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Indirect monitoring of machining characteristics via advanced sensor systems: a critical review

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Abstract

On-line monitoring of the machining processes provides to detect the amount and type of tool wear which is critical for the determination of remaining useful lifetime of cutting tool. According to Industry 4.0 revolution, the machining performance in terms of cutting forces, surface roughness, power consumptions, tool wear, tool life, etc. needs to be automatically monitored because the unfavorable conditions in machining cause chatter vibrations, tool breakage, and dimensional accuracy. Therefore, the usage of advanced sensor systems plays a key role in achieving the improved machining characteristics in terms of less human effort, errors, production time, etc. and fulfills the requirement of Industry 4.0. Hence, this review presents the holistic knowledge of online detection systems including sensors and signal processing software preferred in mechanical machining operations. Initially, this paper is starting with the up-to-date literature introduction section followed by type of sensors used in machining, online detection methods in machining, challenges and suggestions, etc. Eventually, the article concluded the findings and future remarks especially focused on the theme of Industry 4.0. In the end, it is worthy to mention that this review paper is very helpful for researchers and academicians working in the industrial sectors.

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References

  1. Korkmaz ME, Yaşar N (2021) FEM modelling of turning of AA6061-T6: Investigation of chip morphology, chip thickness and shear angle. J Prod Syst Manuf Sci 2:50–58

    Google Scholar 

  2. Yang Y, Guo Y, Huang Z et al (2019) Research on the milling tool wear and life prediction by establishing an integrated predictive model. Measurement 145:178–189. https://doi.org/10.1016/j.measurement.2019.05.009

  3. Arrazola PJ, Rech J, M’Saoubi R, Axinte D (2020) Broaching: cutting tools and machine tools for manufacturing high quality features in components. CIRP Ann 69:554–577. https://doi.org/10.1016/j.cirp.2020.05.010

  4. Kärcher S, Cuk E, Denner T et al (2018) Sensor-driven analysis of manual assembly systems. Procedia CIRP 72:1142–1147. https://doi.org/10.1016/j.procir.2018.03.241

  5. Chen JC, Chen JC (2005) An artificial-neural-networks-based in-process tool wear prediction system in milling operations. Int J Adv Manuf Technol 25:427–434. https://doi.org/10.1007/s00170-003-1848-y

    Article  Google Scholar 

  6. Özel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45:467–479. https://doi.org/10.1016/j.ijmachtools.2004.09.007

  7. Yaseer A, Chen H (2021) Machine learning based layer roughness modeling in robotic additive manufacturing. J Manuf Process 70:543–552. https://doi.org/10.1016/j.jmapro.2021.08.056

  8. Lee S, Rasoolian B, Silva DF et al (2021) Surface roughness parameter and modeling for fatigue behavior of additive manufactured parts: a non-destructive data-driven approach. Addit Manuf 46:102094. https://doi.org/10.1016/j.addma.2021.102094

  9. Ghazali MF, Abdullah MM, Abd Rahim SZ et al (2021) Tool wear and surface evaluation in drilling fly ash geopolymer using HSS, HSS-Co, and HSS-TiN cutting tools. Materials 14

  10. Yaşar N, Korkmaz ME, Gupta MK et al (2021) A novel method for improving drilling performance of CFRP/Ti6AL4V stacked materials. Int J Adv Manuf Technol 117:653–673. https://doi.org/10.1007/s00170-021-07758-0

    Article  Google Scholar 

  11. Antić A, Popović B, Krstanović L et al (2018) Novel texture-based descriptors for tool wear condition monitoring. Mech Syst Signal Process 98:1–15. https://doi.org/10.1016/j.ymssp.2017.04.030

  12. García-Ordás MT, Alegre-Gutiérrez E, Alaiz-Rodríguez R, González-Castro V (2018) Tool wear monitoring using an online, automatic and low cost system based on local texture. Mech Syst Signal Process 112:98–112. https://doi.org/10.1016/j.ymssp.2018.04.035

  13. Benardos PG, Vosniakos G-C (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43:833–844. https://doi.org/10.1016/S0890-6955(03)00059-2

  14. Zhu D, Feng X, Xu X et al (2020) Robotic grinding of complex components: a step towards efficient and intelligent machining – challenges, solutions, and applications. Robot Comput Integr Manuf 65:101908. https://doi.org/10.1016/j.rcim.2019.101908

  15. Gökçe H, Çiftçi İ, Demir H (2018) Cutting parameter optimization in shoulder milling of commercially pure molybdenum. J Brazilian Soc Mech Sci Eng 40:360. https://doi.org/10.1007/s40430-018-1280-8

    Article  Google Scholar 

  16. Mawson VJ, Hughes BR (2019) The development of modelling tools to improve energy efficiency in manufacturing processes and systems. J Manuf Syst 51:95–105. https://doi.org/10.1016/j.jmsy.2019.04.008

  17. Zhang X, Pan T, Ma A, Zhao W (2022) High efficiency orientated milling parameter optimization with tool wear monitoring in roughing operation. Mech Syst Signal Process 165:108394. https://doi.org/10.1016/j.ymssp.2021.108394

  18. Dong Z, Sun X, Chen C et al (2019) An improved signal processing method for the laser displacement sensor in mechanical systems. Mech Syst Signal Process 122:403–418. https://doi.org/10.1016/j.ymssp.2018.12.018

  19. Liu Y, Guo L, Gao H et al (2022) Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: a review. Mech Syst Signal Process 164:108068. https://doi.org/10.1016/j.ymssp.2021.108068

  20. Zhang X, Yu T, Xu P, Zhao J (2022) In-process stochastic tool wear identification and its application to the improved cutting force modeling of micro milling. Mech Syst Signal Process 164:108233. https://doi.org/10.1016/j.ymssp.2021.108233

  21. Cheng K, Huo D (2013) Micro‐cutting: fundamentals and applications. John Wiley & Sons Ltd, Chichester, UK

  22. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann 59:717–739. https://doi.org/10.1016/j.cirp.2010.05.010

  23. Han S, Mannan N, Stein DC et al (2021) Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems. J Manuf Syst 61:45–53. https://doi.org/10.1016/j.jmsy.2021.08.004

  24. Cheng K, Niu Z-C, Wang RC et al (2017) Smart cutting tools and smart machining: development approaches, and their ımplementation and application perspectives. Chinese J Mech Eng 30:1162–1176. https://doi.org/10.1007/s10033-017-0183-4

    Article  Google Scholar 

  25. Kuntoğlu M, Aslan A, Pimenov DY et al (2021) A review of ındirect tool condition monitoring systems and decision-making methods in turning: critical analysis and trends. Sensors. https://doi.org/10.3390/s21010108

