Abstract
Carbon fiber–reinforced plastic (CFRP) is becoming more popular in the aerospace industry due to its high strength-to-weight ratio and low weight. Machining CFRP to achieve the required surface quality, on the other hand, remains a challenge. High temperature in the cutting zone area affects the tool life and surface quality of the machined part. A thermally affected matrix makes an inaccurate interpretation of the surface quality. Then, the roughness parameters cannot be an appropriate indicator for surface evaluation of the CFRP. In the aerospace industry, however, ensuring the acceptable surface quality of a part is essential. Minimizing and controlling tool wear are necessary to avoid degrading the finished surface and losing the dimensional accuracy of the final part. Early detection of tool wear and appropriate surface quality in finishing operations can be achieved using online tool condition monitoring. Cutting forces and electric current signals related to the spindle during machining are very responsive to cutting conditions and can accurately represent tool condition changes. Fractal analysis, as a new approach in the online tool condition monitoring, can assess the tool condition during machining. This research investigates the fractal analysis of the spindle electric current signal and the total cutting force signal while trimming CFRP using a CVD end mill through three different tool life conditions, e.g. new tool, moderately worn tool, and severely worn tool. The empirical fractal index is also introduced to assess the tool condition and ensure the acceptable surface qualities in the finishing operations. The effectiveness of fractal analysis as a decision-making method in the tool condition monitoring was successfully proven in this study.
Similar content being viewed by others
Availability of data and materials
Not applicable.
Code availability
Not applicable.
References
Breuer UP (2016) Commercial aircraft composite technology, 1st edn. Springer, Cham, Switzerland
Ahmad J (2009) Machining of polymer composites, 1st edn. Springer, Boston, MA
Karataş MA, Gökkaya H (2018) A review on machinability of carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) composite materials. Def Technol 14(4):318–326
Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Annals- Manufacturing Technology 59(2):717–739
Nouri M, Fussell BK, Ziniti BL, Linder E (2015) Real-time tool wear monitoring in milling using a cutting condition independent method. Int J Mach Tools Manuf 89:1–13
Abdul-Ameer HK, Al-Kindi GA, Zughaer H (2011) Towards computer vision feedback for enhanced CNC machining. IEEE 3rd International Conference on Communication Software and Networks, Xi'an, China, pp. 754–760
Hidayah MTN, Ghani JA, Nuawi MZ, Haron CHC (2015) A review of utilisation of cutting force analysis in cutting tool condition monitoring. Int J Eng Technol IJET-IJENS 15(03):1
Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26(7–8):693–710
Hu M, Ming W, An Q, Chen M (2019) Tool wear monitoring in milling of titanium alloy Ti–6Al–4 V under MQL conditions based on a new tool wear categorization method. Int J Adv Manuf Technol 104(9–12):4117–4128
Jeong YH, Cho D-W (2002) Estimating cutting force from rotating and stationary feed motor currents on a milling machine. Int J Mach Tools Manuf 42:1559–1566
Soliman E, Ismail F (1997) Chatter detection by monitoring spindle drive current. Int J Adv Manuf Technol 13:27–34
Wang K-S (2013) Towards zero-defect manufacturing (ZDM)- a data mining approach. Adv Manuf 1(1):62–74
Hassan M, Sadek A, Attia MH (2021) Novel sensor-based tool wear monitoring approach for seamless implementation in high speed milling applications. CIRP Ann Manuf Technol 70(1):87–90
Hesser DF, Markert B (2019) Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks. Manuf Lett 19:1–4
Elforjani M, Shanbr S (2018) Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Trans Industr Electron 65(7):5864–5871
Ren Q, Baron L, Balazinski M, Botez R, Bigras P (2015) Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission. Appl Soft Comput 31:14–24
