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http://dx.doi.org/10.12989/sss.2020.26.4.495

Quality monitoring of complex manufacturing systems on the basis of model driven approach  

Castano, Fernando (Spanish National Research Council-Technical University of Madrid, Centre for Automation and Robotics)
Haber, Rodolfo E. (Spanish National Research Council-Technical University of Madrid, Centre for Automation and Robotics)
Mohammed, Wael M. (Tampere University, Faculty of Engineering and Natural Sciences, FAST-Lab)
Nejman, Miroslaw (Warsaw University of Technology, Faculty of Production Engineering)
Villalonga, Alberto (Spanish National Research Council-Technical University of Madrid, Centre for Automation and Robotics)
Lastra, Jose L. Martinez (Tampere University, Faculty of Engineering and Natural Sciences, FAST-Lab)
Publication Information
Smart Structures and Systems / v.26, no.4, 2020 , pp. 495-506 More about this Journal
Abstract
Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.
Keywords
quality monitoring; model-driven; artificial intelligence-based models; surface roughness; fuzzy clustering; manufacturing; embedded systems; hybrid incremental model;
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1 Waewsak, C., Nopharatana, A. and Chaiprasert, P. (2010), "Neural-fuzzy control system application for monitoring process response and control of anaerobic hybrid reactor in wastewater treatment and biogas production", J. Environ. Sci., 22(12), 1883-1890. https://doi.org/10.1016/S1001-0742(09)60334-X.   DOI
2 Wang, Y.K., Chen, H.Y. and Chen, J.R. (2019), "Unobtrusive sleep monitoring using movement activity by video analysis", Electronics, 8(7), 812. https://doi.org/10.3390/electronics8070812.   DOI
3 Beruvides, G., Quiza, R., Del Toro, R. and Haber, R.E. (2013), "Sensoring systems and signal analysis to monitor tool wear in microdrilling operations on a sintered tungsten-copper composite material", Sens. Actuator A Phys., 199, 165-175. https://doi.org/10.1016/j.sna.2013.05.021.   DOI
4 Hashmi, S.W.A., Rehan, M., Aamir, M., Kumar, H. and Liaquat, F. (2014), "Distributed process monitoring and control using FPGA", Proceedings of the Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE), Aalborg, Denmark, May. https://doi.org/10.1109/VITAE.2014.6934456.
5 Guerra, R.H., Quiza, R., Villalonga, A., Arenas, J. and Castano, F. (2019), "Digital twin-based optimization for ultraprecision motion systems with backlash and friction", IEEE Access, 7, 93462-93472. https://doi.org/10.1109/ACCESS.2019.2928141.   DOI
6 Haber, R.E., Juanes, C., Del Toro, R. and Beruvides, G. (2015), "Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process", Comput. Ind., 74, 135-150. https://doi.org/10.1016/j.compind.2015.05.001.   DOI
7 Haber, R.E., Beruvides, G., Quiza, R. and Hernandez, A. (2017), "A simple multi-objective optimization based on the crossentropy method", IEEE Access, 5, 22272-22281. https://doi.org/10.1109/ACCESS.2017.2764047.   DOI
8 Hoang, D.T. and Kang, H.J. (2019), "Rolling element bearing fault diagnosis using convolutional neural network and vibration image", Cog. Syst. Res., 53, 42-50. https://doi.org/10.1016/j.cogsys.2018.03.002.   DOI
9 Hoppner, F. and Klawonn, F. (2003), "Improved fuzzy partitions for fuzzy regression models", Int. J. Approx. Reason., 32(2-3), 85-102. https://doi.org/10.1016/S0888-613X(02)00078-6.   DOI
10 Ramezani, M., Bathaei, A. and Zahrai, S.M. (2019), "Comparing fuzzy type-1 and 2 in semi-active control with TMD considering uncertainties", Smart Struct. Syst., Int. J., 23(2), 155-171. https://doi.org/10.12989/sss.2019.23.2.155.