    Article  Google Scholar 

  26. Mishra D, Roy RB, Dutta S et al (2018) A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0. J Manuf Process 36:373–397. https://doi.org/10.1016/j.jmapro.2018.10.016

  27. Liang YC, Li WD, Lu X, Wang S (2019) Fog computing and convolutional neural network enabled prognosis for machining process optimization. J Manuf Syst 52:32–42. https://doi.org/10.1016/j.jmsy.2019.05.003

  28. Chen B, Zhang Z, Sun C et al (2012) Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors. Mech Syst Signal Process 33:275–298. https://doi.org/10.1016/j.ymssp.2012.07.007

  29. Yavuz M, Gökçe H, Çiftçi İ et al (2020) Investigation of the effects of drill geometry on drilling performance and hole quality. Int J Adv Manuf Technol 106:4623–4633. https://doi.org/10.1007/s00170-019-04843-3

    Article  Google Scholar 

  30. Gökçe H (2021) Modelling and optimization for thrust force, temperature and burr height in drilling of custom 450. Exp Tech. https://doi.org/10.1007/s40799-021-00510-z

    Article  Google Scholar 

  31. Rahimi MH, Huynh HN, Altintas Y (2021) On-line chatter detection in milling with hybrid machine learning and physics-based model. CIRP J Manuf Sci Technol 35:25–40. https://doi.org/10.1016/j.cirpj.2021.05.006

  32. Balsamo V, Caggiano A, Jemielniak K et al (2016) Multi sensor signal processing for catastrophic tool failure detection in turning. Procedia CIRP 41:939–944. https://doi.org/10.1016/j.procir.2016.01.010

  33. Tillmann W, Vogli E, Herper J et al (2010) Development of temperature sensor thin films to monitor turning processes. J Mater Process Technol 210:819–823. https://doi.org/10.1016/j.jmatprotec.2010.01.013

  34. Kene AP, Orra K, Choudhury SK (2016) Experimental ınvestigation of tool wear behavior of multi-layered coated carbide ınserts using various sensors in hard turning process. IFAC-PapersOnLine 49:180–184. https://doi.org/10.1016/j.ifacol.2016.07.592

  35. Clauß B, Meinecke CR, Günther W et al (2020) Process monitoring and impulse detection in face milling using capacitive acceleration sensors based on MEMS. Procedia CIRP 93:1454–1459. https://doi.org/10.1016/j.procir.2020.03.037

  36. Bernard SE, Selvaganesh R, Khoshick G, Raj DS (2021) A novel contact area based analysis to study the thermo-mechanical effect of cutting edge radius using numerical and multi-sensor experimental investigation in turning. J Mater Process Technol 293:117085. https://doi.org/10.1016/j.jmatprotec.2021.117085

  37. Miura K, Döbbeler B, Klocke F (2018) Cutting power estimation via external voltage and current sensors on feed-drive axis for the straight milling process. Procedia CIRP 78:323–328. https://doi.org/10.1016/j.procir.2018.09.068

  38. Kuntoğlu M, Sağlam H (2021) Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Measurement 173:108582. https://doi.org/10.1016/j.measurement.2020.108582

  39. Tran M-Q, Liu M-K, Elsisi M (2021) Effective multi-sensor data fusion for chatter detection in milling process. ISA Trans. https://doi.org/10.1016/j.isatra.2021.07.005

  40. Uebel J, Ströer F, Basten S et al (2019) Approach for the observation of surface conditions in-process by soft sensors during cryogenic hard turning. Procedia CIRP 81:1260–1265. https://doi.org/10.1016/j.procir.2019.03.304

  41. Finkeldey F, Saadallah A, Wiederkehr P, Morik K (2020) Real-time prediction of process forces in milling operations using synchronized data fusion of simulation and sensor data. Eng Appl Artif Intell 94:103753. https://doi.org/10.1016/j.engappai.2020.103753

  42. Zhang XY, Lu X, Wang S et al (2018) A multi-sensor based online tool condition monitoring system for milling process. Procedia CIRP 72:1136–1141. https://doi.org/10.1016/j.procir.2018.03.092

  43. Demirsöz R, Yaşar N, Korkmaz ME et al (2022) Evaluation of the mechanical properties and drilling of glass bead/fiber-reinforced polyamide 66 (PA66)-based hybrid polymer composites. Materials 15

  44. Çamlı KY, Demirsöz R, Boy M et al (2022) Performance of MQL and nano-MQL lubrication in machining ER7 steel for train wheel applications. Lubricants 10:48

    Article  Google Scholar 

  45. Kim D, Jeon D (2011) Fuzzy-logic control of cutting forces in CNC milling processes using motor currents as indirect force sensors. Precis Eng 35:143–152. https://doi.org/10.1016/j.precisioneng.2010.09.001

  46. Miura K, Bergs T (2019) A method of cutting power monitoring for feed axes in milling by power measurement device. IFAC-PapersOnLine 52:2471–2476. https://doi.org/10.1016/j.ifacol.2019.11.577

  47. Oliveira JFG, Ferraz Júnior F, Coelho RT, Silva EJ (2008) Architecture for machining process and production monitoring based in open computer numerical control. Proc Inst Mech Eng Part B J Eng Manuf 222:1605–1612. https://doi.org/10.1243/09544054JEM1156

    Article  Google Scholar 

  48. Vidlak M, Makys P, Stano M (2021) Comparison between model based and non-model based sensorless methods of brushed DC motor. Transp Res Procedia 55:911–918. https://doi.org/10.1016/j.trpro.2021.07.059

  49. Pritschow G, Kramer C (2005) Open system architecture for drives. CIRP Ann 54:375–378. https://doi.org/10.1016/S0007-8506(07)60126-7

  50. Yu X, Zhang R, Zhou D et al (2021) Effects of oil recess structural parameters on comprehensive tribological properties in multi-pad hydrostatic thrust bearing for CNC vertical processing equipment based on low power consumption. Energy Reports. https://doi.org/10.1016/j.egyr.2021.09.017

  51. Kim E-J, Lee C-M (2020) Experimental study on power consumption of laser and induction assisted machining with inconel 718. J Manuf Process 59:411–420. https://doi.org/10.1016/j.jmapro.2020.09.064

  52. Sealy MP, Liu ZY, Zhang D et al (2016) Energy consumption and modeling in precision hard milling. J Clean Prod 135:1591–1601. https://doi.org/10.1016/j.jclepro.2015.10.094

