RESEARCH PAPER
A tool wear condition monitoring approach for end milling based on numerical simulation
1
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China
2
School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, China
Publication date: 2021-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(2):371-380
HIGHLIGHTS
- A numerical simulation model is proposed to overcome sample missing and insufficiency.
- Model parameters are optimized by orthogonal experiment and KL divergence.
- The optimized model provide effectively missing samples and expand sample size.
- Experimental results show the proposed method improves notably the performance of TCM.
KEYWORDS
ABSTRACT
As an important research area of modern manufacturing, tool condition monitoring (TCM)
has attracted much attention, especially artificial intelligence (AI)- based TCM method.
However, the training samples obtained in practical experiments have the problem of sample
missing and sample insufficiency. A numerical simulation- based TCM method is proposed
to solve the above problem. First, a numerical model based on Johnson-Cook model is established, and the model parameters are optimized through orthogonal experiment technology,
in which the KL divergence and cosine similarity are used as the evaluation indexes. Second,
samples under various tool wear categories are obtained by the optimized numerical model
above to provide missing samples not present in the practical experiments and expand sample size. The effectiveness of the proposed method is verified by its application in end milling
TCM experiments. The results indicate the classification accuracies of four classifiers (SVM,
RF, DT, and GRNN) can be improved significantly by the proposed TCM method.
REFERENCES (50)
1.
Chen QP, Xie QS, Yuan QN, Huang HS, Li YT. Research on a real-time monitoring method for the wear state of a tool based on a convolutional bidirectiona LSTM model. Symmetry-Basel 2019; 11(10) 1233, https://doi.org/10.3390/sym111....
2.
Duan CZ, Yu HY, Cai YJ, Li YY. Finite Element Simulation and Experiment of Chip Formation during High Speed Cutting of Hardened Steel. Applied Mechanics and Materials 2010; 29-32: 1838-1843, https://doi.org/10.4028/www.sc....
3.
Duan R, Lin Y, Zeng Y. Fault diagnosis for complex systems based on reliability analysis and sensors data considering epistemic uncertainty. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018, 20(4): 558-566, https://doi.org/10.17531/ein.2....
4.
Ducobu F, Rivière-Lorphèvre E, Filippi E. On the importance of the choice of the parameters of the Johnson-Cook constitutive model and their influence on the results of a Ti6Al4V orthogonal cutting model. International Journal of Mechanical Sciences 2017; 122: 143-155, https://doi.org/10.1016/j.ijme....
5.
Erven T, Harremoës P. Rényi divergence and Kullback-Leibler divergence. IEEE Transactions on Information Theory 2014; 60(7): 3797-3820, https://doi.org/10.1109/TIT.20....
6.
Gao C, Xue W, Ren Y, Zhou YQ. Numerical control machine tool fault diagnosis using hybrid stationary subspace analysis and least squares support vector machine with a single sensor. Applied Sciences 2017; 7(4) 346,https://doi.org/10.3390/app704....
7.
Gao Y, Liu XY, Xiang JW. FEM simulation- based generative adversarial networks to detect bearing faults. IEEE Transactions on Industrial Informatics 2020; 16(7): 4961-4971, https://doi.org/10.1109/TII. 2020.2968370.
8.
Gkdere G, Mehmet G. New reliability score for component strength using kullback-leibler divergence. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(3): 367-372, https://doi.org/ 10.17531/ein.2016.3.7.
9.
Huang ZW, Zhu JM, Lei JT, Li XR, Tian FQ. Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing 2020; 31(4): 953-966,https://doi.org/10.1007/s10845....
10.
Iqbal S, Mativenga PT, Sheikh MA. Characterization of machining of AISI 1045 steel over a wide range of cutting speeds. Part 1: investigation of contact phenomena. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture 2007; 221(5): 909-916, https://doi.org/10.1243/ 09544054JEM796.
11.
Iqbal S, Mativenga PT, Sheikh MA. Characterization of machining of AISI 1045 steel over a wide range of cutting speeds. Part 2: Evaluation of flow stress models and interface friction distribution schemes. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture 2007; 221(5): 917-926, https://doi.org/10.1243/095440....
12.
Jasiulewicz-Kaczmarek M, Antosz K, Żywica P, Mazurkiewicz D, Sun B, Ren Y. Framework of machine criticality assessment with criteria interactions. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(2): 207-220, https://doi.org/10.17531/ein.2....
13.
