Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
The International Journal of Advanced Manufacturing Technology
Erschienen in: The International Journal of Advanced Manufacturing Technology 9-10/2021

06.07.2021 | Critical Review

A state-of-the-art review on sensors and signal processing systems in mechanical machining processes

verfasst von: Mustafa Kuntoğlu, Emin Salur, Munish Kumar Gupta, Murat Sarıkaya, Danil Yu. Pimenov

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 9-10/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Sensors are the main equipment of the data-based enterprises for diagnosis of the health of system. Offering time- or frequency-dependent systemic information provides prognosis with the help of early-warning system using intelligent signal processing systems. Therefore, a chain of data-based information improves the efficiency especially focusing on the determination of remaining useful life of a machine or tool. A broad utilization of sensors in machining processes and artificial intelligence–supported data analysis and signal processing systems are prominent technological tools in the way of Industry 4.0. Therefore, this paper outlines the state of the art of the mentioned systems encountered in the open literature. As a result, existing studies using sensor systems including signal processing facilities in machining processes provide important contribution for error minimization and productivity maximization. However, there is a need for improved adaptive control systems for faster convergence and physical intervention in case of possible problems and failures. On the other hand, sensor fusion is an innovative new technology that makes decisions using multi-sensor information to determine tool status and predict system stability. It is currently not a fully accepted and practiced method. In a nutshell, despite their numerous advantages in terms of efficiency, time saving, and cost, the current situation of sensors used in the industry is not a sufficient level due to the investment cost and its increase with additional signal acquisition hardware and software equipment. Therefore, more studies that can contribute to the literature are needed.
Nächster Artikel Finite element methods used in clinching process