    Article  Google Scholar 

  53. Wang L, He Y, Li Y et al (2019) Modeling and analysis of specific cutting energy of whirling milling process based on cutting parameters. Procedia CIRP 80:56–61. https://doi.org/10.1016/j.procir.2019.01.028

    Article  Google Scholar 

  54. Moliner-Heredia R, Peñarrocha-Alós I, Abellán-Nebot JV (2021) Model-based tool condition prognosis using power consumption and scarce surface roughness measurements. J Manuf Syst 61:311–325. https://doi.org/10.1016/j.jmsy.2021.09.001

    Article  Google Scholar 

  55. Wirtz A, Meiner M, Wiederkehr P, Myrzik J (2018) Simulation-assisted ınvestigation of the electric power consumption of milling processes and machine tools. Procedia CIRP 67:87–92. https://doi.org/10.1016/j.procir.2017.12.181

    Article  Google Scholar 

  56. Kant G, Sangwan KS (2014) Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. J Clean Prod 83:151–164. https://doi.org/10.1016/j.jclepro.2014.07.073

    Article  Google Scholar 

  57. Pawanr S, Garg GK, Routroy S (2019) Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS. Procedia CIRP 86:116–120. https://doi.org/10.1016/j.procir.2020.01.036

    Article  Google Scholar 

  58. Eberspächer P, Schraml P, Schlechtendahl J et al (2014) A model- and signal-based power consumption monitoring concept for energetic optimization of machine tools. Procedia CIRP 15:44–49. https://doi.org/10.1016/j.procir.2014.06.020

    Article  Google Scholar 

  59. Venkatesan K (2018) Optimization of surface roughness and power consumption in laser-assisted machining of Inconel 718 by Taguchi based response surface methodology. Mater Today Proc 5:11326–11335. https://doi.org/10.1016/j.matpr.2018.02.099

    Article  Google Scholar 

  60. Bhushan RK (2013) Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod 39:242–254. https://doi.org/10.1016/j.jclepro.2012.08.008

  61. Hanafi I, Khamlichi A, Cabrera FM et al (2012) Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools. J Clean Prod 33:1–9. https://doi.org/10.1016/j.jclepro.2012.05.005

    Article  Google Scholar 

  62. Aggarwal A, Singh H, Kumar P, Singh M (2008) Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi’s technique—a comparative analysis. J Mater Process Technol 200:373–384. https://doi.org/10.1016/j.jmatprotec.2007.09.041

    Article  Google Scholar 

  63. Shinohara M, Kunieda M (2020) Influences of discharge current pulse shape on machining characteristics in EDM. Procedia CIRP 95:200–203. https://doi.org/10.1016/j.procir.2020.03.146

    Article  Google Scholar 

  64. Gamage JR, DeSilva AKM, Chantzis D, Antar M (2017) Sustainable machining: process energy optimisation of wire electrodischarge machining of Inconel and titanium superalloys. J Clean Prod 164:642–651. https://doi.org/10.1016/j.jclepro.2017.06.186

    Article  Google Scholar 

  65. Newman ST, Nassehi A, Imani-Asrai R, Dhokia V (2012) Energy efficient process planning for CNC machining. CIRP J Manuf Sci Technol 5:127–136. https://doi.org/10.1016/j.cirpj.2012.03.007

    Article  Google Scholar 

  66. Kara S, Li W (2011) Unit process energy consumption models for material removal processes. CIRP Ann 60:37–40. https://doi.org/10.1016/j.cirp.2011.03.018

    Article  Google Scholar 

  67. Li W, Winter M, Kara S, Herrmann C (2012) Eco-efficiency of manufacturing processes: a grinding case. CIRP Ann 61:59–62. https://doi.org/10.1016/j.cirp.2012.03.029

    Article  Google Scholar 

  68. Oda Y, Mori M, Ogawa K et al (2012) Study of optimal cutting condition for energy efficiency improvement in ball end milling with tool-workpiece inclination. CIRP Ann 61:119–122. https://doi.org/10.1016/j.cirp.2012.03.034

    Article  Google Scholar 

  69. Reddy MC, Rao KV, Suresh G (2021) An experimental investigation and optimization of energy consumption and surface defects in wire cut electric discharge machining. J Alloys Compd 861:158582. https://doi.org/10.1016/j.jallcom.2020.158582

    Article  Google Scholar 

  70. Lv J, Tang R, Jia S, Liu Y (2016) Experimental study on energy consumption of computer numerical control machine tools. J Clean Prod 112:3864–3874. https://doi.org/10.1016/j.jclepro.2015.07.040

    Article  Google Scholar 

  71. Bhushan RK (2013) Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod 39:242–254. https://doi.org/10.1016/j.jclepro.2012.08.008

    Article  Google Scholar 

  72. Dong J, Subrahmanyam KVR, Wong YS et al (2006) Bayesian-inference-based neural networks for tool wear estimation. Int J Adv Manuf Technol 30:797–807. https://doi.org/10.1007/s00170-005-0124-8

    Article  Google Scholar 

  73. Zhang X, Zheng G, Cheng X et al (2020) 2D fractal analysis of the cutting force and surface profile in turning of iron-based superalloy. Measurement 151:107125. https://doi.org/10.1016/j.measurement.2019.107125

  74. Korkmaz ME (2020) Verification of Johnson-Cook parameters of ferritic stainless steel by drilling process: experimental and finite element simulations. J Mater Res Technol 9:6322–6330. https://doi.org/10.1016/j.jmrt.2020.03.045

    Article  Google Scholar 

  75. Mohanraj T, Shankar S, Rajasekar R et al (2020) Tool condition monitoring techniques in milling process — a review. J Mater Res Technol 9:1032–1042. https://doi.org/10.1016/j.jmrt.2019.10.031

  76. Ubeda RP, Gutiérrez Rubert SC, Zotovic Stanisic R, Perles Ivars Á (2018) Design and manufacturing of an ultra-low-cost custom torque sensor for robotics. Sensors. https://doi.org/10.3390/s18061786

    Article  Google Scholar 

  77. Huang SN, Tan KK, Wong YS et al (2007) Tool wear detection and fault diagnosis based on cutting force monitoring. Int J Mach Tools Manuf 47:444–451. https://doi.org/10.1016/j.ijmachtools.2006.06.011

  78. Sanchez Y, Trujillo FJ, Sevilla L, Marcos M (2017) Indirect monitoring method of tool wear using the analysis of cutting force during dry machining of Ti alloys. Procedia Manuf 13:623–630. https://doi.org/10.1016/j.promfg.2017.09.127

  79. Rizal M, Ghani JA, Nuawi MZ, Haron CHC (2013) The application of I-kazTM-based method for tool wear monitoring using cutting force signal. Procedia Eng 68:461–468. https://doi.org/10.1016/j.proeng.2013.12.207