Javidikia M, Sadeghifar M, Songmene V, Jahazi M. On the impacts of tool geometry and cutting conditions in straight turning of aluminum alloys 6061-T6: an experimentally validated numerical study. International Journal of Advanced Manufacturing Technology 2020; 106(9-10): 4547-4565, https://doi.org/ 10.1007/s00170-020-04945-3.
14.
Karabacak YE, Ozmen NG, Gumusel L. Worm gear condition monitoring and fault detection from thermal images via deep learning method. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(3): 544-556, https://doi.org/10.17531/ein.2....
15.
Karandikar J, Mcleay T, Turner S, Schmitz T. Tool wear monitoring using naïve bayes classifiers. The International Journal of Advanced Manufacturing Technology 2015; 77(9-12): 1613-1626, https://doi.org/ 10.1007/s00170-014-6560-6.
16.
Karwat B, Rubacha P, Stanczyk E. Simulational and experimental determination of the exploitation parameters of a screw conveyor. Eksploatacja i Niezawodnosc - Maintenance and Reliability 22(4): 741-747, https://doi.org/10.17531/ein.2....
17.
Klocke F, Lung D, Buchkremer S. Inverse identification of the constitutive equation of Inconel 718 and AISI 1045 from FE machining simulations. Procedia CIRP 2013; 8: 212-217, https://doi.org/10.1016/j. procir.2013.06.091.
18.
Kong D, Chen Y, Li N, Duan C, Lu L, Chen D. Tool wear estimation in end milling of titanium alloy using npe and a novel woa-svm model. IEEE Transactions on Instrumentation and Measurement 2020; 69(7): 5219-5232, https://doi.org/10.1109/TIM.20....
19.
Kothuru A, Nooka SP, Liu R. Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling. The International Journal of Advanced Manufacturing Technology 2018; 95(9-12): 3797-3808, https://doi.org/10.1007/s00170....
20.
Kozłowski E, Mazurkiewicz D, Zabinski T, Prucnal S, Sep J. Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 679-685, https://doi.org/10.17531/ein.2....
21.
Kozłowski E, Mazurkiewicz D, Żabiński T, Prucnal S, Sęp J. Machining sensor data management for operation-level predictive model. Expert Systems with Applications 2020; 159: 1-22, https://doi.org/ 10.1016/j.eswa.2020.113600.
22.
Kumar A, Kumar R. Adaptive artificial intelligence for automatic identification of defect in the angular contact bearing. Neural Computing and Applications 2018; 29: 277-287, https://doi.org/10.1007/s00521... -3123-4.
23.
Kumar A, Kumar R. Least Square Fitting for Adaptive Wavelet Generation and Automatic Prediction of Defect Size in the Bearing Using Levenberg-Marquardt Backpropagation. Journal of Nondestructive Evaluation 2017; 36, 7, https://doi.org/10.1007/s10921....
24.
Küppers F, Albers J, Haselhoff A. Random Forest on an Embedded Device for Real-time Machine State Classification. 2019 European Signal Processing Conference (EUSIPCO), A Coruna, Spain, https://doi.org/10.23919/EUSIP....
25.
Lei Z, Zhou YQ, Sun BT, Sun WF. An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process. The International Journal of Advanced Manufacturing Technology 2020; 106(3-4): 1203-1212, https://doi.org/10.1007/s00170...- 04689-9.
26.
Li GF, Wang YB, He JL, Hao QB, Yang HJ, Wei JF. Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM. The International Journal of Advanced Manufacturing Technology 2020; 110: 511-522, https://doi.org/10.1007/s00170....
27.
Liu D, Wang S, Tomovic M. Degradation modeling method for rotary lip seal based on failure mechanism analysis and stochastic process. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(3): 381-390, https://doi.org/10.17531/ein.2....
28.
Liu H, Liu ZY, Jia WQ, Lin XK, Zhang S. A novel transformer-based neural network model for tool wear estimation. Measurement Science and Technology 2020; 31(6) 065106, https://doi.org/10.1088/1361- 6501/ab7282.
29.
Liu XY, Huang HZ, Xiang JW. A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine. Knowledge-Based Systems 2020; 105653, https://doi.org/10.1016/j.knos....
30.
Liu ZQ, Wu JH, Shi ZY, Zhao PF. State-of-the-art of Constitutive Equations in Metal Cutting Operations. Tool Engineering 2008; 42(3): 3-10 (in Chinese).
31.
Padma RB, Kumara SM. Finite Element Simulation of a Friction Drilling process using Deform-3D. International Journal of Engineering Research and Applications 2012; 2(6): 716-721.