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Kuntoğlu M, Aslan A, Sağlam H, Pimenov DY, Giasin K, Mikolajczyk T (2020) Optimization and analysis of surface roughness, flank wear and 5 different sensorial data via tool condition monitoring system in turning of AISI 5140. Sensors 20(16):4377. https://​doi.​org/​10.​3390/​s20164377CrossRef
2.
Duan Z, Wu T, Guo S, Shao T, Malekian R, Li Z (2018) Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review. Int J Adv Manuf Technol 96(1):803–819CrossRef
3.
Tlusty J, Andrews G (1983) A critical review of sensors for unmanned machining. CIRP Ann 32(2):563–572CrossRef
4.
Zhou Y, Xue W (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96(5):2509–2523CrossRef
5.
Wu Y, Du R (1996) Feature extraction and assessment using wavelet packets for monitoring of machining processes. Mech Syst Signal Process 10(1):29–53CrossRef
6.
Bustillo A, Pimenov DY, Mia M, Kapłonek W (2020) Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth. J Intell Manuf:1–18
7.
Mohanraj T, Shankar S, Rajasekar R, Sakthivel N, Pramanik A (2020) Tool condition monitoring techniques in milling process—a review. J Mater Res Technol 9(1):1032–1042CrossRef
8.
Fallah M, Moetakef-Imani B (2019) Adaptive inverse control of chatter vibrations in internal turning operations. Mech Syst Signal Process 129:91–111CrossRef
9.
Kumar P, Chauhan SR, Pruncu CI, Gupta MK, Pimenov DY, Mia M, Gill HS (2019) Influence of different grades of CBN inserts on cutting force and surface roughness of AISI H13 die tool steel during hard turning operation. Materials 12(1):177CrossRef
10.
Brunete A, Gambao E, Koskinen J, Heikkilä T, Kaldestad KB, Tyapin I, Hovland G, Surdilovic D, Hernando M, Bottero A (2018) Hard material small-batch industrial machining robot. Robot Comput Integr Manuf 54:185–199CrossRef
11.
Das A, Pradhan O, Patel SK, Das SR, Biswal BB (2019) Performance appraisal of various nanofluids during hard machining of AISI 4340 steel. J Manuf Process 46:248–270CrossRef
12.
13.
Deif AM (2011) A system model for green manufacturing. J Clean Prod 19(14):1553–1559CrossRef
14.
Salem A, Hegab H, Kishawy HA (2021) An integrated approach for sustainable machining processes: assessment, performance analysis, and optimization. Sustainable Production and Consumption 25:450–470CrossRef
15.
Wickramasinghe K, Sasahara H, Abd Rahim E, Perera G (2020) Green Metalworking Fluids for sustainable machining applications: a review. J Clean Prod 257:120552CrossRef
16.
Gupta MK, Song Q, Liu Z, Sarikaya M, Jamil M, Mia M, Singla AK, Khan AM, Khanna N, Pimenov DY (2021) Environment and economic burden of sustainable cooling/lubrication methods in machining of Inconel-800. J Clean Prod 287:125074CrossRef
17.
Jamil M, Zhao W, He N, Gupta MK, Sarikaya M, Khan AM, Siengchin S, Pimenov DY (2021) Sustainable milling of Ti–6Al–4V: a trade-off between energy efficiency, carbon emissions and machining characteristics under MQL and cryogenic environment. J Clean Prod 281:125374CrossRef
18.
Kuntoğlu M, Sağlam H (2021) Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Measurement 173:108582CrossRef
19.
Mouli KC, Prasad BS, Sridhar A, Alanka S (2020) A review on multi sensor data fusion technique in CNC machining of tailor-made nanocomposites. SN Applied Sciences 2(5):1–12CrossRef
20.
Jovic S, Anicic O, Jovanovic M (2017) Adaptive neuro-fuzzy fusion of multi-sensor data for monitoring of CNC machining. Sens Rev
21.
Monka PP, Monkova K, Majstorovic VD, Božić Ž, Andrej A (2020) Optimal cutting parameter specification of newly designed milling tools based on the frequency monitoring. Int J Adv Manuf Technol:1–18
22.
Teti R (2015) Advanced IT methods of signal processing and decision making for zero defect manufacturing in machining. Procedia CIRP 28:3–15CrossRef
23.
Ratava J, Lohtander M, Varis J (2017) Tool condition monitoring in interrupted cutting with acceleration sensors. Robot Comput Integr Manuf 47:70–75CrossRef
24.
Ranjan J, Patra K, Szalay T, Mia M, Gupta MK, Song Q, Krolczyk G, Chudy R, Pashnyov VA, Pimenov DY (2020) Artificial intelligence-based hole quality prediction in micro-drilling using multiple sensors. Sensors 20(3):885CrossRef
25.
K-f Z, H-q Y, Nie P (2015) A method for tool condition monitoring based on sensor fusion. J Intell Manuf 26(5):1011–1026CrossRef
26.
Li N, Chen Y, Kong D, Tan S (2017) Force-based tool condition monitoring for turning process using v-support vector regression. Int J Adv Manuf Technol 91(1-4):351–361CrossRef