  80. Cakir MC, Isik Y (2005) Detecting tool breakage in turning aisi 1050 steel using coated and uncoated cutting tools. J Mater Process Technol 159:191–198. https://doi.org/10.1016/j.jmatprotec.2004.05.006

  81. Luo X, Cheng K, Holt R, Liu X (2005) Modeling flank wear of carbide tool insert in metal cutting. Wear 259:1235–1240. https://doi.org/10.1016/j.wear.2005.02.044

  82. Scheffer C, Kratz H, Heyns PS, Klocke F (2003) Development of a tool wear-monitoring system for hard turning. Int J Mach Tools Manuf 43:973–985. https://doi.org/10.1016/S0890-6955(03)00110-X

    Article  Google Scholar 

  83. Zhang S, Jiao F, Wang X, Niu Y (2021) Modeling of cutting forces in helical milling of unidirectional CFRP considering carbon fiber fracture. J Manuf Process 68:1495–1508. https://doi.org/10.1016/j.jmapro.2021.06.058

    Article  Google Scholar 

  84. Bari P, Law M, Wahi P (2021) Comparative analysis of cutting forces and stability of standard and non-standard profiled serrated end mills. Procedia CIRP 101:114–117. https://doi.org/10.1016/j.procir.2021.02.014

    Article  Google Scholar 

  85. Reddy TS, Banik T, Velagala R, Kashyap S (2020) A study and modeling of cutting forces in dry turning of heat treated AISI H13 tool steel with brazed tungsten carbide tip. Mater Today Proc 24:704–713. https://doi.org/10.1016/j.matpr.2020.04.326

    Article  Google Scholar 

  86. Jadhav P, Kumar S, Bongale A (2020) Optimization of cutting forces by cryogenic treatment on tungsten carbide inserts during dry turning of the P 20 tool steel. Mater Today Proc 28:2485–2493. https://doi.org/10.1016/j.matpr.2020.04.798

    Article  Google Scholar 

  87. Bratan S, Novikov P (2021) Theoretical determination of cutting forces during machining holes in parts made of alloy iron-carbon alloys. Mater Today Proc 38:2009–2012. https://doi.org/10.1016/j.matpr.2020.10.030

    Article  Google Scholar 

  88. Jangali SG, Gaitonde VN, Kulkarni VN, Madhusudhana HK (2021) Analyzing the effect of cutting parameters on forces and tool-tip temperature in turning of nickel-based superalloy using FE simulation. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.08.054

    Article  Google Scholar 

  89. Mehta S, Singh G, Saini A, Singh H (2021) Finite element analysis of face milling of Ti-6Al-4 V alloy considering cutting forces and cutting temperatures. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.10.061

    Article  Google Scholar 

  90. Mostaghimi H, Park CI, Kang G et al (2021) Reconstruction of cutting forces through fusion of accelerometer and spindle current signals. J Manuf Process 68:990–1003. https://doi.org/10.1016/j.jmapro.2021.06.007

    Article  Google Scholar 

  91. Zhao Z, To S, Zhu Z, Yin T (2020) A theoretical and experimental investigation of cutting forces and spring back behaviour of Ti6Al4V alloy in ultraprecision machining of microgrooves. Int J Mech Sci 169:105315. https://doi.org/10.1016/j.ijmecsci.2019.105315

    Article  Google Scholar 

  92. Aslan A (2020) Optimization and analysis of process parameters for flank wear, cutting forces and vibration in turning of AISI 5140: a comprehensive study. Measurement 163:107959. https://doi.org/10.1016/j.measurement.2020.107959

    Article  Google Scholar 

  93. Otalora-Ortega H, Aristimuño Osoro P, Arrazola Arriola P (2021) Uncut chip geometry determination for cutting forces prediction in orthogonal turn-milling operations considering the tool profile and eccentricity. Int J Mech Sci 198:106351. https://doi.org/10.1016/j.ijmecsci.2021.106351

    Article  Google Scholar 

  94. Kaya B, Oysu C, Ertunc HM (2011) Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks. Adv Eng Softw 42:76–84. https://doi.org/10.1016/j.advengsoft.2010.12.002

    Article  Google Scholar 

  95. Zhang X, Pan T, Ma A, Zhao W (2022) High efficiency orientated milling parameter optimization with tool wear monitoring in roughing operation. Mech Syst Signal Process 165:108394. https://doi.org/10.1016/j.ymssp.2021.108394

    Article  Google Scholar 

  96. An Q, Cai C, Zou F et al (2020) Tool wear and machined surface characteristics in side milling Ti6Al4V under dry and supercritical CO2 with MQL conditions. Tribol Int 151:106511. https://doi.org/10.1016/j.triboint.2020.106511

    Article  Google Scholar 

  97. Li S, Zhang D, Liu C et al (2021) Influence of dynamic angles and cutting strain on chip morphology and cutting forces during titanium alloy Ti-6Al-4 V vibration-assisted drilling. J Mater Process Technol 288:116898. https://doi.org/10.1016/j.jmatprotec.2020.116898

    Article  Google Scholar 

  98. Chen Y-L, Tao Y, Hu P et al (2021) Self-sensing of cutting forces in diamond cutting by utilizing a voice coil motor-driven fast tool servo. Precis Eng 71:178–186. https://doi.org/10.1016/j.precisioneng.2021.03.009

    Article  Google Scholar 

  99. Aslan A (2020) Optimization and analysis of process parameters for flank wear, cutting forces and vibration in turning of AISI 5140: a comprehensive study. Measurement 163:107959. https://doi.org/10.1016/j.measurement.2020.107959

  100. McCloskey P, Katz A, Berglind L et al (2019) Chip geometry and cutting forces in gear power skiving. CIRP Ann 68:109–112. https://doi.org/10.1016/j.cirp.2019.04.085

  101. Wang C, Cheng K, Nelson N et al (2014) Cutting force–based analysis and correlative observations on the tool wear in diamond turning of single-crystal silicon. Proc Inst Mech Eng Part B J Eng Manuf 229:1867–1873. https://doi.org/10.1177/0954405414543316

    Article  Google Scholar 

  102. Wang C, Cheng K, Rakowski R, Soulard J (2018) An experimental investigation on ultra-precision instrumented smart aerostatic bearing spindle applied to high speed micro-drilling. J Manuf Process 31:324–335. https://doi.org/10.1016/j.jmapro.2017.11.022

  103. Świć A, Gola A, Sobaszek Ł, Šmidová N (2021) A thermo-mechanical machining method for improving the accuracy and stability of the geometric shape of long low-rigidity shafts. J Intell Manuf 32:1939–1951. https://doi.org/10.1007/s10845-020-01733-4