11 Samanta, S. and Chakraborty, S. (2011), "Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm", Eng. Appl. Artif. Intell., 24(6), 946-957. https://doi.org/10.1016/j.engappai.2011.03.009.   DOI
12 Huang, S., Zhang, D.H., Leong, W.Y., Chan, H.L., Goh, K.M., Zhang, J.B. and Kristo, K. (2008), "Detecting tool breakage using accelerometer in ball-nose end milling", Proceedings of the 2008 10th International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam, December. https://doi.org/10.1109/ICARCV.2008.4795642.
13 Humphreys, I., Eisenblätter, G. and O'Donnell, G.E. (2014), "FPGA based monitoring platform for condition monitoring in cylindrical grinding", Procedia CIRP, 14, 448-453. https://doi.org/10.1016/j.procir.2014.03.022.   DOI
14 Ranjan, J., Patra, K., Szalay, T., Mia, M., Gupta, M.K., Song, Q., Krolczyk, G., Chudy, R., Pashnyov, V.A. and Pimenov, D.Y. (2020), "Artificial intelligence-based hole quality prediction in micro-drilling using multiple sensors", Sensors, 20(3), 885. https://doi.org/10.3390/s20030885.   DOI
15 Reichenbach, M., Pfundt, B. and Fey, D. (2014). "Designing and manufacturing of real embedded multi-core CPUs: A holistic teaching approach in computer architecture", Proceedings of the Microelectronics Education (EWME), Tallinn, Estonia, May. https://doi.org/10.1109/EWME.2014.6877428.
16 Salman, H., Uddin, M.N., Acheampong, S. and Xu, H. (2019), "Design and implementation of IoT based class attendance monitoring system using computer vision and embedded linux platform", Proceedings of the Workshops of the International Conference on Advanced Information Networking and Applications, Matsue, Japan, March. https://doi.org/10.1007/978-3-030-15035-8_3.
17 Sevilla-Camacho, P.Y., Herrera-Ruiz, G., Robles-Ocampo, J.B. and Jauregui-Correa, J.C. (2011), "Tool breakage detection in CNC high-speed milling based in feed-motor current signals", Int. J. Adv. Manuf. Technol., 53(9-12), 1141-1148. https://doi.org/10.1007/s00170-010-2907-9.   DOI
18 Silva Junior, M.M., Cruz, F.C., Farias, P.C.M.A., Simas Filho, E.F., Albuquerque, M.C.S., Da Silva, I.C. and Farias, C.T.T. (2015), "Neural decision support system for ultrasound nondestructive evaluation embedded in a DSP", Proceedings of the Instrumentation and Measurement Technology Conference (I2MTC), Pisa, Italy, May. https://doi.org/10.1109/I2MTC.2015.7151304.
19 Yen, C.L., Lu, M.C. and Chen, J.L. (2013), "Applying the selforganization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting", Mech. Syst. Signal Process., 34(1-2), 353-366. https://doi.org/10.1016/j.ymssp.2012.05.001.   DOI
20 Xu, X., Huang, Q., Ren, Y., Zhao, D.Y. and Yang, J. (2019), "Sensor fault diagnosis for bridge monitoring system using similarity of symmetric responses", Smart Struct. Syst., Int. J., 23(3), 279-293. https://doi.org/10.12989/sss.2019.23.3.279.
21 Yi, T.H., Li, H.N. and Zhang, X.D. (2015), "Sensor placement optimization in structural health monitoring using distributed monkey algorithm", Smart Struct. Syst., Int. J., 15(1), 191-207. https://doi.org/10.12989/sss.2015.15.1.191.   DOI
22 Yu, J.B. and Xi, L.F. (2009), "A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes", Expert Syst. Appl., 36(1), 909-921. https://doi.org/10.1016/j.eswa.2007.10.003.   DOI
23 Zarkogianni, K., Mitsis, K., Litsa, E., Arredondo, M.T., Ficο, G., Fioravanti, A. and Nikita, K.S. (2015), "Comparative assessment of glucose prediction models for patients with type 1 diabetes mellitus applying sensors for glucose and physical activity monitoring", Med. Biol. Eng. Comput., 53, 1333-1343. https://doi.org/10.1007/s11517-015-1320-9.   DOI
24 Zhang, F., Yang, Y., Ye, X., Yang, J. and Han, B. (2019), "Structural modal identification and MCMC-based model updating by a Bayesian approach", Smart Struct. Syst., Int. J., 24(5), 631-639. https://doi.org/10.12989/sss.2019.24.5.631.
