Intelligent Cyber-Physical Monitoring and Control of I4.0 Machining Systems - An Overview and Future Perspectives
More details
Hide details
Aerospace Manufacturing Technology Centre, National Research Council Canada
Mechanical Engineering Department, McGill University, Canada
Submission date: 2022-01-22
Final revision date: 2022-03-03
Acceptance date: 2022-03-05
Online publication date: 2022-03-08
Publication date: 2022-03-30
Corresponding author
M. Helmi Attia   

Mechanical Engineering / Aerospace Manufacturing Technology Centre, McGill University / National Research Council Canada, 2107, chemin de la Polytechnique, H3T 1J4, Montréal, Canada
Journal of Machine Engineering 2022;22(1):5-24
Rapid evolution in sensing, data analysis, and industrial internet of things technologies had enabled the manufacturing of advanced smart tooling. This has been fused with effective digital inter-connectivity and integrated process control intelligence to form the industry I4.0 platform. This keynote paper presents the recent advances in smart tooling and intelligent control techniques for machining processes. Self-powered wireless sensing nodes have been utilized for non-intrusive measurement of process-born phenomena near the cutting zone, as well as tool wear and tool failure, to increase confidence in the process and tool condition monitoring accuracy. Cyber-physical adaptive control approaches have been developed to optimize the cycle time and cost while eliminating machined part defects. Novel artificial intelligence AI-based signal processing and modeling approaches were developed to guarantee the generalization and practicality of these systems. The paper concludes with the outlook for future work needed for seamless implementation of these developments in industry.
GEISSBAUER R.SS., BERTTRAM P., CHERAGHI F., 2017, Digital Factories 2020: Shaping the Future of Manufacturing, PricewaterhouseCoopers GmbH Wirtschaftsprufungsgesllschaft (PwC).
Invest in Canada – Aerospace Industries: Canada's Compatitive Advantages, 2014, Foreign Affairs, Trade and Development Canada.
ZHOU Y., XUE W., 2018, Review of Tool Condition Monitoring Methods in Milling Processes, Int. J. Adv. Manuf. Technol., 1–15.
ALTINTAS Y., 2014, Adaptive Control, Laperrière L, Reinhart G, editors, CIRP Encyclopedia of Production Engineering, Springer Berlin Heidelberg., 17–19.
KUTTOLAMADOM M., 2012, Prediction of the Wear & Evolution of Cutting Tools in a Carbide/Ti-6Al-4V Machining Tribosystem by Volumetric Tool Wear Characterization & Modeling, TigerPrints 8, Clemson University.
HASSAN M., SADEK A., ATTIA M.H., THOMSON V., 2018, Intelligent Machining: Real-Time Tool Condition Monitoring and Intelligent Adaptive Control Systems, Journal of Machine Engineering, 18/1, 5–17.
LIU C., WU J-Q., LIU H-L., LI G-H., TAN G-Y., 2016, Geometry Features of Breakage Section and Variation of Cutting Force for End Mills AFter Brittle Breakage, Int. J. Adv. Manuf. Technol., 5–8, 1–14.
NOURI M., FUSSELL B.K., ZINITI B.L., LINDER E., 2015, Real-Time Tool Wear Monitoring in Milling Using a Cutting Condition Independent Method, International Journal of Machine Tools and Manufacture, 89, 1–13.
AMMOURI A., HAMADE R., 2014, Current Rise Criterion: a Process-Independent Method for Tool-Condition Monitoring and Prognostics, Int. J. Adv. Manuf. Technol., 72, 509–519.
HASSAN M., SADEK A., ATTIA M.H, 2019, A Generalized Multisensor Real-Time Tool Condition–Monitoring Approach Using Deep Recurrent Neural Network, Smart and Sustainable Manufacturing Systems. 3/2.
HASSAN M., SADEK A., DAMIR A., ATTIA M., THOMSON V., 2016, Tool Pre-Failure Monitoring in Intermittent Cutting Operations, ASME, International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, V002T02A49-VT02A49.
HASSAN M., SADEK A., DAMIR A., ATTIA M.H., THOMSON V., 2018, A Novel Approach for Real-Time Prediction and Prevention of Tool Chipping in Intermittent Turning Machining, CIRP Annals, 67, 41–44.
HASSAN M., SADEK A., ATTIA M.H., 2021, Novel Sensor-Based Tool Wear Monitoring Approach for Seamless Implementation in High Speed Milling Applications, CIRP Annals, 70/1, 87–90.
MOHANRAJ T., YERCHURU .J, KRISHNAN H., NITHIN ARAVIND RS., YAMENI R., 2021, Development of Tool Condition Monitoring System in End Milling Process Using Wavelet Features and Hoelder’s Exponent with Machine Learning Algorithms, Measurement, 173, 108671.
WANG G., ZHANG Y., LIU C., XIE Q., XU Y., 2019, A New Tool Wear Monitoring Method Based on Multi-Scale PCA, Journal of Intelligent Manufacturing, 30, 113–22.
LI T., SHI T., TANG Z., LIAO G., DUAN J., HAN J., et al., 2021, Real-Time Tool Wear Monitoring Using Thin-Film Thermocouple, Journal of Materials Processing Technology, 288, 116901.
TAO Z., AN Q., LIU G., CHEN M., 2019, A Novel Method for Tool Condition Monitoring Based on Long Short- Term Memory and Hidden Markov Model Hybrid Framework in High-Speed Milling Ti-6Al-4V, Int. J. Adv. Manuf. Technol, 3165–3182.