27.
Yang B, Guo K, Liu J, Sun J, Song G, Zhu S, Sun C, Jiang Z (2020) Vibration singularity analysis for milling tool condition monitoring. Int J Mech Sci 166:105254CrossRef
28.
Alexandre FA, Lopes WN, Dotto FRL, Ferreira FI, Aguiar PR, Bianchi EC, Lopes JC (2018) Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model. Int J Adv Manuf Technol 96(1):67–79CrossRef
29.
Moia D, Thomazella I, Aguiar P, Bianchi E, Martins C, Marchi M (2015) Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks. J Braz Soc Mech Sci Eng 37(2):627–640CrossRef
30.
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 103:199–207CrossRef
31.
Patra K, Jha A, Szalay T, Ranjan J, Monostori L (2017) Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals. Precis Eng 48:279–291CrossRef
32.
Bhuiyan M, Choudhury IA, Dahari M, Nukman Y, Dawal S (2016) Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement 92:208–217CrossRef
33.
Kuntoğlu M, Aslan A, Pimenov DY, Giasin K, Mikolajczyk T, Sharma S (2020) Modeling of cutting parameters and tool geometry for multi-criteria optimization of surface roughness and vibration via response surface methodology in turning of AISI 5140 steel. Materials 13(19):4242CrossRef
34.
Caggiano A (2018) Tool wear prediction in Ti-6Al-4V machining through multiple sensor monitoring and PCA features pattern recognition. Sensors 18(3):823CrossRef
35.
Xie Z, Li J, Lu Y (2019) Feature selection and a method to improve the performance of tool condition monitoring. Int J Adv Manuf Technol 100(9):3197–3206CrossRef
36.
Azmi A (2015) Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites. Adv Eng Softw 82:53–64CrossRef
37.
Kene AP, Choudhury SK (2019) Analytical modeling of tool health monitoring system using multiple sensor data fusion approach in hard machining. Measurement 145:118–129CrossRef
38.
Mehta P, Werner A, Mears L (2015) Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion. J Intell Manuf 26(2):331–346CrossRef
39.
Liu C, Zhang L, Niu J, Yao R, Wu C (2020) Intelligent prognostics of machining tools based on adaptive variational mode decomposition and deep learning method with attention mechanism. Neurocomputing 417:239–254CrossRef
40.
Liang Y, Li W, Lu X, Wang S (2019) Fog computing and convolutional neural network enabled prognosis for machining process optimization. J Manuf Syst 52:32–42CrossRef
41.
Benkedjouh T, Medjaher K, Zerhouni N, Rechak S (2015) Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 26(2):213–223CrossRef
42.
Yu J (2018) Tool condition prognostics using logistic regression with penalization and manifold regularization. Appl Soft Comput 64:454–467CrossRef
43.
Wu J, Su Y, Cheng Y, Shao X, Deng C, Liu C (2018) Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system. Appl Soft Comput 68:13–23CrossRef
44.
Duro JA, Padget JA, Bowen CR, Kim HA, Nassehi A (2016) Multi-sensor data fusion framework for CNC machining monitoring. Mech Syst Signal Process 66:505–520CrossRef
45.
Albertelli P, Goletti M, Torta M, Salehi M, Monno M (2016) Model-based broadband estimation of cutting forces and tool vibration in milling through in-process indirect multiple-sensors measurements. Int J Adv Manuf Technol 82(5-8):779–796CrossRef
46.
Liu C, Li Y, Zhou G, Shen W (2018) A sensor fusion and support vector machine based approach for recognition of complex machining conditions. J Intell Manuf 29(8):1739–1752CrossRef
47.
Zhou Y, Xue W (2018) A multisensor fusion method for tool condition monitoring in milling. Sensors 18(11):3866CrossRef
48.
Garcia Plaza E, Nunez Lopez P, Beamud Gonzalez E (2018) Multi-sensor data fusion for real-time surface quality control in automated machining systems. Sensors 18(12):4381CrossRef
49.
Zhang C, Yao X, Zhang J, Jin H (2016) Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors 16(6):795CrossRef
50.
Gupta MK, Song Q, Liu Z, Sarikaya M, Mia M, Jamil M, Singla AK, Bansal A, Pimenov DY, Kuntoğlu M (2021) Tribological performance based machinability investigations in cryogenic cooling assisted turning of α-β titanium alloy. Tribol Int:107032
51.
Gupta MK, Song Q, Liu Z, Sarikaya M, Jamil M, Mia M, Khanna N, Krolczyk GM (2021) Experimental characterisation of the performance of hybrid cryo-lubrication assisted turning of Ti–6Al–4V alloy. Tribol Int 153:106582CrossRef
52.
Kumar J, Verma RK (2021) A novel methodology of Combined Compromise Solution and Principal Component Analysis (CoCoSo-PCA) for machinability investigation of graphene nanocomposites. CIRP J Manuf Sci Technol 33:143–157CrossRef