25 Beruvides, G., Juanes, C., Castano, F. and Haber, R.E. (2015), "A self-learning strategy for artificial cognitive control systems", Proceedings of the 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), Cambridge, UK, July .
26 Zhu, L., Chung, F.L. and Wang, S. (2009), "Generalized fuzzy cmeans clustering algorithm with improved fuzzy partitions", IEEE Trans. Syst. Man. Cybern. Part B Cybern., 39(3), 578-591. https://doi.org/10.1109/TSMCB.2008.2004818.   DOI
27 Zhu, Y.M., Chen, J.P. and Zheng, G. (2011), "Application of neural network on burr expert system in micro-machining", Int. J. Intell. Syst. Appl., 3(1), 1-9.   DOI
28 Beruvides, G., Quiza, R., Del Toro, R., Castaño, F. and Haber, R.E. (2014a), "Correlation of the holes quality with the force signals in a microdrilling process of a sintered tungsten-copper alloy", Int. J. Precis. Eng. Manuf., 15(9), 1801-1808. https://doi.org/10.1007/s12541-014-0532-5.   DOI
29 Beruvides, G., Quiza, R., Rivas, M., Castano, F. and Haber, R.E. (2014b), "A fuzzy-genetic system to predict the cutting force in microdrilling processes", Proceedings of the Industrial Electronics Society, IECON 2014 - 40th Annual Conference of the IEEE, Dallas, TX, USA, October.
30 Beruvides, G., Quiza, R., Rivas, M., Castano, F. and Haber, R.E. (2014c), "Online detection of run out in microdrilling of tungsten and titanium alloys", Int. J. Adv. Manuf. Technol., 74(9-12), 1567-1575. https://doi.org/10.1007/s00170-014-6091-1.   DOI
31 Kamel, T., Biletskiy, Y. and Chang, L. (2015), "Fault diagnosis and on-line monitoring for grid-connected single-phase inverters", Elec. Power Syst. Res., 126, 68-77. https://doi.org/10.1016/j.epsr.2015.05.001.   DOI
32 Beruvides, G., Castaño, F., Quiza, R. and Haber, R.E. (2016a), "Surface roughness modeling and optimization of tungstencopper alloys in micro-milling processes", Measurement, 86, 246-252. https://doi.org/10.1016/j.measurement.2016.03.002.   DOI
33 Beruvides, G., Quiza, R. and Haber, R.E. (2016b), "Multiobjective optimization based on an improved cross-entropy method: A case study of a micro-scale manufacturing process", Inf. Sci., 334, 161-173. https://doi.org/10.1016/j.ins.2015.11.040.   DOI
34 Beruvides, G., Castaño, F., Haber, R.E., Quiza, R. and Villalonga, A. (2017), "Coping with complexity when predicting surface roughness in milling processes: Hybrid incremental model with optimal parametrization", Complexity, 2017, 7317254. https://doi.org/10.1155/2017/7317254.
35 Iarovyi, S., Lastra, J.L.M., Haber, R. and Toro, R.D. (2015), "From artificial cognitive systems and open architectures to cognitive manufacturing systems", Proceedings of the 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), Cambridge, UK, July. https://doi.org/10.1109/INDIN.2015.7281910.
36 Jemielniak, K., Bombiński, S. and Aristimuno, P.X. (2008), "Tool condition monitoring in micromilling based on hierarchical integration of signal measures", CIRP Ann. Manuf. Technol., 57(1), 121-124. https://doi.org/10.1016/j.cirp.2008.03.053.   DOI
37 Kiswanto, G., Zariatin, D.L. and Ko, T.J. (2014), "The effect of spindle speed, feed-rate and machining time to the surface roughness and burr formation of aluminum alloy 1100 in micromilling operation", J. Manuf. Process., 16(4), 435-450. https://doi.org/10.1016/j.jmapro.2014.05.003.   DOI
38 Kothamasu, R. and Huang, S.H. (2007), "Adaptive Mamdani fuzzy model for condition-based maintenance", Fuzzy Sets Syst., 158(24), 2715-2733. https://doi.org/10.1016/j.fss.2007.07.004.   DOI
39 Kryjak, T., Komorkiewicz, M. and Gorgon, M. (2018), "Real-time hardware-software embedded vision system for ITS smart camera implemented in zynq SoC", J. Real Time Image Process., 15(1), 123-159. https://doi.org/10.1007/s11554-016-0588-9.   DOI
40 Zoroglu, C. and Turkeli, S. (2016), "Fuzzy expert system for severity prediction of obstructive sleep apnea hypopnea syndrome", J. Cog. Syst., 2(2), 37-43.