32.
Pawełczyk M, Fulara S, Sepe M, Luca AD, Badora M. Industrial gas turbine operating parameters monitoring and data-driven prediction. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(3):391-399. https://doi.org/10.17531/ein.2....
33.
Serin GB, Sener AM, Ozbayoglu HO. Review of tool condition monitoring in machining and opportunities for deep learning. International Journal of Advanced Manufacturing Technology 2020; 109(2): 953-974. https://doi.org/10.1007/s00170....
34.
Shankar S, Mohanraj T, Rajasekar R. Prediction of cutting tool wear during milling process using artificial intelligence techniques. International Journal of Computer Integrated Manufacturing 2019; 32(2): 174-182, https://doi.org/10.1080/095119....
35.
Shao F, Liu Z, Wan Y, Shi Z. Finite element simulation of machining of Ti-6Al-4V alloy with thermodynamical constitutive equation. The International Journal of Advanced Manufacturing Technology 2010; 49: 431-439, https://doi.org/10.1007/s00170....
36.
Shrot A, Bäker M. Determination of Johnson-Cook parameters from machining simulations. Computational Materials Science 2012; 52 (1):298-304, https://doi.org/10.1016/j.comm....
37.
Su C, Chen H, Wen Z. Prediction of remaining useful life for lithium-ion battery with multiple health indicators. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(1): 176-183, 10.17531/ein.2021.1.18.
38.
Sun HB, Zhang JD, Mo R, Zhang XZ. In-process tool condition forecasting based on a deep learning method. Robotics and Computer-Integrated Manufacturing 2020; 64: 101924, https://doi.org/10.1016/j.rcim....
39.
Tamizharasan T, Kumar S. Optimization of cutting insert geometry using DEFORM-3 D: numerical simulation and experimental validation. International Journal of Simulation Modelling 2012; 11(2): 65-76, https://doi.org/10.2507/IJSIMM....
40.
Wang Y, Su HH, Dai JB, Yang SB. A novel finite element method for the wear analysis of cemented carbide tool during high speed cutting Ti6Al4V process. The International Journal of Advanced Manufacturing Technology 2019; 103, 2795-2807, https://doi.org/10.1007/s00170....
41.
Wu DZ, Jennings C, Terpenny J, Gao RX, Kumara S. A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. Journal of Manufacturing Science and Engineering-Transactions of the ASME 2017; 139(7): 071018, https://doi.org/10.1115/ 1.4036350.
42.
Xiang JW, Zhong YT. A novel personalized diagnosis methodology using numerical simulation and an intelligent method to detect faults in a shaft. Applied Sciences 2016; 6(12) 414, https://doi.org/10.3390/ app6120414.
43.
Xu Z, Guo D, Wang J, Ge D. A numerical simulation method for a repairable dynamic fault tree. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(1):34-41, https://doi.org/10.17531/ein.2....
44.
Zhou J, Pang C, Zhong Z, Lewis F. Tool wear monitoring using acoustic emissions by dominant- feature identification. IEEE Transactions on Instrumentation and Measurement 2011; 60(2): 547-559, https://doi.org/10.1109/TIM.20....
45.
Zhou YQ, Sun BT, Sun WF. A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling. Measurement 2020; 16(15): 186-196, https://doi.org/10.1016/j.meas....
46.
Zhou YQ, Sun BT, Sun WF, Lei Z. Tool Wear Condition Monitoring Based on a Two-layer Angle Kernel Extreme Learning Machine using Sound Sensor for Milling Process. Journal of Intelligent Manufacturing 2020; 9, https://doi.org/ 10.1007/s10845-020-01663-1.
47.
Zhou YQ, Sun WF. Tool Wear Condition Monitoring in Milling Process Based on Current Sensors. IEEE Access 2020; 8: 95491-95502, https://doi.org/10.1109/ACCESS....
48.
Zhou YQ, Xue W. A multisensor fusion method for tool condition monitoring in milling. Sensors 2018; 18(11): 3866, https://doi.org/10.3390/s18113....
49.
Zhou YQ, Xue W. Review of tool condition monitoring methods in milling processes. The International Journal of Advanced Manufacturing Technology 2018; 96(3-4): 2509-2523, https://doi.org/10.1007/s00170- 018-1768-5.
50.
Zhu KP, Liu T. Online tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Transactions on Industrial Informatics 2018; 14(1): 69-78, https://doi.org/10.1109/TII.20....
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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