53.
Lyu Y, Jamil M, He N, Gupta MK, Pimenov DY (2021) Development and testing of a high-frequency dynamometer for high-speed milling process. Machines 9(1):11CrossRef
54.
Pimenov DY, Hassui A, Wojciechowski S, Mia M, Magri A, Suyama DI, Bustillo A, Krolczyk G, Gupta MK (2019) Effect of the relative position of the face milling tool towards the workpiece on machined surface roughness and milling dynamics. Appl Sci 9(5):842CrossRef
55.
Kapłonek W, Mikolajczyk T, Pimenov DY, Gupta MK, Mia M, Sharma S, Patra K, Sutowska M (2020) High-accuracy 3D optical profilometry for analysis of surface condition of modern circulated coins. Materials 13(23):5371CrossRef
56.
Şap E, Usca UA, Gupta MK, Kuntoğlu M (2021) Tool wear and machinability investigations in dry turning of Cu/Mo-SiC p hybrid composites. Int J Adv Manuf Technol 114(1):379–396CrossRef
57.
Şap E, Usca ÜA, Gupta MK, Kuntoğlu M, Sarıkaya M, Pimenov DY, Mia M (2021) Parametric optimization for improving the machining process of Cu/Mo-SiCP composites produced by powder metallurgy. Materials 14(8):1921CrossRef
58.
Bazaz SM, Lohtander M, Varis J (2020) The prediction method of tool life on small lot turning process–development of Digital Twin for production. Procedia Manuf 51:288–295CrossRef
59.
Lee J, Bagheri B, Kao H-A (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23CrossRef
60.
Baheti R, Gill H (2011) The impact of control technology: cyber-physical systems. IEEE Control Systems Society:1–6
61.
Shi J, Wan J, Yan H, Suo H A (2011) survey of cyber-physical systems. In: 2011 international conference on wireless communications and signal processing (WCSP). IEEE, pp 1–6
62.
Lee J, Porretta DL (2013) Enhancing the motor skills of children with autism spectrum disorders: a pool-based approach. Journal of Physical Education, Recreation & Dance 84(1):41–45CrossRef
63.
Bazaz SM, Lohtander M, Varis J (2019) 5-dimensional definition for a manufacturing digital twin. Procedia Manuf 38:1705–1712CrossRef
64.
Botkina D, Hedlind M, Olsson B, Henser J, Lundholm T (2018) Digital twin of a cutting tool. Procedia Cirp 72:215–218CrossRef
65.
Rosen R, Von Wichert G, Lo G, Bettenhausen KD (2015) About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48(3):567–572CrossRef
66.
Vachálek J, Bartalský L, Rovný O, Šišmišová D, Morháč M, Lokšík M (2017) The digital twin of an industrial production line within the industry 4.0 concept. In: 2017 21st international conference on process control (PC). IEEE, pp 258-262
67.
Rüßmann M, Lorenz M, Gerbert P, Waldner M, Justus J, Engel P, Harnisch M (2015) Industry 4.0: the future of productivity and growth in manufacturing industries. Boston Consulting Group 9(1):54–89
68.
Bazaz SM, Lohtander M, Varis J (2020) Availability of manufacturing data resources in Digital Twin. Procedia Manuf 51:1125–1131CrossRef
69.
Vaidya S, Ambad P, Bhosle S (2018) Industry 4.0–a glimpse. Procedia Manuf 20:233–238CrossRef
70.
Lasi H, Fettke P, Kemper H-G, Feld T, Hoffmann M (2014) Industry 4.0. Bus Inf Syst Eng 6(4):239–242CrossRef
71.
Bi Z, Da Xu L, Wang C (2014) Internet of things for enterprise systems of modern manufacturing. IEEE Trans Industr Inform 10(2):1537–1546CrossRef
72.
Kuntoğlu M, Aslan A, Pimenov DY, Usca ÜA, Salur E, Gupta MK, Mikolajczyk T, Giasin K, Kapłonek W, Sharma S (2020) A review of indirect tool condition monitoring systems and decision-making methods in turning: critical analysis and trends. Sensors 21(1):108CrossRef
73.
Oborski P (2014) Developments in integration of advanced monitoring systems. Int J Adv Manuf Technol 75(9-12):1613–1632CrossRef
74.
Serin G, Sener B, Ozbayoglu A, Unver H (2020) Review of tool condition monitoring in machining and opportunities for deep learning. Int J Adv Manuf Technol:1–22
75.
Baur M, Albertelli P, Monno M (2020) A review of prognostics and health management of machine tools. Int J Adv Manuf Technol 107(5):2843–2863CrossRef
76.
Uzun M, Usca UA (2018) Effect of Cr particulate reinforcements in different ratios on wear performance and mechanical properties of Cu matrix composites. J Braz Soc Mech Sci Eng 40(4):197CrossRef
77.
Mikołajczyk T, Nowicki K, Bustillo A, Pimenov DY (2018) Predicting tool life in turning operations using neural networks and image processing. Mech Syst Signal Process 104:503–513CrossRef
78.
Mikołajczyk T, Nowicki K, Kłodowski A, Pimenov DY (2017) Neural network approach for automatic image analysis of cutting edge wear. Mech Syst Signal Process 88:100–110CrossRef