41 Foukarakis, M., Leonidis, A., Antona, M. and Stephanidis, C. (2014), Combining Finite State Machine and Decision-Making Tools for Adaptable Robot Behavior, Springer International Publishing, Greece.
42 Castano, F., Strzelczak, S., Villalonga, A., Haber, R.E. and Kossakowska, J. (2019), "Sensor reliability in cyber-physical systems using internet-of-things data: A review and case study", Remote Sens., 11(19), 2252. https://doi.org/10.3390/rs11192252.   DOI
43 La Fe-Perdomo, I., Beruvides, G., Quiza, R., Haber, R. and Rivas, M. (2019), "Automatic selection of optimal parameters based on simple soft-computing methods: A case study of micromilling processes", IEEE Trans. Ind. Inf., 15(2), 800-811. https://doi.org/10.1109/TII.2018.2816971.   DOI
44 Lee, Y., Lee, S., Zhao, X.G., Lee, D., Kim, T., Jung, H. and Kim, N. (2018), "Impact of UV curing process on mechanical properties and dimensional accuracies of digital light processing 3D printed objects", Smart Struct. Syst., Int. J., 22(2), 161-166. https://doi.org/10.12989/sss.2018.22.2.161.
45 Brinkschulte, U., Bechina, A., Picioroaga, F., Schneider, E., Ungerer, T., Kreuzinger, J. and Pfeffer, M. (2001), "A microkernel middleware architecture for distributed embedded real-time systems", Proceedings of the Reliable Distributed Systems, 20th IEEE Symposium, New Orleans, LA, USA, October. https://doi.org/10.1109/RELDIS.2001.970772.
46 Castano, F., Torelli, G., Perez-Aloe, R. and Carrillo, J.M. (2010), "Low-voltage rail-to-rail bulk-driven CMFB network with improved gain and bandwidth", Proceedings of the 2010 IEEE International Conference on Electronics, Circuits and Systems, ICECS 2010, Athens, Greece, December. https://doi.org/10.1109/ICECS.2010.5724490.
47 Castano, F., Del Toro, R.M., Beruvides, G. and Haber, R.E. (2015a), "Application of hybrid incremental modeling for predicting surface roughness in micromachining processes", Proceedings of the 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, Orlando, FL, USA, December. https://doi.org/10.1109/CIES.2014.7011831.
48 Castano, F., Toro, R.M.D., Haber, R.E. and Beruvides, G. (2015b), "Conductance sensing for monitoring micromechanical machining of conductive materials", Sens. Actuator A Phys., 232, 163-171. https://doi.org/10.1016/j.sna.2015.05.015.   DOI
49 Castano, F., Haber, R.E. and Del Toro, R.M. (2017), "Characterization of tool-workpiece contact during the micromachining of conductive materials", Mech. Syst. Signal Process., 83, 489-505. https://doi.org/10.1016/j.ymssp.2016.06.027.   DOI
50 Choi, G.P., Kim, D.Y., Yoo, K.H. and Na, M.G. (2016), "Prediction of hydrogen concentration in nuclear power plant containment under severe accidents using cascaded fuzzy neural networks", Nucl. Eng. Des., 300, 393-402. https://doi.org/10.1016/j.nucengdes.2016.02.015.   DOI
51 Ma, Y., Jia, X., Hu, Q., Xu, D., Guo, C., Wang, Q. and Wang, S. (2019), "Laplace prior-based bayesian compressive sensing using K-SVD for vibration signal transmission and fault detection", Electronics, 8(5), 517. https://doi.org/10.3390/electronics8050517.   DOI
52 Li, X., Gao, L., Shao, X., Zhang, C. and Wang, C. (2010), "Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling", Comput. Oper. Res., 37(4), 656-667. https://doi.org/10.1016/j.cor.2009.06.008.   DOI
53 Lim, D.J. (2019), "Incorporating a model-driven approach into an embedded software course", Electronics, 8(9), 1004. https://doi.org/10.3390/electronics8091004.   DOI
54 Lipinski, D. and Majewski, M. (2015), Intelligent Monitoring and Optimization of Micro- and Nano-Machining Processes, Springer International Publishing, Poland.