    Article  Google Scholar 

  104. Niu Z, Jiao F, Cheng K (2018) An innovative investigation on chip formation mechanisms in micro-milling using natural diamond and tungsten carbide tools. J Manuf Process 31:382–394. https://doi.org/10.1016/j.jmapro.2017.11.023

    Article  Google Scholar 

  105. Shu S, Cheng K, Ding H, Chen S (2013) An ınnovative method to measure the cutting temperature in process by using an ınternally cooled smart cutting tool. J Manuf Sci Eng. https://doi.org/10.1115/1.4025742

    Article  Google Scholar 

  106. Abukhshim NA, Mativenga PT, Sheikh MA (2006) Heat generation and temperature prediction in metal cutting: a review and implications for high speed machining. Int J Mach Tools Manuf 46:782–800. https://doi.org/10.1016/j.ijmachtools.2005.07.024

  107. Ferri C, Minton T, Ghani SBC, Cheng K (2014) Internally cooled tools and cutting temperature in contamination-free machining. Proc Inst Mech Eng Part C J Mech Eng Sci 228:135–145. https://doi.org/10.1177/0954406213480312

    Article  Google Scholar 

  108. Davim JP, Maranhão C (2009) A study of plastic strain and plastic strain rate in machining of steel AISI 1045 using FEM analysis. Mater Des 30:160–165. https://doi.org/10.1016/j.matdes.2008.04.029

  109. Kus A, Isik Y, Cakir CM et al (2015) Thermocouple and infrared sensor-based measurement of temperature distribution in metal cutting. Sensors (Switzerland) 15:1274–1291. https://doi.org/10.3390/s150101274

    Article  Google Scholar 

  110. List G, Sutter G, Bouthiche A (2012) Cutting temperature prediction in high speed machining by numerical modelling of chip formation and its dependence with crater wear. Int J Mach Tools Manuf 54–55:1–9. https://doi.org/10.1016/j.ijmachtools.2011.11.009

  111. Zhao J, Liu Z (2019) Modelling for prediction of time-varying heat partition coefficient at coated tool-chip interface in continuous turning and interrupted milling. Int J Mach Tools Manuf 147:103467. https://doi.org/10.1016/j.ijmachtools.2019.103467

  112. Shah D, Bhavsar S (2020) Effect of tool nose radius and machining parameters on cutting force, cutting temperature and surface roughness – an experimental study of Ti-6Al-4V (ELI). Mater Today Proc 22:1977–1986. https://doi.org/10.1016/j.matpr.2020.03.163

    Article  Google Scholar 

  113. Hou G, Luo B, Zhang K et al (2021) Investigation of high temperature effect on CFRP cutting mechanism based on a temperature controlled orthogonal cutting experiment. Compos Struct 268:113967. https://doi.org/10.1016/j.compstruct.2021.113967

    Article  Google Scholar 

  114. Shimanuki K, Hosokawa A, Koyano T et al (2020) Studies on high-efficiency and high-precision orthogonal turn-milling-the effects of relative cutting speed and tool axis offset on tool flank temperature. Precis Eng 66:180–187. https://doi.org/10.1016/j.precisioneng.2020.06.013

    Article  Google Scholar 

  115. Nalband SC, Pamidimukkala K, Gunda RK, Reddy Paturi UM (2021) Effect of minimum quantity solid lubrication (MQSL) parameters on cutting force and temperature during turning of EN31 steel. Mater Today Proc 38:3314–3319. https://doi.org/10.1016/j.matpr.2020.10.119

    Article  Google Scholar 

  116. Jayarjun Kadam B, Mahajan KA (2021) Optimization of cutting temperature in machining of titanium alloy using response surface method, genetic algorithm and Taguchi method. Mater Today Proc 47:6285–6290. https://doi.org/10.1016/j.matpr.2021.05.252

    Article  Google Scholar 

  117. Zhao J, Liu Z (2020) Influences of coating thickness on cutting temperature for dry hard turning Inconel 718 with PVD TiAlN coated carbide tools in initial tool wear stage. J Manuf Process 56:1155–1165. https://doi.org/10.1016/j.jmapro.2020.06.010

    Article  Google Scholar 

  118. Raffic NM, Babu KG, Srinivasan S et al (2021) Experimental investigation on surface roughness and cutting tool – workpiece interface temperature for AA6061 using CRITIC and TOPSIS techniques. Mater Today Proc 47:6858–6863. https://doi.org/10.1016/j.matpr.2021.05.145

    Article  Google Scholar 

  119. Kaushik VS, Subramanian M, Sakthivel M (2018) Optimization of processes parameters on temperature rise in CNC end milling of Al 7068 using hybrid techniques. Mater Today Proc 5:7037–7046. https://doi.org/10.1016/j.matpr.2017.11.367

    Article  Google Scholar 

  120. Shan C, Zhang X, Shen B, Zhang D (2019) An improved analytical model of cutting temperature in orthogonal cutting of Ti6Al4V. Chinese J Aeronaut 32:759–769. https://doi.org/10.1016/j.cja.2018.12.001

    Article  Google Scholar 

  121. Zhao J, Liu Z, Ren X et al (2021) Coating-thickness-dependent physical properties and cutting temperature for cutting Inconel 718 with TiAlN coated tools. J Adv Res. https://doi.org/10.1016/j.jare.2021.07.009

    Article  Google Scholar 

  122. Mitrofanov A, Parsheva K, Nosenko V (2021) Simulation of an artificial neural network for predicting temperature and cutting force during grinding using CAMQL. Mater Today Proc 38:1508–1511. https://doi.org/10.1016/j.matpr.2020.08.139

    Article  Google Scholar 

  123. Sato M, Ueda T, Tanaka H (2007) An experimental technique for the measurement of temperature on CBN tool face in end milling. Int J Mach Tools Manuf 47:2071–2076. https://doi.org/10.1016/j.ijmachtools.2007.05.006

    Article  Google Scholar 

  124. Hong SY, Ding Y (2001) Cooling approaches and cutting temperatures in cryogenic machining of Ti-6Al-4V. Int J Mach Tools Manuf 41:1417–1437. https://doi.org/10.1016/S0890-6955(01)00026-8

    Article  Google Scholar 

  125. Tsai C-H, Lin B-C (2007) Laser cutting with controlled fracture and pre-bending applied to LCD glass separation. Int J Adv Manuf Technol 32:1155–1162. https://doi.org/10.1007/s00170-006-0422-9