HE Z., SHI T., XUAN J., LI T., 2021, Research on Tool Wear Prediction Based on Temperature Signals and Deep Learning, Wear, 478–479.
LUO M., LUO H., AXINTE D., LIU D., MEI J., LIAO Z., 2018, A Wireless Instrumented Milling Cutter System with Embedded PVDF Sensors, Mechanical Systems and Signal Processing, 110, 556–568.
ZHANG C., YAO X., ZHANG J., JIN H., 2016, Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations, Sensors (Basel), 16/6, 795.
CHUNG T-K., YEH P-C., LEE H., LIN C-M., TSENG C-Y., LO W-T., et al., 2016, An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring, Sensors, 16/3, 269.
XIE Z., LI J., LU Y., 2018, An Integrated Wireless Vibration Sensing Tool Holder for Milling Tool Condition Monitoring, Int. J. Adv. Manuf. Technol., 95, 2885–2896.
SERIN G., SENER B., OZBAYOGLU A.M., UNVER H.O., 2020, Review of Tool Condition Monitoring in Machining and Opportunities for Deep Learning, Int. J. Adv. Manuf. Technol., 109, 953-974.
MOHANRAJ T., SHANKAR S., RAJASEKAR R., SAKTHIVEL N.R., PRAMANIK A., 2020, Tool Condition Monitoring Techniques in Milling Process — a Review, Journal of Materials Research and Technology, 9, 1032–1042.
KUNTOGLU M., ASLAN A., PIMENOV D.Y., USCA U.A., SALUR E., GUPTA M.K., et al., 2020, A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends, Sensors (Basel), 21/1. 108.
WANG S-M., HO C-D., TSAI P-C., YEN C., 2014, Study of an Efficient Real-Time Monitoring and Control System for BUE and Cutter Breakage for CNC Machine Tools, Int. J. Precis. Eng. Manuf., 15, 1109–1115.
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, 4112–4128.
MOU W., JIANG Z., ZHU S., 2019, A Study of Tool Tipping Monitoring for Titanium Milling Based on Cutting Vibration, Int. J. Adv. Manuf. Technol., 104, 3457–3471.
HUANG C.Y., LEE R.M., YANG S.K., 2016, Implement of Low Cost MEMS Accelerometers for Vibration Monitoring of Milling Process, International Conference on Applied System Innovation (ICASI), 16227947, 1–4.
AGHAZADEH F., TAHAN A., THOMAS M., 2018, Tool Condition Monitoring Using Spectral Subtraction and Convolutional Neural Networks in Milling Process, Int. J. Adv. Manuf. Technol., 98, 3217–3227.
JAUREGUI J.C., RESENDIZ J.R., THENOZHI S., SZALAY T.A.J., TAKACS M., 2018, Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring, IEEE Access, 6, 6400–6410.
JING L., ZHAO M., LI P., XU X., 2017, A Convolutional Neural Network Based Feature Learning and Fault Diagnosis Method for the Condition Monitoring of Gearbox, Measurement, 111, 1–10.
YANG Y., HAO B., HAO X., LI L., CHEN N., XU T., et al., 2020, A Novel Tool (Single-Flute) Condition Monitoring Method for End Milling Process Based on Intelligent Processing of Milling Force Data by Machine Learning Algorithms, Int. J. Precis. Eng. Manuf., 21, 2159–2171.
WANG P., LIU Z., GAO R.X., GUO Y., 2019, Heterogeneous Data-Driven Hybrid Machine Learning for Tool Condition Prognosis, CIRP Annals, 68/1, 455–458.
CAGGIANO A., RIMPAULT X., TETI R., BALAZINSKI M., CHATELAIN J-F., NELE L., 2018, Machine Learning Approach Based on Fractal Analysis for Optimal Tool Life Exploitation in CFRP Composite Drilling for Aeronautical Assembly, CIRP Annals, 67/1, 483–486.
CAI J., LUO J., WANG S., YANG S., 2018, Feature selection in Machine Learning: A New Perspective, Neurocomputing, 300, 70–79.
LAURO C.H., BRANDAO L.C., BALDO D., REIS R.A., DAVIM J.P., 2014, Monitoring and Processing Signal Applied in Machining Processes – A review, Measurement, 58, 73–86.
KALVODA T., HWANG Y.R., 2010, A Cutter Tool Monitoring in Machining Process Using HilbertHuang Transform, International Journal of Machine Tools and Manufacture, 50/5, 495–501.
WESTERMARK P., 2017, Wavelets, Scattering Transforms and Convolutional Neural Networks: Tools for Image Processing, Uppsala University.
ANDEN J., MALLAT S., 2014, Deep Scattering Spectrum, IEEE Transactions on Signal Processing, 62, 4114–4128.
CUKA B., KIM D-W., 2017, Fuzzy Logic Based Tool Condition Monitoring For End-Milling, Robotics and Computer-Integrated Manufacturing, 47, 22–36.
ZHU K., ZHANG Y., 2018, A Cyber-Physical Production System Framework of Smart CNC Machining Monitoring System, IEEE/ASME Transactions on Mechatronics, 23, 2579–2586.
SADEK A., HASSAN M., ATTIA M.H., 2020, A New Cyber-Physical Adaptive Control System for Drilling of Hybrid Stacks, CIRP Annals, 69/1, 105–108.
JI W., WANG L., 2019, Industrial Robotic Machining: a Review, Int. J. Adv. Manuf. Technol., 103, 1239–1255.
Journals System - logo
Scroll to top