79.
Pimenov DY, Bustillo A, Mikolajczyk T (2018) Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. J Intell Manuf 29(5):1045–1061CrossRef
80.
Kuntoğlu M, Sağlam H (2019) Investigation of progressive tool wear for determining of optimized machining parameters in turning. Measurement 140:427–436CrossRef
81.
Laghari RA, Li J, Mia M (2020) Effects of turning parameters and parametric optimization of the cutting forces in machining SiCp/Al 45 wt% composite. Metals 10(6):840CrossRef
82.
Karimi NZ, Minak G, Kianfar P (2015) Analysis of damage mechanisms in drilling of composite materials by acoustic emission. Compos Struct 131:107–114CrossRef
83.
Chen J, Chen H, Xu J, Wang J, Zhang X, Chen X (2018) Acoustic signal-based tool condition monitoring in belt grinding of nickel-based superalloys using RF classifier and MLR algorithm. Int J Adv Manuf Technol 98(1):859–872CrossRef
84.
Stavropoulos P, Papacharalampopoulos A, Vasiliadis E, Chryssolouris G (2016) Tool wear predictability estimation in milling based on multi-sensorial data. Int J Adv Manuf Technol 82(1-4):509–521CrossRef
85.
Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Comput Ind 34(1):55–72CrossRef
86.
Altintas Y, Aslan D (2017) Integration of virtual and on-line machining process control and monitoring. CIRP Ann 66(1):349–352CrossRef
87.
Gomez M, Schmitz T (2020) Low-cost, constrained-motion dynamometer for milling force measurement. Manuf Lett 25:34–39CrossRef
88.
Bhuiyan M, Choudhury I, Dahari M (2014) Monitoring the tool wear, surface roughness and chip formation occurrences using multiple sensors in turning. J Manuf Syst 33(4):476–487CrossRef
89.
Wang H, Hu Y, Cong W, Hu Z (2019) A mechanistic model on feeding-directional cutting force in surface grinding of CFRP composites using rotary ultrasonic machining with horizontal ultrasonic vibration. Int J Mech Sci 155:450–460CrossRef
90.
Sivalingam V, Zhao Y, Thulasiram R, Sun J, Thangapandian N (2021) Machining behaviour, surface integrity and tool wear analysis in environment friendly turning of Inconel 718 alloy. Measurement:109028
91.
Pandiyan V, Caesarendra W, Tjahjowidodo T, Tan HH (2018) In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. J Manuf Process 31:199–213CrossRef
92.
Luo M, Chong Z, Liu D (2018) Cutting forces measurement for milling process by using working tables with integrated PVDF thin-film sensors. Sensors 18(11):4031CrossRef
93.
Xiao X, Zheng K, Liao W, Meng H (2016) Study on cutting force model in ultrasonic vibration assisted side grinding of zirconia ceramics. Int J Mach Tools Manuf 104:58–67CrossRef
94.
Díaz-Álvarez J, Tapetado A, Vázquez C, Miguélez H (2017) Temperature measurement and numerical prediction in machining inconel 718. Sensors 17(7):1531CrossRef
95.
Liu S, Chen T, Wu C (2017) Rotary ultrasonic face grinding of carbon fiber reinforced plastic (CFRP): a study on cutting force model. Int J Adv Manuf Technol 89(1-4):847–856CrossRef
96.
Ahmed YS, Arif A, Veldhuis SC (2020) Application of the wavelet transform to acoustic emission signals for built-up edge monitoring in stainless steel machining. Measurement 154:107478CrossRef
97.
Wang B, Liu Z (2017) Acoustic emission signal analysis during chip formation process in high speed machining of 7050-T7451 aluminum alloy and Inconel 718 superalloy. J Manuf Process 27:114–125CrossRef
98.
Chethan Y, Ravindra H, Krishnegowda Y (2019) Optimization of machining parameters in turning Nimonic-75 using machine vision and acoustic emission signals by Taguchi technique. Measurement 144:144–154CrossRef
99.
Ahmed YS, Alam MS, Arif A, Veldhuis S (2019) Use of acoustic emission and cutting force signals to monitor built-up edge formation in stainless steel turning. Int J Adv Manuf Technol 103(5):2257–2276CrossRef
100.
Griffin JM, Diaz F, Geerling E, Clasing M, Ponce V, Taylor C, Turner S, Michael EA, Mena FP, Bronfman L (2017) Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals. Mech Syst Signal Process 85:1020–1034CrossRef
101.
Yao Q, Luo M, Zhang D, Wu B (2018) Identification of cutting force coefficients in machining process considering cutter vibration. Mech Syst Signal Process 103:39–59CrossRef
102.
Salur E, Aslan A, Kuntoglu M, Gunes A, Sahin OS (2019) Experimental study and analysis of machinability characteristics of metal matrix composites during drilling. Compos Part B 166:401–413CrossRef
103.
Filippov A, Nikonov AY, Rubtsov V, Dmitriev A, Tarasov SY (2017) Vibration and acoustic emission monitoring the stability of peakless tool turning: experiment and modeling. J Mater Process Technol 246:224–234CrossRef
