]
158.
Niaki FA, Feng L, Ulutan D, Mears L. A wavelet-based data-driven modelling for tool wear assessment of difficult to machine materials. Int. J. Mechatron. Manuf. Syst. 2016, 9, 97–121. [Google Scholar]
159.
Akhavan Niaki F, Ulutan D, Mears L. Parameter inference under uncertainty in end-milling γ′-strengthened difficult-to-machine alloy. J. Manuf. Sci. Eng. 2016, 138, 061014. [Google Scholar]
160.
Mia M, Khan MA, Dhar NR. Performance prediction of high-pressure coolant assisted turning of Ti-6Al-4V. Int. J. Adv. Manuf. Technol. 2017, 90, 1433–1445. [Google Scholar]
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