    Article  Google Scholar 

  126. Cao X-F, Woo W-S, Lee C-M (2020) A study on the laser-assisted milling of 13–8 stainless steel for optimal machining. Opt Laser Technol 132:106473. https://doi.org/10.1016/j.optlastec.2020.106473

    Article  Google Scholar 

  127. Liu C, He Y, Wang Y et al (2020) Effects of process parameters on cutting temperature in dry machining of ball screw. ISA Trans 101:493–502. https://doi.org/10.1016/j.isatra.2020.01.031

    Article  Google Scholar 

  128. Fu S, Kor WS, Cheng F, Seah LK (2020) In-situ measurement of surface roughness using chromatic confocal sensor. Procedia CIRP 94:780–784. https://doi.org/10.1016/j.procir.2020.09.133

  129. Ali MM, Ibrahim AF (2021) Influence of machining parameters on surface roughness in wire EDM using zinc coated brass wire. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.10.276

    Article  Google Scholar 

  130. Karthikeyan S, Subbarayan MR, Beemaraj RK, Sivakandhan C (2021) Computer vision-based surface roughness measurement using artificial neural network. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.09.314

    Article  Google Scholar 

  131. Sanjeevi R, Nagaraja R, Radha Krishnan B (2021) Vision-based surface roughness accuracy prediction in the CNC milling process (Al6061) using ANN. Mater Today Proc 37:245–247. https://doi.org/10.1016/j.matpr.2020.05.122

    Article  Google Scholar 

  132. Kittali P, Kalwa V, Athith D et al (2021) Optimization of machining parameters in turning operation to minimize the surface roughness using Taguchi technique for EN1A alloy steel. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.10.323

    Article  Google Scholar 

  133. Korkut I, Kasap M, Ciftci I, Seker U (2004) Determination of optimum cutting parameters during machining of AISI 304 austenitic stainless steel. Mater Des 25:303–305. https://doi.org/10.1016/j.matdes.2003.10.011

    Article  Google Scholar 

  134. Manivel D, Gandhinathan R (2016) Optimization of surface roughness and tool wear in hard turning of austempered ductile iron (grade 3) using Taguchi method. Measurement 93:108–116. https://doi.org/10.1016/j.measurement.2016.06.055

    Article  Google Scholar 

  135. Özel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45:467–479. https://doi.org/10.1016/j.ijmachtools.2004.09.007

    Article  Google Scholar 

  136. Kıvak T (2014) Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts. Measurement 50:19–28. https://doi.org/10.1016/J.MEASUREMENT.2013.12.017

    Article  Google Scholar 

  137. Shah DR, Pancholi N, Gajera H, Patel B (2021) Investigation of cutting temperature, cutting force and surface roughness using multi-objective optimization for turning of Ti-6Al-4 V (ELI). Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.08.285

    Article  Google Scholar 

  138. Fetecau C, Stan F (2012) Study of cutting force and surface roughness in the turning of polytetrafluoroethylene composites with a polycrystalline diamond tool. Measurement 45:1367–1379. https://doi.org/10.1016/j.measurement.2012.03.030

    Article  Google Scholar 

  139. Bagaber SA, Yusoff AR (2017) Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316. J Clean Prod 157:30–46. https://doi.org/10.1016/j.jclepro.2017.03.231

    Article  Google Scholar 

  140. Narayanan V, Singh R, Marla D (2021) A computational model to predict surface roughness in laser surface processing of mild steel using nanosecond pulses. J Manuf Process 68:1880–1889. https://doi.org/10.1016/j.jmapro.2021.07.016

    Article  Google Scholar 

  141. Saxena KK, Bellotti M, Qian J, Reynaerts D (2018) Characterization of circumferential surface roughness of micro-EDMed holes using replica technology. Procedia CIRP 68:582–587. https://doi.org/10.1016/j.procir.2017.12.118

    Article  Google Scholar 

  142. Davim J, Reis P (2003) Study of delamination in drilling carbon fiber reinforced plastics (CFRP) using design experiments. Compos Struct 59:481–487. https://doi.org/10.1016/S0263-8223(02)00257-X

    Article  Google Scholar 

  143. Gowda BMU, Ravindra HV, Ullas M et al (2014) Estimation of circularity, cylindricity and surface roughness in drilling Al-Si 3 N 4 metal matrix composites using artificial neural network. Procedia Mater Sci 6:1780–1787. https://doi.org/10.1016/j.mspro.2014.07.208

    Article  Google Scholar 

  144. Paturi UMR, Yash A, Teja Palakurthy S, Reddy NS (2021) Modeling and optimization of machining parameters for minimizing surface roughness and tool wear during AISI 52100 steel dry turning. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.08.047

    Article  Google Scholar 

  145. Unver HO, Sener B (2021) A novel transfer learning framework for chatter detection using convolutional neural networks. J Intell Manuf. https://doi.org/10.1007/s10845-021-01839-3

    Article  Google Scholar 

  146. Liu D, Luo M, Urbikain Pelayo G et al (2021) Position-oriented process monitoring in milling of thin-walled parts. J Manuf Syst 60:360–372. https://doi.org/10.1016/j.jmsy.2021.06.010

  147. Urbikain G, de Lacalle LL (2020) MoniThor: a complete monitoring tool for machining data acquisition based on FPGA programming. SoftwareX 11:100387. https://doi.org/10.1016/j.softx.2019.100387

  148. Wang R, Song Q, Liu Z et al (2021) A novel unsupervised machine learning-based method for chatter detection in the milling of thin-walled parts. Sensors 21

  149. Postel M, Aslan D, Wegener K, Altintas Y (2019) Monitoring of vibrations and cutting forces with spindle mounted vibration sensors. CIRP Ann 68:413–416. https://doi.org/10.1016/j.cirp.2019.03.019

  150. Li Y, Liu C, Hua J et al (2019) A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning. CIRP Ann 68:487–490. https://doi.org/10.1016/j.cirp.2019.03.010

  151. Siddhpura M, Paurobally R (2012) A review of chatter vibration research in turning. Int J Mach Tools Manuf 61:27–47. https://doi.org/10.1016/j.ijmachtools.2012.05.007

    Article  Google Scholar 

  152. Zhang SJ, To S, Wang SJ, Zhu ZW (2015) A review of surface roughness generation in ultra-precision machining. Int J Mach Tools Manuf 91:76–95. https://doi.org/10.1016/j.ijmachtools.2015.02.001

    Article  Google Scholar 

  153. Bhuiyan MSH, Choudhury IA, Dahari M (2014) Monitoring the tool wear, surface roughness and chip formation occurrences using multiple sensors in turning. J Manuf Syst 33:476–487. https://doi.org/10.1016/j.jmsy.2014.04.005