104.
Neslušan M, Mičieta B, Mičietová A, Čilliková M, Mrkvica I (2015) Detection of tool breakage during hard turning through acoustic emission at low removal rates. Measurement 70:1–13CrossRef
105.
Geng D, Lu Z, Yao G, Liu J, Li Z, Zhang D (2017) Cutting temperature and resulting influence on machining performance in rotary ultrasonic elliptical machining of thick CFRP. Int J Mach Tools Manuf 123:160–170CrossRef
106.
Prasad BS, Babu MP (2017) Correlation between vibration amplitude and tool wear in turning: numerical and experimental analysis. Eng Sci Technol Int J 20(1):197–211
107.
Suárez A, Veiga F, de Lacalle LL, Polvorosa R, Wretland A (2019) An investigation of cutting forces and tool wear in turning of Haynes 282. J Manuf Process 37:529–540CrossRef
108.
Ning F, Cong W, Wang H, Hu Y, Hu Z, Pei Z (2017) Surface grinding of CFRP composites with rotary ultrasonic machining: a mechanistic model on cutting force in the feed direction. Int J Adv Manuf Technol 92(1):1217–1229CrossRef
109.
Qiu X, Li P, Niu Q, Chen A, Ouyang P, Li C, Ko TJ (2018) Influence of machining parameters and tool structure on cutting force and hole wall damage in drilling CFRP with stepped drills. Int J Adv Manuf Technol 97(1):857–865CrossRef
110.
Kara F, Aslantaş K, Cicek A (2016) Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network. Appl Soft Comput 38:64–74CrossRef
111.
Wang C, Ming W, Chen M (2016) Milling tool’s flank wear prediction by temperature dependent wear mechanism determination when machining Inconel 182 overlays. Tribol Int 104:140–156CrossRef
112.
Cui D, Zhang D, Wu B, Luo M (2017) An investigation of tool temperature in end milling considering the flank wear effect. Int J Mech Sci 131:613–624CrossRef
113.
Abainia S, Ouelaa N (2015) Experimental study of the combined influence of the tool geometry parameters on the cutting forces and tool vibrations. Int J Adv Manuf Technol 79(5-8):1127–1138CrossRef
114.
Balaji M, Rao KV, Rao NM, Murthy B (2018) Optimization of drilling parameters for drilling of TI-6Al-4V based on surface roughness, flank wear and drill vibration. Measurement 114:332–339CrossRef
115.
Gopal P, Prakash KS (2018) Minimization of cutting force, temperature and surface roughness through GRA, TOPSIS and Taguchi techniques in end milling of Mg hybrid MMC. Measurement 116:178–192CrossRef
116.
Yi S, Li J, Zhu J, Wang X, Mo J, Ding S (2020) Investigation of machining Ti-6Al-4V with graphene oxide nanofluids: tool wear, cutting forces and cutting vibration. J Manuf Process 49:35–49CrossRef
117.
Karabulut Ş (2015) Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method. Measurement 66:139–149CrossRef
118.
Chen Y, Sun R, Gao Y, Leopold J (2017) A nested-ANN prediction model for surface roughness considering the effects of cutting forces and tool vibrations. Measurement 98:25–34CrossRef
119.
Soylu A (2007) Investigation of thrust force and torque couple of tool-workpiece (HSS-Ç1040) by the design and manufacturing of drilling dynamometer. Master Degree Thesis, Selcuk University Institute of Natural Science and Technology
120.
Sağlam H (2000) Tool condition monitoring, based on multi-component force measurements using artificial neural network in milling. Phd Thesis, Selçuk University, institute of science
121.
Totis G, Sortino M (2011) Development of a modular dynamometer for triaxial cutting force measurement in turning. Int J Mach Tools Manuf 51(1):34–42CrossRef
122.
Liang Q, Zhang D, Wu W, Zou K (2016) Methods and research for multi-component cutting force sensing devices and approaches in machining. Sensors 16(11):1926CrossRef
123.
Kishawy H, Hegab H, Umer U, Mohany A (2018) Application of acoustic emissions in machining processes: analysis and critical review. Int J Adv Manuf Technol 98(5):1391–1407CrossRef
124.
Gierlak P, Burghardt A, Szybicki D, Szuster M, Muszyńska M (2017) On-line manipulator tool condition monitoring based on vibration analysis. Mech Syst Signal Process 89:14–26CrossRef
125.
Tamang S, Chandrasekaran M, Sahoo A (2018) Sustainable machining: an experimental investigation and optimization of machining Inconel 825 with dry and MQL approach. J Braz Soc Mech Sci Eng 40(8):1–18CrossRef
126.
Moradnazhad M, Unver HO (2017) Energy consumption characteristics of turn-mill machining. Int J Adv Manuf Technol 91(5):1991–2016CrossRef
127.