55 Marani Barzani, M., Zalnezhad, E., Sarhan, A.A.D., Farahany, S. and Ramesh, S. (2015), "Fuzzy logic based model for predicting surface roughness of machined Al-Si-Cu-Fe die casting alloy using different additives-turning", Measurement, 61, 150-161. https://doi.org/10.1016/j.measurement.2014.10.003.   DOI
56 Dzakpasu, M., Scholz, M., McCarthy, V., Jordan, S. and Sani, A. (2015), "Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands", Water Sci. Technol., 71(1), 22-30. https://doi.org/10.2166/wst.2014.461.   DOI
57 Mohammed, W.M., Ferrer, B.R., Iarovyi, S., Negri, E., Fumagalli, L., Lobov, A. and Martinez Lastra, J.L. (2018a), "Generic platform for manufacturing execution system functions in knowledge-driven manufacturing systems", Int. J. Comput. Integr. Manuf., 31(3), 262-274. https://doi.org/10.1080/0951192X.2017.1407874.   DOI
58 Mohammed, W.M., Ferrer, B.R., Martinez, J.L., Sanchis, R., Andres, B. and Agostinho, C. (2018b), "A multi-agent approach for processing industrial enterprise data", Proceedings of the 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017, Funchal, Portugal, June. https://doi.org/10.1109/ICE.2017.8280018.
59 Constantinou, A.C., Fenton, N., Marsh, W. and Radlinski, L. (2016), "From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support", Artif. Intell. Med., 67, 75-93. https://doi.org/10.1016/j.artmed.2016.01.002.   DOI
60 Di Francescomarino, C., Dumas, M., Federici, M., Ghidini, C., Maggi, F.M., Rizzi, W. and Simonetto, L. (2018), "Genetic algorithms for hyperparameter optimization in predictive business process monitoring", Inf. Syst., 74, 67-83. https://doi.org/10.1016/j.is.2018.01.003.   DOI
61 Ercetin, A., Aslantas, K. and Percin, M. (2018), "Micro milling of tungsten-copper composite materials produced through powder metallurgy method: Effect of composition and sintering temperature", J. Fac. Eng. Archit. Gazi Univ., 33(4), 1369-1381.
62 Fiol-Roig, G. (2004), "Knowledge-based architecture for real time supervision of dynamic processes", Proceedings of the Eighth IASTED International Conference on Artificial Intelligence and Soft Computing, Marbella, Spain, September.
63 Flouri, K., Saukh, O., Sauter, R., Jalsan, K.E., Bischoff, R., Meyer, J. and Feltrin, G. (2012), "A versatile software architecture for civil structure monitoring with wireless sensor networks", Smart Struct. Syst., Int. J., 10(3), 209-228. http://dx.doi.org/10.12989/sss.2012.10.3.209.   DOI
64 Gajate, A., Haber, R.E., Vega, P.I. and Alique, J.R. (2010), "A transductive neuro-fuzzy controller: Application to a drilling process", IEEE Trans. Neural Netw., 21(7), 1158-1167. http://doi.org/10.1109/TNN.2010.2050602.   DOI
65 Gao, Q. (2012), "A method of tool breakage detection in CNC high-speed milling", Appl. Mech. Mater., 138-139, 598-603. https://doi.org/10.4028/www.scientific.net/AMM.138-139.598.   DOI
66 Palani, S., Natarajan, U. and Chellamalai, M. (2013), "On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS)", Mach. Vis. Appl., 24(1), 19-32. https://doi.org/10.1007/s00138-011-0378-0.   DOI
67 Monks, U., Trsek, H., Dürkop, L., GeneiB, V. and Lohweg, V. (2015), "Towards distributed intelligent sensor and information fusion", Mechatronics, 34, 63-71. https://doi.org/10.1016/j.mechatronics.2015.05.005.   DOI
68 Mosavi, A., Ozturk, P. and Chau, K.W. (2018), "Flood prediction using machine learning models: Literature review", Water, 10(11), 1536. https://doi.org/10.3390/w10111536.   DOI
69 Muhammad, U., Ferrer, B.R., Mohammed, W.M. and Lastra, J.L.M. (2018), "An approach for implementing key performance indicators of a discrete manufacturing simulator based on the ISO 22400 standard", Proceedings of the 2018 IEEE Industrial Cyber-Physical Systems (ICPS), St. Petersburg, Russia, May. https://doi.org/10.1109/ICPHYS.2018.8390779.
