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2022 | OriginalPaper | Chapter

4. Mathematical Foundations of Machining System Monitoring

Author : Kunpeng Zhu

Published in: Smart Machining Systems

Publisher: Springer International Publishing

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Abstract

To ensure the safety and processing quality of high investment automation processing equipment, machining process monitoring is becoming an urgent problem to be solved in the modern machining system.

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Literature
1.
go back to reference Teti R, Jemielniak K, O’Donnel G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Techn 59(2):717–739CrossRef Teti R, Jemielniak K, O’Donnel G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Techn 59(2):717–739CrossRef
2.
go back to reference Zhou Z, Chen Y (1999) The monitoring and fault diagnosis of modern manufacturing systems. Huazhong University of Science and Technology Press Zhou Z, Chen Y (1999) The monitoring and fault diagnosis of modern manufacturing systems. Huazhong University of Science and Technology Press
3.
go back to reference Grzesik W (2017) Advanced machining processes of metallic materials: theory, modelling and applications, 2nd edn. Elsevier Grzesik W (2017) Advanced machining processes of metallic materials: theory, modelling and applications, 2nd edn. Elsevier
4.
go back to reference Brecher C, Esser M, Witt S (2009) Interaction of manufacturing process and machine tool. CIRP Ann Manuf Technol 58(2):588–607CrossRef Brecher C, Esser M, Witt S (2009) Interaction of manufacturing process and machine tool. CIRP Ann Manuf Technol 58(2):588–607CrossRef
5.
go back to reference Ljung L (1999) System identification: theory for the user. Prentice-Hall Ljung L (1999) System identification: theory for the user. Prentice-Hall
6.
go back to reference Bendat JS (2010) Random data analysis and measurement procedures. Wiley Bendat JS (2010) Random data analysis and measurement procedures. Wiley
7.
go back to reference Manolakis DG, Ingle VK, Kogon SM (2000) Statistical and adaptive signal processing. McGraw-Hill Education Manolakis DG, Ingle VK, Kogon SM (2000) Statistical and adaptive signal processing. McGraw-Hill Education
8.
go back to reference Shumway RH, Stoffer DS (2017) Time series analysis and its application, 4th edn. Springer Shumway RH, Stoffer DS (2017) Time series analysis and its application, 4th edn. Springer
9.
go back to reference Altintas Y, Yellowley I (1989) The process detection of tool failure in milling using cutting force models. ASME J Eng Ind 111:149–157CrossRef Altintas Y, Yellowley I (1989) The process detection of tool failure in milling using cutting force models. ASME J Eng Ind 111:149–157CrossRef
10.
go back to reference Kumar SA, Ravindra HV, Srinivasa YG (1997) In-process tool wear monitoring through time series modeling and pattern recognition. Int J Prod Res 35(3):739–751CrossRef Kumar SA, Ravindra HV, Srinivasa YG (1997) In-process tool wear monitoring through time series modeling and pattern recognition. Int J Prod Res 35(3):739–751CrossRef
11.
go back to reference Gradisek J, Govekar E, Grabec I (1998) Time series analysis in metal cutting: chatter versus chatter-free cutting. Mech Syst Signal Process 12(6):839–854CrossRef Gradisek J, Govekar E, Grabec I (1998) Time series analysis in metal cutting: chatter versus chatter-free cutting. Mech Syst Signal Process 12(6):839–854CrossRef
12.
go back to reference Tönshoff HK (ed) (2001) Sensors in manufacturing, vol 1. Wiley-VCH Tönshoff HK (ed) (2001) Sensors in manufacturing, vol 1. Wiley-VCH
13.
go back to reference Snr D (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40(8):1073–1098CrossRef Snr D (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40(8):1073–1098CrossRef
14.
go back to reference Zhou Y, Xue W (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96:2509–2523CrossRef Zhou Y, Xue W (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96:2509–2523CrossRef
15.
go back to reference Kuntoglu M, Saglam H (2020) Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Measurement 108582 Kuntoglu M, Saglam H (2020) Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Measurement 108582
16.
go back to reference Özel T, Nadgir A (2002) Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools. Int J Mach Tools Manuf 42:287–297CrossRef Özel T, Nadgir A (2002) Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools. Int J Mach Tools Manuf 42:287–297CrossRef
17.
go back to reference Bhattacharyya P, Sengupta D, Mukhopadhyay S (2007) Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques. Mech Syst Signal Process 21(6):2665–2683CrossRef Bhattacharyya P, Sengupta D, Mukhopadhyay S (2007) Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques. Mech Syst Signal Process 21(6):2665–2683CrossRef