Choi YJ, Park MS, Chu CN (2008) Prediction of drill failure using features extraction in time and frequency domains of feed motor current. Int J Mach Tools Manuf 48(1):29–39
Liao X, Zhou G, Zhang Z, Lu J, Ma J (2019) Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm. Int J Adv Manuf Technol 104(1–4):1051–1063
Pyatykh AS, Savilov AV, Timofeev SA (2022) Method of tool wear control during stainless steel end milling. J Frict Wear 42(4):263–267
Mandelbrot BB (1982) The fractal geometry of nature. W.H. Freeman, New York
Zuo X, Zhu H, Zhou Y, Yang J (2015) Estimation of fractal dimension and surface roughness based on material characteristics and cutting conditions in the end milling of carbon steels. Proc Inst Mech Eng Part B J Eng Manuf 231(8):1423–1437
Rimpault X, Balazinski M, Chatelain J-F (2018) Fractal analysis application outlook for improving process monitoring and machine maintenance in manufacturing 4.0. J Manuf Mater Process 2(3)
Rimpault X, Chatelain JF, Klemberg-Sapieha JE, Balazinski M (2017) Tool wear and surface quality assessment of CFRP trimming using fractal analyses of the cutting force signals. CIRP J Manuf Sci Technol 16:72–80
Jamshidi M, Rimpault X, Balazinski M, Chatelain J-F (2020) Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining. Int J Adv Manuf Technol 106(9–10):3859–3868
Bérubé S (2012) Usinage en détourage de laminés composites carbone/époxy, Mechanical engineering École de technologie supérieure. Montréal
ASTM (2018) Standard practice for measuring and compensating for emissivity using infrared imaging radiometers, E1933–14, ASTM International, West Conshohocken, PA
Majumdar A, Tien CL (1990) Fractal characterization and simulation of rough surface. Wear 136:313–327
Rimpault X, Chatelain J-F, Klemberg-Sapieha J-E, Balazinski M (2016) Fractal analysis of cutting force and acoustic emission signals during CFRP machining. Procedia CIRP 46:143–146
Feng Z, Zuo MJ, Chu F (2010) Application of regularization dimension to gear damage assessment. Mech Syst Signal Process 24(4):1081–1098
Roueff F, Véhe JL (1998) A regularization approach to fractional dimension estimation. Fractals
Akbari A, Danesh M, Khalili K (2017) A method based on spindle motor current harmonic distortion measurements for tool wear monitoring. J Braz Soc Mech Sci Eng 39(12):5049–5055
Rimpault X, Bitar-Nehme E, Balazinski M, Mayer JRR (2018) Online monitoring and failure detection of capacitive displacement sensor in a Capball device using fractal analysis. Measurement 118:23–28
Hintze W, Klingelhöller C (2017) Analysis and modeling of heat flux into the tool in abrasive circular cutting of unidirectional CFRP. Procedia CIRP 66:210–214
Yashiro T, Ogawa T, Sasahara H (2013) Temperature measurement of cutting tool and machined surface layer in milling of CFRP. Int J Mach Tools Manuf 70:63–69
ISO (2012) Geometrical product specifications (GPS) — Surface texture: areal — Part 2: terms, definitions and surface texture parameters. ISO 25178–2, p. 47
Hamedanianpour H, Chatelain JF (2013) Effect of tool wear on quality of carbon fiber reinforced polymer laminate during edge trimming. Appl Mech Mater 325–326:34–39
Ghidossi P, El Mansori M, Pierron F (2004) Edge machining effects on the failure of polymer matrix composite coupons. Compos A Appl Sci Manuf 35(7–8):989–999
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics approval
The authors confirm to the work’s novelty and state that it has not been submitted to any other journal.
Consent to participate
The authors give consent to participate.
Consent for publication
The authors give their consent for their work to be published.
Conflict of interest
The authors declare no competing.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jamshidi, M., Chatelain, JF., Rimpault, X. et al. Tool condition monitoring based on the fractal analysis of current and cutting force signals during CFRP trimming. Int J Adv Manuf Technol 121, 8127–8142 (2022). https://doi.org/10.1007/s00170-022-09860-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-022-09860-3