Khan AM, Jamil M, Mia M, He N, Zhao W, Gong L (2020) Sustainability-based performance evaluation of hybrid nanofluid assisted machining. J Clean Prod 257:120541CrossRef
128.
Balogun VA, Edem IF, Adekunle AA, Mativenga PT (2016) Specific energy based evaluation of machining efficiency. J Clean Prod 116:187–197CrossRef
129.
Arriaza OV, Kim D-W, Lee DY, Suhaimi MA (2017) Trade-off analysis between machining time and energy consumption in impeller NC machining. Robot Comput Integr Manuf 43:164–170CrossRef
130.
Zhou Y, Orban P, Nikumb S (1995) Sensors for intelligent machining-a research and application survey. In: 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century. IEEE, pp 1005-1010
131.
Sheikh-Ahmad J, Almaskari F, Hafeez F (2019) Thermal aspects in machining CFRPs: effect of cutter type and cutting parameters. Int J Adv Manuf Technol 100(9):2569–2582CrossRef
132.
Coromant S (1994) Modern metal cutting: a practical handbook. Sandvik Coromant
133.
Shang Z, Liao Z, Sarasua JA, Billingham J, Axinte D (2019) On modelling of laser assisted machining: forward and inverse problems for heat placement control. Int J Mach Tools Manuf 138:36–50CrossRef
134.
Heigel JC, Whitenton E, Lane B, Donmez MA, Madhavan V, Moscoso-Kingsley W (2017) Infrared measurement of the temperature at the tool–chip interface while machining Ti–6Al–4V. J Mater Process Technol 243:123–130CrossRef
135.
Núñez-Cascajero A, Tapetado A, Vargas S, Vázquez C (2021) Optical fiber pyrometer designs for temperature measurements depending on object size. Sensors 21(2):646CrossRef
136.
Vázquez C, Pérez-Prieto S, López-Cardona JD, Tapetado A, Blanco E, Moreno-López J, Montero DS, Lallana PC (2018) Fiber-optic pyrometer with optically powered switch for temperature measurements. Sensors 18(2):483CrossRef
137.
Li T, Shi T, Tang Z, Liao G, Han J, Duan J (2020) Temperature monitoring of the tool-chip interface for PCBN tools using built-in thin-film thermocouples in turning of titanium alloy. J Mater Process Technol 275:116376CrossRef
138.
Santhanakrishnan M, Sivasakthivel P, Sudhakaran R (2017) Modeling of geometrical and machining parameters on temperature rise while machining Al 6351 using response surface methodology and genetic algorithm. J Braz Soc Mech Sci Eng 39(2):487–496CrossRef
139.
Li J, Tao B, Huang S, Yin Z (2018) Built-in thin film thermocouples in surface textures of cemented carbide tools for cutting temperature measurement. Sensors Actuators A Phys 279:663–670CrossRef
140.
Abbas AT, Benyahia F, El Rayes MM, Pruncu C, Taha MA, Hegab H (2019) Towards optimization of machining performance and sustainability aspects when turning AISI 1045 steel under different cooling and lubrication strategies. Materials 12(18):3023CrossRef
141.
Mia M, Gupta MK, Pruncu CI, Sen B, Khan AM, Jamil M, Faraz S, Asef F, Imran GS, Rahman MA (2020) Six sigma optimization of multiple machining characteristics in hard turning under dry, flood, MQL and solid lubrication. Journal of Production Systems and Manufacturing Science 1(1):6–6
142.
Caggiano A, Segreto T, Teti R (2016) Cloud manufacturing framework for smart monitoring of machining. Procedia Cirp 55:248–253CrossRef
143.
Lu Z, Wang M, Dai W, Sun J (2019) In-process complex machining condition monitoring based on deep forest and process information fusion. Int J Adv Manuf Technol 104(5):1953–1966CrossRef
144.
Salehi M, Albertelli P, Goletti M, Ripamonti F, Tomasini G, Monno M (2015) Indirect model based estimation of cutting force and tool tip vibrational behavior in milling machines by sensor fusion. Procedia CIRP 33:239–244CrossRef
145.
Seiti H, Hafezalkotob A (2018) Developing pessimistic–optimistic risk-based methods for multi-sensor fusion: an interval-valued evidence theory approach. Appl Soft Comput 72:609–623CrossRef
146.
Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann 59(2):717–739CrossRef
147.
Abellan-Nebot JV, Subirón FR (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1):237–257CrossRef
148.
Lauro C, Brandão L, Baldo D, Reis R, Davim J (2014) Monitoring and processing signal applied in machining processes–a review. Measurement 58:73–86CrossRef
149.
Dai W, Sun J, Chi Y, Lu Z, Xu D, Jiang N (2019) Review of machining equipment reliability analysis methods based on condition monitoring technology. Appl Sci 9(14):2786CrossRef
150.
Slamani M, Chatelain J-F (2019) Assessment of the suitability of industrial robots for the machining of carbon-fiber reinforced polymers (CFRPs). J Manuf Process 37:177–195CrossRef
151.