  154. Prasad BS, Babu MP, Reddy YR (2014) Evaluation of correlation between vibration signal features and three-dimensional finite element simulations to predict cutting tool wear in turning operation. Proc Inst Mech Eng Part B J Eng Manuf 230:203–214. https://doi.org/10.1177/0954405414554018

    Article  Google Scholar 

  155. Emami M, Karimipour A (2021) Theoretical and experimental study of the chatter vibration in wet and MQL machining conditions in turning process. Precis Eng 72:41–58. https://doi.org/10.1016/j.precisioneng.2021.04.006

    Article  Google Scholar 

  156. Elangovan M, Sakthivel NR, Saravanamurugan S et al (2015) Machine learning approach to the prediction of surface roughness using statistical features of vibration signal acquired in turning. Procedia Comput Sci 50:282–288. https://doi.org/10.1016/j.procs.2015.04.047

  157. Akkuş H, Yaka H (2021) Experimental and statistical investigation of the effect of cutting parameters on surface roughness, vibration and energy consumption in machining of titanium 6Al-4V ELI (grade 5) alloy. Measurement 167:108465. https://doi.org/10.1016/j.measurement.2020.108465

  158. Türkeş E, Neşeli S (2014) A simple approach to analyze process damping in chatter vibration. Int J Adv Manuf Technol 70:775–786. https://doi.org/10.1007/s00170-013-5307-0

    Article  Google Scholar 

  159. Tsai Y-H, Chen JC, Lou S-J (1999) An in-process surface recognition system based on neural networks in end milling cutting operations. Int J Mach Tools Manuf 39:583–605. https://doi.org/10.1016/S0890-6955(98)00053-4

  160. Alonso FJ, Salgado DR (2008) Analysis of the structure of vibration signals for tool wear detection. Mech Syst Signal Process 22:735–748. https://doi.org/10.1016/j.ymssp.2007.09.012

  161. Salgado DR, Alonso FJ (2006) Tool wear detection in turning operations using singular spectrum analysis. J Mater Process Technol 171:451–458. https://doi.org/10.1016/j.jmatprotec.2005.08.005

  162. Uchino K (2017) Manufacturing technologies for piezoelectric transducers, 2nd ed. Elsevier Ltd

  163. Ding F, He Z (2011) Cutting tool wear monitoring for reliability analysis using proportional hazards model. Int J Adv Manuf Technol 57:565–574. https://doi.org/10.1007/s00170-011-3316-4

    Article  Google Scholar 

  164. Turkes E, Orak S, Neseli S, Yaldiz S (2011) Linear analysis of chatter vibration and stability for orthogonal cutting in turning. Int J Refract Met Hard Mater 29:163–169. https://doi.org/10.1016/j.ijrmhm.2010.10.002

    Article  Google Scholar 

  165. Anderson CS, Semercigil SE, Turan ÖF (2007) A passive adaptor to enhance chatter stability for end mills. Int J Mach Tools Manuf 47:1777–1785. https://doi.org/10.1016/j.ijmachtools.2006.06.020

    Article  Google Scholar 

  166. Turkes E, Orak S, Neseli S, Yaldiz S (2011) A new process damping model for chatter vibration. Measurement 44:1342–1348. https://doi.org/10.1016/j.measurement.2011.04.004

    Article  Google Scholar 

  167. Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51:363–376. https://doi.org/10.1016/j.ijmachtools.2011.01.001

    Article  Google Scholar 

  168. Liu N, Liu B, Jiang H et al (2021) Study on vibration and surface roughness in MQCL turning of stainless steel. J Manuf Process 65:343–353. https://doi.org/10.1016/j.jmapro.2021.03.041

    Article  Google Scholar 

  169. Özbek O, Saruhan H (2020) The effect of vibration and cutting zone temperature on surface roughness and tool wear in eco-friendly MQL turning of AISI D2. J Mater Res Technol 9:2762–2772. https://doi.org/10.1016/j.jmrt.2020.01.010

    Article  Google Scholar 

  170. Singh T, Sharma VK, Rana M et al (2021) GRA based optimization of tool vibration and surface roughness in face milling of hardened steel alloy. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.09.546

    Article  Google Scholar 

  171. Takahashi W, Nakanomiya T, Suzuki N, Shamoto E (2021) Influence of flank texture patterns on the suppression of chatter vibration and flank adhesion in turning operations. Precis Eng 68:262–272. https://doi.org/10.1016/j.precisioneng.2020.12.007

    Article  Google Scholar 

  172. Suzuki N, Takahashi W, Igeta H, Nakanomiya T (2020) Flank face texture design to suppress chatter vibration in cutting. CIRP Ann 69:93–96. https://doi.org/10.1016/j.cirp.2020.04.037

    Article  Google Scholar 

  173. Shankar NVS, Shankar HR, Kumar NP, Saichandu K (2020) Process parameter optimization for minimizing vibrations and surface roughness during turning EN19 steel using coated carbide tool. Mater Today Proc 24:788–797. https://doi.org/10.1016/j.matpr.2020.04.387

    Article  Google Scholar 

  174. Zhang XY, Lu X, Wang S et al (2018) A multi-sensor based online tool condition monitoring system for milling process. Procedia CIRP 72:1136–1141. https://doi.org/10.1016/j.procir.2018.03.092

    Article  Google Scholar 

  175. Sharma V, Pandey PM (2016) Optimization of machining and vibration parameters for residual stresses minimization in ultrasonic assisted turning of 4340 hardened steel. Ultrasonics 70:172–182. https://doi.org/10.1016/j.ultras.2016.05.001

    Article  Google Scholar 

  176. Li Z, Liu R, Wu D (2019) Data-driven smart manufacturing: tool wear monitoring with audio signals and machine learning. J Manuf Process 48:66–76. https://doi.org/10.1016/j.jmapro.2019.10.020

  177. Lattanzi E, Freschi V (2021) Machine learning techniques to ıdentify unsafe driving behavior by means of ın-vehicle sensor data. Expert Syst Appl 176:114818. https://doi.org/10.1016/j.eswa.2021.114818

  178. Wang R, Song Q, Liu Z et al (2022) Multi-condition identification in milling Ti-6Al-4V thin-walled parts based on sensor fusion. Mech Syst Signal Process 164:108264. https://doi.org/10.1016/j.ymssp.2021.108264

    Article  Google Scholar 

  179. Zhou C, Guo K, Sun J (2021) Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing. Mech Syst Signal Process 157:107738. https://doi.org/10.1016/j.ymssp.2021.107738