18.
go back to reference Brinksmeier E, Preuss W, Riemer O, Rentsch R (2017) Cutting forces, tool wear and surface finish in high speed diamond machining. Precis Eng 49:293–304CrossRef Brinksmeier E, Preuss W, Riemer O, Rentsch R (2017) Cutting forces, tool wear and surface finish in high speed diamond machining. Precis Eng 49:293–304CrossRef
19.
go back to reference Zhu KP, Zhang Y (2019) A generic tool wear model and its application to force modeling and wear monitoring in high speed milling. Mech Syst Signal Process 115(15):147–161CrossRef Zhu KP, Zhang Y (2019) A generic tool wear model and its application to force modeling and wear monitoring in high speed milling. Mech Syst Signal Process 115(15):147–161CrossRef
20.
go back to reference Sevilla P, Robles J, Muñiz J, Lee F (2015) Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering. Int J Adv Manuf Technol 81(5–8):1–8 Sevilla P, Robles J, Muñiz J, Lee F (2015) Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering. Int J Adv Manuf Technol 81(5–8):1–8
21.
go back to reference Zhou Y, Liu X, Li F, Sun B, Xue W (2015) An online damage identification approach for numerical control machine tools based on data fusion using vibration signals. J Vib Control 21(15):2925–2936CrossRef Zhou Y, Liu X, Li F, Sun B, Xue W (2015) An online damage identification approach for numerical control machine tools based on data fusion using vibration signals. J Vib Control 21(15):2925–2936CrossRef
22.
go back to reference Aghdam B, Vahdati M, Sadeghi M (2015) Vibration-based estimation of tool major flank wear in a turning process using ARMA models. Int J Adv Manuf Technol 76:1631–1642CrossRef Aghdam B, Vahdati M, Sadeghi M (2015) Vibration-based estimation of tool major flank wear in a turning process using ARMA models. Int J Adv Manuf Technol 76:1631–1642CrossRef
23.
go back to reference Dimla DE (2002) The correlation of vibration signal features to cutting tool wear in a metal turning operation. Int J Adv Manuf Technol 19:705–713CrossRef Dimla DE (2002) The correlation of vibration signal features to cutting tool wear in a metal turning operation. Int J Adv Manuf Technol 19:705–713CrossRef
24.
go back to reference Kataoka R, Shamoto E (2019) Influence of vibration in cutting on tool flank wear: Fundamental study by conducting a cutting experiment with forced vibration in the depth-of-cut direction. Precis Eng 55:322–329CrossRef Kataoka R, Shamoto E (2019) Influence of vibration in cutting on tool flank wear: Fundamental study by conducting a cutting experiment with forced vibration in the depth-of-cut direction. Precis Eng 55:322–329CrossRef
25.
go back to reference 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 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
26.
go back to reference Chiou RY, Liang SY (2000) Analysis of acoustic emission in chatter vibration with tool wear effect in turning. Int J Mach Tools Manuf 40:927–941CrossRef Chiou RY, Liang SY (2000) Analysis of acoustic emission in chatter vibration with tool wear effect in turning. Int J Mach Tools Manuf 40:927–941CrossRef
27.
go back to reference Maia LHA, Abrao AM, Vasconcelos WL, Sales WF, Machado AR (2015) A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission. Tribol Int 92:519–532CrossRef Maia LHA, Abrao AM, Vasconcelos WL, Sales WF, Machado AR (2015) A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission. Tribol Int 92:519–532CrossRef
28.
go back to reference Wang C, Bao Z, Zhang P, Ming W, Chen M (2019) Tool wear evaluation under minimum quantity lubrication by clustering energy of acoustic emission burst signals. Measurement 138:256–265CrossRef Wang C, Bao Z, Zhang P, Ming W, Chen M (2019) Tool wear evaluation under minimum quantity lubrication by clustering energy of acoustic emission burst signals. Measurement 138:256–265CrossRef
29.
go back to reference Jemielniak K, Arrazola P (2008) application of AE and cutting force signals in tool conditionmonitoring in micro-milling. CIRP J Manuf Sci Technol 1:97–102CrossRef Jemielniak K, Arrazola P (2008) application of AE and cutting force signals in tool conditionmonitoring in micro-milling. CIRP J Manuf Sci Technol 1:97–102CrossRef
30.
go back to reference Pechenin V, Khaimovich A, Kondratiev A, Bolotov M (2017) Method of controlling cutting tool wear based on signal analysis of acoustic emission for milling. Procedia Eng 176:246–252CrossRef Pechenin V, Khaimovich A, Kondratiev A, Bolotov M (2017) Method of controlling cutting tool wear based on signal analysis of acoustic emission for milling. Procedia Eng 176:246–252CrossRef
Metadata
Title
Mathematical Foundations of Machining System Monitoring
Author
Kunpeng Zhu
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-030-87878-8_4

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