Zhu K, Hong G, Wong Y, Wang W (2008) Cutting force denoising in micro-milling tool condition monitoring. Int J Prod Res 46(16):4391–4408CrossRef
152.
He G, Wang H, Sang Y, Lv Y (2020) An improved decomposition algorithm of surface topography of machining. Mach Sci Technol 24(5):781–809CrossRef
153.
Bonda AGY, Nanda BK, Jonnalagadda S (2020) Vibration signature based stability studies in internal turning with a wavelet denoising preprocessor. Measurement 154:107520CrossRef
154.
Kim D-H, Kim TJ, Wang X, Kim M, Quan Y-J, Oh JW, Min S-H, Kim H, Bhandari B, Yang I (2018) Smart machining process using machine learning: A review and perspective on machining industry. International Journal of Precision Engineering and Manufacturing-Green Technology 5(4):555–568CrossRef
155.
Haber RE, Jiménez JE, Peres CR, Alique JR (2004) An investigation of tool-wear monitoring in a high-speed machining process. Sensors Actuators A Phys 116(3):539–545CrossRef
156.
Dong J, Subrahmanyam K, San Wong Y, Hong GS, Mohanty A (2006) Bayesian-inference-based neural networks for tool wear estimation. Int J Adv Manuf Technol 30(9-10):797–807CrossRef
157.
Al-Habaibeh A, Gindy N (2000) A new approach for systematic design of condition monitoring systems for milling processes. J Mater Process Technol 107(1-3):243–251CrossRef
158.
Pour M (2018) Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform. Int J Adv Manuf Technol 97(5):2603–2619CrossRef
159.
Ghosh AK, Ullah A, Kubo A, Akamatsu T, D’Addona DM (2020) Machining phenomenon twin construction for industry 4.0: a case of surface roughness. Journal of Manufacturing and Materials Processing 4(1):11CrossRef
160.
Fu Y, Zhang Y, Gao H, Mao T, Zhou H, Sun R, Li D (2019) Automatic feature constructing from vibration signals for machining state monitoring. J Intell Manuf 30(3):995–1008CrossRef
161.
Mu X, Sun Q, Xu J, Chai Z, Sun W, Zhao B (2019) Feasibility analysis of the replacement of the actual machining surface by a 3D numerical simulation rough surface. Int J Mech Sci 150:135–144CrossRef
162.
Rohit VS, Gopal AV, Krishna LR (2020) A new approach in establishing stable machining parameters using frame statistics and kurtosis. In: Advances in applied mechanical engineering. Springer, pp 1159-1167
163.
Mejri S, Gagnol V, Le T-P, Sabourin L, Ray P, Paultre P (2016) Dynamic characterization of machining robot and stability analysis. Int J Adv Manuf Technol 82(1-4):351–359CrossRef
164.
Muhammad BB, Wan M, Feng J, Zhang W-H (2017) Dynamic damping of machining vibration: a review. Int J Adv Manuf Technol 89(9-12):2935–2952CrossRef
165.
Zhu K, San Wong Y, Hong GS (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manuf 49(7-8):537–553CrossRef
166.
Zuo B, Li J, Sun C, Zhou X (2019) A new statistical method for detecting trend turning. Theor Appl Climatol 138(1):201–213CrossRef
167.
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRef
168.
Yu J, Zheng X, Wang S (2019) A deep autoencoder feature learning method for process pattern recognition. J Process Control 79:1–15CrossRef
169.
Yu J, Liu G (2020) Knowledge-based deep belief network for machining roughness prediction and knowledge discovery. Comput Ind 121:103262CrossRef
Metadaten
Titel
A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
verfasst von
Mustafa Kuntoğlu
Emin Salur
Munish Kumar Gupta
Murat Sarıkaya
Danil Yu. Pimenov
Publikationsdatum
06.07.2021
Verlag
Springer London
Erschienen in
The International Journal of Advanced Manufacturing Technology / Ausgabe 9-10/2021
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-021-07425-4

Weitere Artikel der Ausgabe 9-10/2021

The International Journal of Advanced Manufacturing Technology 9-10/2021 Zur Ausgabe

ORIGINAL ARTICLE

A new single probe scanning method for on-machine measurement of roundness error

ORIGINAL ARTICLE

Experimental investigation and optimization of delamination factors in the drilling of jute fiber–reinforced polymer biocomposites with multiple estimators

ORIGINAL ARTICLE

3D printing of porcelain: finite element simulation of anisotropic sintering

ORIGINAL ARTICLE

Fabrication of S-shaped micron-sized constrictions on FeC (steel) surface using femtosecond laser ablation with beam shaping

ORIGINAL ARTICLE

Analysis of dimensional accuracy for micro-milled areal material measures with kinematic simulation

ORIGINAL ARTICLE

Evaluation of machinability-based sustainability indicators in the eco-benign turning of Ti3Al2.5V alloy with textured tools

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise