Gestão & Produção
Gestão & Produção
Artigo Original

A cyber process control system based on pattern recognition and cloud computing

Amr Mohamed Ali; Soumaya Yacout; Eladl Rabeih; Yasser Shaban

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Abstract:: This paper presents a novel simulation model of the Cyber Process Control System (CPCS) by combining pattern recognition and Cloud Computing (CC). This paper's originality arises from its aim to build a cloud computing platform for autonomous machines, and the exploration of manufacturing data to generate interpretable patterns to be used in process control decision making. The combining of Cloud technology and machine learning brings production to Industry 4.0. The proposed system is tested using data Carbon Fiber Reinforced Polymer (CFRP) routing process. The little information available about the manufacturing process of this type of material and the interaction between the production steps makes the manufacturing process quite difficult. This system generates interpretable rules of controllable operating parameters sent to the controller to keep the machining process within the limits of the specifications. The second step is activated during the drifting conditions in the machining step. Also, the simulation of the machining process is illustrated to generate the relations between input and output variables of the machining process. The findings of the corrective actions are illustrated and the interaction between the two industrial steps is simulated. Finally, current and future CPCS and CC applications in Industry 4.0 are discussed.


Process control, Pattern recognition, Multi-class logical analysis of data, Cyber-physical system, Industry 4.0, Cloud computing


Alexe, G., Alexe, S., Liotta, L. A., Petricoin, E., Reiss, M., & Hammer, P. L. (2004). Ovarian cancer detection by logical analysis of proteomic data. Proteomics, 4(3), 766-783. PMid:14997498.

Avram, M. G. (2014). Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technology, 12, 529-534.

Che, D., Saxena, I., Han, P., Guo, P., & Ehmann, K. F. (2014). Machining of carbon fiber reinforced plastics/polymers: a literature review. Journal of Manufacturing Science and Engineering, 136(3), 034001.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.

Davim, J. P., & Reis, P. (2005). Damage and dimensional precision on milling carbon fiber-reinforced plastics using design experiments. Journal of Materials Processing Technology, 160(2), 160-167.

De Paula Ferreira, W., Armellini, F., & De Santa-Eulalia, L. A. (2020). Simulation in industry 4.0: a state-of-the-art review. Computers & Industrial Engineering, 149, 106868.

Ferreira, J. R., Coppini, N. L., & Miranda, G. W. A. (1999). Machining optimisation in carbon fibre reinforced composite materials. Journal of Materials Processing Technology, 92-93, 135-140.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explorations, 11(1), 10-18.

Huang, P. B. (2016). An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations. Journal of Intelligent Manufacturing, 27(3), 689-700.

Jiang, P., Jia, F., Wang, Y., & Zheng, M. (2014). Real-time quality monitoring and predicting model based on error propagation networks for multistage machining processes. Journal of Intelligent Manufacturing, 25(3), 521-538.

Liang, S. Y., Hecker, R. L., & Landers, R. G. (2004). Machining process monitoring and control: the state-of-the-art. Journal of Manufacturing Science and Engineering, 126(2), 297-310.

Mallick, P. K. (2007). Fiber-reinforced composites: materials, manufacturing, and design. Boca Raton: CRC Press.‏

McCulloch, W. S., & Pitts, W. (1990). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology, 52(1-2), 99-115. PMid:2185863.

McFarlane, D., Sarma, S., Chirn, J. L., Wong, C., & Ashton, K. (2003). Auto ID systems and intelligent manufacturing control. Engineering Applications of Artificial Intelligence, 16(4), 365-376.

Merdan, M., Hoebert, T., List, E., & Lepuschitz, W. (2019). Knowledge-based cyber-physical systems for assembly automation. Production & Manufacturing Research, 7(1), 223-254.

Meshreki, M., Sadek, A., & Attia, M. H. (2012). High speed routing of woven carbon fiber reinforced epoxy laminates. In ASME 2012 International Mechanical Engineering Congress and Exposition (pp. 2061-2066). New York: American Society of Mechanical Engineers.‏

Moreira, L. M. (2000). The use of Boolean concepts in general classification contexts (Doctoral dissertation). Ecole Polytechnique Federale de Lausanne, Lausanne.‏

Mortada, M. A., Yacout, S., & Lakis, A. (2014). Fault diagnosis in power transformers using multi-class logical analysis of data. Journal of Intelligent Manufacturing, 25(6), 1429-1439.

Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247-259.

Mourtzis, D. (2020). Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7), 1927-1949.

Mourtzis, D., Vlachou, E., Milas, N., Tapoglou, N., & Mehnen, J. (2019). A cloud-based, knowledge-enriched framework for increasing machining efficiency based on machine tool monitoring. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 233(1), 278-292.

Paiva, J. M. F. D., Santos, A. D. N. D., & Rezende, M. C. (2009). Mechanical and morphological characterizations of carbon fiber fabric reinforced epoxy composites used in aeronautical field. Materials Research, 12(3), 367-374.

Rashvand, H. F., Abedi, A., Alcaraz-Calero, J. M., Mitchell, P. D., & Mukhopadhyay, S. C. (2014). Wireless sensor systems for space and extreme environments: a review. IEEE Sensors Journal, 14(11), 3955-3970.

Shaban, Y., Meshreki, M., Yacout, S., Balazinski, M., & Attia, H. (2017). Process control based on pattern recognition for routing carbon fiber reinforced polymer. Journal of Intelligent Manufacturing, 28(1), 165-179.

Sharma, M., Gao, S., Mäder, E., Sharma, H., Wei, L. Y., & Bijwe, J. (2014). Carbon fiber surfaces and composite interphases. Composites Science and Technology, 102, 35-50.

Sharma, V. S., Dhiman, S., Sehgal, R., & Sharma, S. K. (2008). Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 19(4), 473-483.

Song, Z., & Moon, Y. (2019). Performance analysis of cyber manufacturing systems. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 233(5), 1362-1376.

Sorrentino, L., Turchetta, S., & Bellini, C. (2016). Milling machining of CFRPs: a model to simulate and forecast the cutting forces intime domain. IACSIT International Journal of Engineering and Technology, 8(5), 1880-1892.

Soutis, C. (2005). Fibre reinforced composites in aircraft construction. Progress in Aerospace Sciences, 41(2), 143-151.

Tyczyński, P., Lemańczyk, J., Ostrowski, R., & Ewa S´liwa, R. (2014). Drilling of CFRP, GFRP, glare type composites. Aircraft Engineering and Aerospace Technology: An International Journal, 86(4), 312-322.

Wolpert, D. H. (1996). The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7), 1341-1390.

Yacout, S., Salamanca, D., & Mortada, M.-A. (2012). Patent Cooperation Treaty PCT/CA2011/000876, No.Wo 2012/009804 A1.

Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: a review. Engineering, 3(5), 616-630.

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