70 Onat, O. and Gul, M. (2018), "Application of artificial neural networks to the prediction of out-of-plane response of infill walls subjected to shake table", Smart Struct. Syst., Int. J., 21(4), 521-535. https://doi.org/10.12989/sss.2018.21.4.521.
71 Park, S., Jeong, H., Min, H., Lee, H. and Lee, S. (2018), "Waveletlike convolutional neural network structure for time-series data classification", Smart Struct. Syst., Int. J., 22(2), 175-183. https://doi.org/10.12989/sss.2018.22.2.175.
72 Penedo, F., Haber, R.E., Gajate, A. and Del Toro, R.M. (2012), "Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes", IEEE Trans. Ind. Inf., 8(4), 811-818. https://doi.org/10.1109/TII.2012.2205699.   DOI
73 Rajesh Kumar, P., Vinod, Y. and Ramkumar, J. (2014), "Neural network based modelling and GRA coupled PCA optimization of hole sinking electro discharge micromachining", Int. J. Manuf. Mater. Mech. Eng., 4(1), 1-21. https://doi.org/10.4018/ijmmme.2014010101.   DOI
74 Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J. and Nenadic, G. (2019), "Machine learning methods for wind turbine condition monitoring: A review", Renew. Energ., 133, 620-635. https://doi.org/10.1016/j.renene.2018.10.047.   DOI
75 Suganthi, X.H., Natarajan, U., Sathiyamurthy, S. and Chidambaram, K. (2013), "Prediction of quality responses in micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model", Int. J. Adv. Manuf. Technol., 68(1-4), 339-347. https://doi.org/10.1007/s00170-013-4731-5.   DOI
76 Treutterer, W., Cole, R., Luddecke, K., Neu, G., Rapson, C., Raupp, G., Zasche, D. and Zehetbauer, T. (2014), "ASDEX upgrade discharge control system - a real-time plasma control framework", Fusion Eng. Des., 89(3), 146-154. https://doi.org/10.1016/j.fusengdes.2014.01.001.   DOI
77 Turing, A.M. (2009), Computing Machinery and Intelligence, Springer, Netherlands.
78 Venkatesh, V., Swain, N., Srinivas, G., Kumar, P. and Barshilia, H.C. (2017), "Review on the machining characteristics and research prospects of conventional microscale machining operations", Mater. Manuf. Process., 32(3), 235-262. https://doi.org/10.1080/10426914.2016.1151045.   DOI
79 Villalonga, A., Beruvides, G., Castano, F. and Haber, R. (2020), "Cloud-based industrial cyber-physical system for data-driven reasoning: A review and use case on an industry 4.0 pilot line", IEEE Trans. Ind. Inf., 16(9), 5975-5984.   DOI
80 AitMou, Y., Elgendy, M., Jan, S., Lucas, A.M., Elzein, A. and Bermak, A. (2018), "Smart wearable sensing platform with wireless communication and embedded processing for health monitoring applications", Proceedings of the Qatar Foundation Annual Research Conference, Doha, Qatar, March.
81 Alakesh, M. (2012), Taguchi, Fuzzy Logic and Grey Relational Analysis Based Optimization of ECSM Process during Micro Machining of E-Glass-Fibre-Epoxy Composite, IGI Global, Hershey, PA, USA.
82 Azmi, A.I. (2015), "Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites", Adv. Eng. Soft., 82, 53-64. https://doi.org/10.1016/j.advengsoft.2014.12.010.   DOI