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Tool life prediction via SMB-enabled monitor based on BPNN coupling algorithms for sustainable manufacturing

Published online by Cambridge University Press:  03 July 2023

Wen-Yang Chang Open the ORCID record for Wen-Yang Chang [Opens in a new window]  and
Bo-Yao Hsu
Wen-Yang Chang*
Affiliation:
Department of Mechanical and Computer-Aided Engineering, National Formosa University, No. 64, Wunhua Rd., Huwei Township, Yunlin County 632, Taiwan Smart Machinery and Intelligent Manufacturing Research Center, National Formosa University, Yunlin 632, Taiwan
Bo-Yao Hsu
Affiliation:
Department of Power Mechanical Engineering, National Formosa University, Yunlin 632, Taiwan
*
Corresponding author: Wen-Yang Chang; Email: wenyang@nfu.edu.tw
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Abstract

The predictive methods of tool wear are usually based on different algorithm predictors, different source data, and different sensing devices for remaining useful life (RUL). In general, it has challenges to model and ensure all of the cutting conditions that are suitable in the actual cutting process for sustainable manufacturing. In order to overcome the doing large amount of experimental data and predict different tool RULs, this study combines the analytical force modeling, the back-propagation neural network (BPNN) machine learning, and the current sensor which all are integrated in smart machine box (SMB) to realize the practical RUL prediction for on-line and real-time applications. The analytical model of the cutting force coefficients of shear and ploughing was established from average cutting forces, it could reduce the experimental number and predict the different cutting conditions. In general, the loading current of the cutting tool from a spindle motor is relatively easier acquired than the resultant forces. Thus, the loading currents of the spindle are used to train and predict the cutting forces using the BPNN model during intelligent manufacturing. The SMB architecture mainly performed the autonomous actions based on the edge layer, the fog layer, and the cloud layer via the TCP/IP, the MQTT protocol, and the unified communication library. Results showed that a predictive error for the ends of the tool life is about 3–10% that are based on the calculating of the cumulative current ratio.

Keywords

BPNNforce coefficientsloading currentpredictiontool life
Type
Research Article
Information
AI EDAM , Volume 37 , 2023 , e20
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Alauddin, M, Baradie, MAE and Hashmi, MSJ (1997) Prediction of tool life in end milling by response surface methodology. Journal of Materials Processing Technology 71, 456–465.CrossRefGoogle Scholar
Altintas, Y. (2012) Manufacturing Automation - Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, 2nd Edn. Cambridge: Cambridge University Press.Google Scholar
An, Q, Tao, Z, Xu, X, Mansori, ME and Chen, M (2020) A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network. Measurement 154, 107461.CrossRefGoogle Scholar
Bird, JO and Chivers, PJ (1993) Newnes Engineering and Physical Science Pocket Book, 1st Edn. Virginia: Newnesn.Google Scholar
Castejon, M, Alegre, E, Barreiro, J and Hernandez, LK (2007) On-line tool wear monitoring using geometric descriptors from digital images. International Journal of Machine Tools & Manufacture 47, 1847–1853.CrossRefGoogle Scholar
Chang, WY and Wu, SJ (2016) Big data analysis of a mini three-axis CNC machine tool based on the tuning operation of controller parameters. International Journal of Advanced Manufacturing Technology 99, 1077–1083.CrossRefGoogle Scholar
Chang, WY, Chen, CC and Wu, SJ (2019) Chatter analysis and stability prediction of milling tool based on zero-order and envelope methods for real-time monitoring and compensation. International Journal of Precision Engineering and Manufacturing 20, 693–700.CrossRefGoogle Scholar
Chang, WY, Wu, SJ and Hsu, JW (2020) Investigated iterative convergences of neural network for prediction turning tool wear. International Journal of Advanced Manufacturing Technology 106, 2939–2948.CrossRefGoogle Scholar
Daniel, J, Soren, H, Volodymyr, B and Jan-Eric, S (2017) Assessment of commonly used tool life models in metal cutting. Procedia Manufacturing 11, 602–609.Google Scholar
Dos Santos, ALB, Duarte, MAV, Abrao, AM and Machado, AR (1999) An optimisation procedure to determine the coefficients of the extended Taylor's equation in machining. International Journal of Machine Tools and Manufacture 39, 17–31.CrossRefGoogle Scholar
Drouillet, C, Karandikar, J, Nath, C, Hourneaux, AC, Mansori, ME and Kurfess, T (2016) Tool life predictions in milling using spindle power with the neural network technique. Journal of Manufacturing Processes 22, 161–168.CrossRefGoogle Scholar
Dutta, S, Pal, SK and Sen, R (2016) On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression. Precision Engineering 43, 34–42.CrossRefGoogle Scholar
Feng, Y, Hung, TP, Lu, YT, Lin, YF, Hsu, FC, Lin, CF, Lu, YC and Liang, SY (2019) Flank tool wear prediction of laser-assisted milling. Journal of Manufacturing Processes 43, 292–299.CrossRefGoogle Scholar
Gao, G, Xia, Z, Su, T, Xiang, D and Zhao, B (2021) Cutting force model of longitudinal-torsional ultrasonic-assisted milling Ti-6Al-4 V based on tool flank wear. Journal of Materials Processing Techology 291, 117042.CrossRefGoogle Scholar
Gomes, MC, Brtio, LC, Bacci da Silva, M and Viana Durate, MA (2021) Tool wear monitoring in micromilling using support vector machine with vibration and sound sensors. Precision Engineering 67, 137–151.CrossRefGoogle Scholar
Goodall, P, Pantazis, D and West, A (2020) A cyber physical system for tool condition monitoring using electrical power and a mechanistic model. Computers in Industry 118, 103223.CrossRefGoogle Scholar
Grzesik, W (2017) Chapter twelve - Tool wear and damage. In Grzesik, Wit (ed.), Advanced Machining Processes of Metallic Materials, 2nd Edn. Elsevier, pp. 215–239.CrossRefGoogle Scholar
Guo, YB and Chou, YK (2004) The determination of ploughing force and its influence on material properties in metal cutting. Journal of Materials Processing Technology 148, 368–375.CrossRefGoogle Scholar
Hosseinkhani, K and Ng, E-G (2020) A unique methodology for tool life prediction in machining. Journal of Manufacturing and Materials Processing 4, 16–35.CrossRefGoogle Scholar
Karandikar, JM, Schmitz, TL and Abbas, AE (2012) Spindle speed selection for tool life testing using Bayesian inference. Journal of Manufacturing Systems 31, 403–411.CrossRefGoogle Scholar
Karandikar, JM, Abbas, AE and Schmitz, TL (2014) Tool life prediction using Bayesian updating. Part 1: milling tool life model using a discrete grid method. Precision Engineering 38, 9–17.CrossRefGoogle Scholar
Kuljanic, E and Sortino, M (2005) TWEM, a method based on cutting forces-monitoring tool wear in face milling. International Journal of Machine Tools & Manufacture 45, 29–34.CrossRefGoogle Scholar
Li, X, Venuvinod, PK and Chen, MK (2000) Feed cutting force estimation from the current measurement with hybrid learning. International Journal of Advanced Manufacturing Technology 16, 859–862.CrossRefGoogle Scholar
Li, X, Ouyang, G and Liang, Z (2008) Complexity measure of motor current signals for tool flute breakage detection in end milling. International Journal of Machine Tools & Manufacture 48, 371–379.CrossRefGoogle Scholar
Li, H, Wang, W, Li, Z, Dong, L and Li, Q (2020) A novel approach for predicting tool remaining useful life using limited data. Mechanical Systems and Signal Processing 143, 106832.CrossRefGoogle Scholar
Liu, ZQ, Ai, X, Zhang, H, Wang, ZT and Wan, Y (2002) Wear patterns and mechanisms of cutting tools in high-speed face milling. Journal of Materials Processing Technology 129, 222–226.CrossRefGoogle Scholar
Marani, M, Zeinali, M, Songmene, V and Mechefske, CK (2021) Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modeling. Measurement 177, 109329.CrossRefGoogle Scholar
Ren, Q, Baron, L, Balazinski, M, Botez, R and Bigras, P (2015) Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission. Applied Soft Computing 31, 14–24.CrossRefGoogle Scholar
Salgado, DR and Alonso, FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. International Journal of Machine Tools & Manufacture 47, 2140–2152.CrossRefGoogle Scholar
Stevenson, R (1998) The measurement of parasitic forces in orthogonal cutting. International Journal of Machine Tools and Manufacture 38, 113–130.CrossRefGoogle Scholar
Taylor, FW (1907) On the art of cutting metals. Transactions of the American Society of Mechanical Engineers 28, 31–58.Google Scholar
Ubhanyaratne, I, Pereira, MP, Xiang, Y and Rolfe, BF (2017) Audio signal analysis for tool wear monitoring in sheet metal stamping. Mechanical Systems and Signal Processing 85, 809–826.CrossRefGoogle Scholar
Wang, CY, Xie, YX, Qin, Z, Lin, HS, Yuan, YH and Wang, QM (2015) Wear and breakage of TiAlN- and TiSiN-coated carbide tools during high-speed milling of hardened steel. Wear 336-337, 29–42.CrossRefGoogle Scholar
Wong, T, Kim, W and Kwon, P (2004) Experimental support for a model-based prediction of tool wear. Wear 257, 790–798.CrossRefGoogle Scholar
Yang, Y, Guo, Y, Haung, Z, Chen, N, Li, L, Jiang, Y and He, N (2019) Research on the milling tool wear and life prediction by establishing an integrated predictive model. Measurement 145, 178–189.CrossRefGoogle Scholar
Yao, Y, Li, X and Yuan, Z (1999) Tool wear detection with fuzzy classification and wavelet fuzzy neural network. International Journal of Machine Tools & Manufacture 39, 1525–1538.CrossRefGoogle Scholar
Yen, CL, Lu, MC and Chen, JL (2013) Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting. Mechanical System and Signal Processing 34, 353–366.CrossRefGoogle Scholar
You, Z, Gao, H, Guo, L, Liu, Y and Li, J (2020) On-line milling cutter wear monitoring in a wide field-of-view camera. Wear 460-461, 203479.CrossRefGoogle Scholar
Zheng, L, Li, Y and Liang, SY (1997) A generalized model of milling forces. International Journal of Advanced Manufacturing Technology 14, 160–171.CrossRefGoogle Scholar
Zhou, JT, Zhao, X and Gao, J (2019) Tool remaining useful life prediction method based on LSTM under variable working conditions. International Journal of Advanced Manufacturing Technology 104, 4715–4726.CrossRefGoogle Scholar
Zhu, K and Zhang, Y (2017) Modeling of the instantaneous milling force per tooth with tool run-out effect in high speed ball-end milling. International Journal of Machine Tools & Manufacture 118-119, 37–48.CrossRefGoogle Scholar
Zhu, K and Zhang, Y (2019) A generic tool wear model and its application to force modeling and wear monitoring in high speed milling. Mechanical Systems and Signal Processing 115, 147–161.CrossRefGoogle Scholar