  180. Biermann D, Zabel A, Brüggemann T, Barthelmey A (2013) A comparison of low cost structure-borne sound measurement and acceleration measurement for detection of workpiece vibrations in 5-axis simultaneous machining. Procedia CIRP 12:91–96. https://doi.org/10.1016/j.procir.2013.09.017

  181. Frigieri EP, Campos PHS, Paiva AP et al (2016) A mel-frequency cepstral coefficient-based approach for surface roughness diagnosis in hard turning using acoustic signals and gaussian mixture models. Appl Acoust 113:230–237. https://doi.org/10.1016/j.apacoust.2016.06.027

    Article  Google Scholar 

  182. Balsamo V, Caggiano A, Jemielniak K et al (2016) Multi sensor signal processing for catastrophic tool failure detection in turning. Procedia CIRP 41:939–944. https://doi.org/10.1016/j.procir.2016.01.010

    Article  Google Scholar 

  183. Han S, Mannan N, Stein DC et al (2021) Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems. J Manuf Syst 61:45–53. https://doi.org/10.1016/j.jmsy.2021.08.004

    Article  Google Scholar 

  184. Mishra R, Singh B (2021) SB-LMD based online monitoring of tool chatter detection in milling process. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.09.489

    Article  Google Scholar 

  185. Ravikumar S, Ramachandran KI (2018) Tool wear monitoring of multipoint cutting tool using sound signal features signals with machine learning techniques. Mater Today Proc 5:25720–25729. https://doi.org/10.1016/j.matpr.2018.11.014

    Article  Google Scholar 

  186. Niu B, Sun J, Yang B (2020) Multisensory based tool wear monitoring for practical applications in milling of titanium alloy. Mater Today Proc 22:1209–1217. https://doi.org/10.1016/j.matpr.2019.12.126

    Article  Google Scholar 

  187. Carou D, Rubio EM, Lauro CH et al (2017) Study based on sound monitoring as a means for superficial quality control in ıntermittent turning of magnesium workpieces. Procedia CIRP 62:262–268. https://doi.org/10.1016/j.procir.2016.06.061

    Article  Google Scholar 

  188. Yusof MFM, Ishak M, Ghazali MF (2020) Classification of weld penetration condition through synchrosqueezed-wavelet analysis of sound signal acquired from pulse mode laser welding process. J Mater Process Technol 279:116559. https://doi.org/10.1016/j.jmatprotec.2019.116559

    Article  Google Scholar 

  189. Mohanraj T, Yerchuru J, Krishnan H et al (2021) Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms. Measurement 173:108671. https://doi.org/10.1016/j.measurement.2020.108671

    Article  Google Scholar 

  190. Zhou C, Yang B, Guo K et al (2020) Vibration singularity analysis for milling tool condition monitoring. Int J Mech Sci 166:105254. https://doi.org/10.1016/j.ijmecsci.2019.105254

    Article  Google Scholar 

  191. Uekita M, Takaya Y (2017) Tool condition monitoring technique for deep-hole drilling of large components based on chatter identification in time–frequency domain. Measurement 103:199–207. https://doi.org/10.1016/j.measurement.2017.02.035

    Article  Google Scholar 

  192. Sharma VS, Sharma SK, Sharma AK (2008) Cutting tool wear estimation for turning. J Intell Manuf 19:99–108. https://doi.org/10.1007/s10845-007-0048-2

    Article  Google Scholar 

  193. Lee WJ, Mendis GP, Sutherland JW (2019) Development of an ıntelligent tool condition monitoring system to ıdentify manufacturing tradeoffs and optimal machining conditions. Procedia Manuf 33:256–263. https://doi.org/10.1016/j.promfg.2019.04.031

    Article  Google Scholar 

  194. Dai Y, Zhu K (2018) A machine vision system for micro-milling tool condition monitoring. Precis Eng 52:183–191. https://doi.org/10.1016/j.precisioneng.2017.12.006

    Article  Google Scholar 

  195. Geng D, Lu Z, Yao G et al (2017) Cutting temperature and resulting influence on machining performance in rotary ultrasonic elliptical machining of thick CFRP. Int J Mach Tools Manuf 123:160–170. https://doi.org/10.1016/j.ijmachtools.2017.08.008

    Article  Google Scholar 

  196. Sevilla-Camacho PY, Robles-Ocampo JB, Jauregui-Correa JC, Jimenez-Villalobos D (2015) FPGA-based reconfigurable system for tool condition monitoring in high-speed machining process. Measurement 64:81–88. https://doi.org/10.1016/j.measurement.2014.12.037

    Article  Google Scholar 

  197. Mali R, Telsang MT, Gupta TVK (2017) Real time tool wear condition monitoring in hard turning of Inconel 718 using sensor fusion system. Mater Today Proc 4:8605–8612. https://doi.org/10.1016/j.matpr.2017.07.208

    Article  Google Scholar 

  198. Elangovan M, Devasenapati SB, Sakthivel NR, Ramachandran KI (2011) Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm. Expert Syst Appl 38:4450–4459. https://doi.org/10.1016/j.eswa.2010.09.116

    Article  Google Scholar 

  199. Kuntoğlu M, Sağlam H (2021) Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Measurement 173:108582. https://doi.org/10.1016/j.measurement.2020.108582

    Article  Google Scholar 

  200. Zhang C, Zhang J (2013) On-line tool wear measurement for ball-end milling cutter based on machine vision. Comput Ind 64:708–719. https://doi.org/10.1016/j.compind.2013.03.010

    Article  Google Scholar 

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Funding

The authors would like to thanks “Polısh Natıonal Agency For Academıc Exchange (NAWA) No. PPN/ULM/2020/1/00121” and National Science Center (NCN) Project No. UMO-2020/37/K/ST8/02795 for financial supports.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Karabük University, Karabük, Turkey

    Mehmet Erdi Korkmaz

  2. Faculty of Mechanical Engineering, Opole University of Technology, 76 Proszkowska St, 45-758, Opole, Poland

    Munish Kumar Gupta, Zhixiong Li & Grzegorz M. Krolczyk

  3. Mechanical Engineering Department, Selcuk University, Technology Faculty, Konya, 42130, Turkey

    Mustafa Kuntoğlu & Rüstem Binali

  4. Yenice Vocational High School, Karabük University, Karabük, Turkey

    Nafiz Yaşar

  5. Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk, 454080, Russia

    Danil Yu. Pimenov

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Korkmaz, M.E., Gupta, M.K., Li, Z. et al. Indirect monitoring of machining characteristics via advanced sensor systems: a critical review. Int J Adv Manuf Technol 120, 7043–7078 (2022). https://doi.org/10.1007/s00170-022-09286-x

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