Abstract
Online monitoring and measurements of tool wear were carried out using cutting forces for precision turning of stainless steel parts. The best combination of features was selected from 14 features extracted from force signals by using a Sequential Forward Search algorithm. Back-propagation neural networks (BPNs) used two features for online classification. When the adaptive neuro-fuzzy inference system (ANFIS) was applied, seven features were needed for the classification. For online measurements, only one feature is needed for BPN. Three features are needed for ANFIS for online measurements. For online classification of turning tool conditions, a 2 × 20 × 1 BPN can achieve a success rate of higher than 86% while a 7 × 2 ANFIS can reach a success rate of higher than 96%. For online measurements of tool wear, the estimation error can be as low as 1.37% when a 1 × 20 × 1 BPN was used while the error can be as low as 0.56% using a 3 × 3 ANFIS. Therefore, the 3 × 3 ANFIS can be used first to predict the degradation of tool conditions during the turning process. It can also be used to measure the tool wear online so as to take feedback control action to enhance accuracy of the process. Once the detected tool wear is close to the worn-out threshold, the 7 × 2 ANFIS will be then applied to classify the tool conditions in order to stop the turning operation on time automatically so as to assure the quality of products and to avoid catastrophic failure.
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References
Atluru S, Huang SH, Snyder JP (2012) A Smart Machine Supervisory System Framework. Int J Adv Manuf Technol 58(5):563–572
Tansel IN, Li M, Demetgul M, Bickraj K, Kaya B, Ozcelik B (2012) Detecting chatter and estimating wear from the torque of end milling signals by using index based reasoner. Int J Adv Manuf Technol 58(1):109–118
Lee S, Li L, Ni J (2010) Online degradation assessment and adaptive fault detection using modified hidden Markov model. ASME J Manuf Sci Eng 132:021010-1–021010-11
Liu TI, Kumagai A, Wang YC, Song SD, Fu Z, Lee J (2010) On-line monitoring of boring tools for control of boring operations. Int J Robot Comput Integr Manuf 26(3):230–239
Zheng L, Yang XM, Zhang ZH, Liu TI (2008) A Web-based machining parameter selection system for life cycle cost reduction and product quality enhancement. Comput Ind 59:254–261
Liu TI, Kumagai A, Lee C (2003) Enhancement of drilling safety and quality using online sensors and artificial neural networks. Int J Occup Saf Ergon 9:37–56
Filho JMC (2012) Prediction of cutting forces in mill turning through process simulation using a five-axis machining center. Int J Adv Manuf Technol 58(1):71–80
Kalvoda T, Hwang YR (2010) A cutter tool monitoring using Hilbert-Huang transform. Int J Mach Tools Manuf 50:495–501
Suprock CA, Roth JT, Downey LM (2009) Endmill condition monitoring and failure forecasting method for curvilinear cuts of nonconstant radii. ASME J Manuf Sci Eng 131:021003-1–021003-8
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 48:29–39
Jemielniak K, Urbanski T, Kossakowska J, Bombbinski S (2012) Tool condition monitoring based on numerous signal features. Int J Adv Manuf Technol 59(1):73–81
Wang L, Liang M (2009) Chatter detection based on probability distribution of wavelet modulus maxima. Int J Robot Comput Integr Manuf 25(6):989–998
Devillez A, Dudzinski D (2008) Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers. Mech Syst Signal Process 21:441–456
Li X (2001) Real-time tool wear condition monitoring in turning. Int J Prod Res 39:981–992
ISO (1972) Tool life testing with single-point turning tools. ISO/TC 29/WG 22 (Secretariat 37) 91
Liu TI, Chen WY, Anantharaman KS (1998) Intelligent detection of drill wear. Mech Syst Signal Process 12(6):863–873
Johnson GW (1994) Practical applications in instrumentation and control, Labview graphical programming. McGraw-Hill, Inc., New York
Dornfeld D, Rangwala S (1990) Sensor integration using neural networks for intelligent tool condition monitoring. ASME J Eng Ind 112:219–228
Niu YM, Wong YS, Hong GS, Liu TI (1998) Multi-category classification of tool conditions using wavelet packets and ART2 network. ASME J Manuf Sci Eng 120:807–816
Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with application in pattern recognition. IEEE Trans Comput 14:326–334
Devijver PA, Kittler J (1982) Pattern recognition—a statistical approach. Prentice-Hall, New Jersey
Whitney (1971) A direct method of non-parametric measurement selection. IEEE Trans Comput 20:100–1103
HNC Inc (1991) HNC ExploreNet 3000. HNC Inc., San Diego
Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21:466–479
Rajsiri V, Lorre JP, Benaben F, Pingaud H (2010) Knowledge-based system for collaborative process specification. Comput Ind 61:161–175
Liu TI, Ordukhani F, Jani D (2005) Monitoring and diagnosis of roller bearing conditions using neural networks and soft computing. Int J Knowl-Based Intell Eng Syst 9:149–158
Nagata F, Kusumoto Y, Watanabe K (2009) Intelligent machining system for the artistic design of wooden paint rollers. Int J Robot Comput Integr Manuf 25(3):680–688
Jang JS, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River
Kumagai A, Liu TI, Hozian P (2006) Control of shape memory alloy actuators with a neuro-fuzzy feedforward model element. J Intell Manuf 17:45–56
He W, Zhang YF, Lee KS, Liu TI (2001) Development of a fuzzy-neuro system for parameter resetting of injection molding. ASME J Manuf Sci Eng 123:110–118
Liu TI, Ko EJ, Lee J (1993) Intelligent control of dynamic systems. J Franklin Inst 330(3):491–503
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Liu, TI., Song, SD., Liu, G. et al. Online monitoring and measurements of tool wear for precision turning of stainless steel parts. Int J Adv Manuf Technol 65, 1397–1407 (2013). https://doi.org/10.1007/s00170-012-4265-2
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DOI: https://doi.org/10.1007/s00170-012-4265-2