Speed Control of an Electrical Cable Extrusion Process Using Artificial Intelligence-Based Technique

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Robert Agyare Ofosu
Erwin Normanyo
N-Yo Abdul-Aziz
Stephen Smart Stickings



Most cable manufacturing companies use Programmable Logic Controllers with conventional controllers to control line speed during cable extrusion. These traditional controllers have difficulties keeping the line speed constant, causing surface defects on the extruded cables and affecting the quality of the manufactured cables. To overcome these challenges, data on the causes of defects during cable manufacturing were collected from a cable manufacturing company in Ghana to ascertain the possible causes during cable manufacturing. Adaptive Neuro-Fuzzy Inference System (ANFIS) controller was designed to provide a constant line speed during the cable extrusion process. To ascertain its robustness, the ANFIS controller was compared to a conventional Proportional Integral Derivative controller and a Fuzzy Logic controller. The controllers were designed and simulated using MATLAB/Simulink software. The analysis of the collected data indicated that a break in insulation/ sheath was a frequently occurring defect during the cable manufacturing process due to improper line speed control of the machines used in the cable manufacturing process. Based on the results obtained from the various controllers, it was concluded that the ANFIS controller was robust in achieving stability regarding line speed variations.


A. T. William, Electrical power cable engineering, 1st ed. 711 Third Avenue, New York. CRC Press Publications, 2012.

I. Sazirul, “What is electrical cable”, 2017. [Online] Available: https://www.quora.com/what-is-the-electrical-cable. [Accessed September 21, 2019].

P. Shuan, “An Electrical Cable”, 2019. [Online] Available: https://www.quora.com/what-is-the-electrical-cable. [Accessed September 2019].

D. Goran, “Copper vs. aluminium – substitution slows but continues”, 2016. [Online] Available: https://aluminiuminsider.com/copper-vs-aluminium-substitution-slows-but-continues/. [Accessed May 28, 2020].

K. G. Sushil, and K. Vivek, Power cable technology, 1st ed. 711 Third Avenue, New York. CRC Press Publications, 2016

R. A. Ofosu, E. Normanyo, and L. Obeng, “Temperature control of heaters in cable extrusion machine using PSO-ANFIS controller”, 2019 IEEE AFRICON, pp. 1-9, 2020.

E. K. Addai, K. T. Samuel, S. A. Joe and I. Isaac, “Trend of fire outbreaks in ghana and ways to prevent these incidents”, 2016. [Online] Available: https://www.researchgate net/publication./297645558. [Accessed: October 28, 2019].

N. Narasimha, and R. Rejikumar. “Plastic pipe defects minimization” International Journal of Innovative Research and Development, vol. 2, pp. 1337-1351, 2013.

S. S. M. Subramanian, T. hirumarimurugan, C. N. Shruthi, G. Sowndarya and G. Swathy, “A novel model based controller for polymer extrusion process”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 5,pp. 163-166, . 2016a.

M. Thirumarimurugan, S. S. Subramanian, and M. Ramasubramanian, “Performance evaluation of extrusion process”, Journal of Applied Science Research, vol. 12, pp. 65-70, 2016.

S. S. Subramanian, and M. Thirumarimurugan, “performance enhancement of the extrusion process with smith predictor and AWPI”, Asian Journal of Research in Social Sciences and Humanities, vol. 6, pp. 485-489, 2016b.

C. C. Mbaocha, N. C. Amaeze, and P. C. Eze, “Design of a plastic extrusion system controller”, International Journal of Scientific and Engineering Research, vol. 7, pp. 595-598, 2016.

V. N. Mitroshin, “System for distributed control of melt temperature of polymer in a screw extruder” Industrial Engineering, Applications and Manufacturing (ICIEAM), 2017 International Conference IEEE, pp. 1-5. Sankt-Peterburg, Russia, 2017.

M. Mayda, “Barrel temperature control for quality of thermoplastic polymers in the extrusion process” Control and Systems Engineering, vol. 2, pp. 1-6, 2018.

C. Belavý, G. Hulkó, D. Šišmišová, and M. Kubiš. “FEM based modeling and control of temperature field in extruder barrel”, Proceedings of the 29th International Conference 2018 Cybernetics & Informatics (K&I), Lazy pod Makytou, Slovakia, 2018, pp.1-6.

Anon., “Wire and cable extrusion control”, 2019. [Online] Available: https://www.maguire.com/wire-cable-extrusion-1/. [Accessed August 21, 2019].

V. K. Mehta, and R. Mehta. Principles of electrical machines. 6th ed. New Delhi. Schand Publications, 2016.

T., Edward, “Why is the induction motor more used in industry than a synchronous motor or dc motor?”, 2020. [Online] Available: https://www.quora.com/why-is-the-induction-motor-more-used-in-industry-than-a-synchronous-motor-or-dc-motor. [Accessed January 20, 2020].

G. A. Fayez, Y. H. Amira, and H. M. Reham, “Adaptive Neuro-Fuzzy Control of an Induction Motor” Ain Shams Engineering Journal, vol.1, pp.71-78. 2010.

D. S. Hooda, and Vivek., Fuzzy logic models and fuzzy control: an introduction, 1st ed. Oxford, UK: alpha science international ltd, R. 2017.

M. Kushwah, and Patra, A., “PID controller tuning using ziegler-nichols method for speed control of DC motor”, International Journal of Scientific Engineering and Technology Research, vol. 3, pp.2924- 2929, 2014.

Z. GarcÃa, and E. Yohn, “Fuzzy logic in process control: a new fuzzy logic controller and an improved fuzzy-internal model controller” Master’s thesis, University of South Florida, USA, 2006.

N. Elias, N. M. Yahya and E. H. Sing, “Numerical analysis of fuzzy logic temperature and humidity control system in pharmaceutical warehouse using MATLAB fuzzy toolbox: intelligent manufacturing and mechatronics”, Proceedings of Symposium, Pahang, Malaysia, Springer Singapore, pp.623-629, 2018.

J. T. Dzib, E. J. Alejos-Moo., A. Bassam., M. Flota-Bañuelo., M. A. Escalante-Soberanis, L. J. Ricalde, and M. J. López-Sánchez, “Photovoltaic module temperature estimation: a comparison between artificial neural networks and adaptive neuro fuzzy inference systems models”, International Symposium on Intelligent Computing Systems, vol.10, pp.46-60, 2016.

M. Sahin, and R. Erol, “A comparative study of neural networks and ANFIS for forecasting attendance rate of soccer games”, Mathematical and Computational Applications, vol. 22, pp. 1-43, 2017.

B. Mrinal, “Adaptive network based fuzzy inference system (ANFIS) as a tool for system identification with special emphasis on training data minimization”, PhD Thesis., Indian Institute of Technology, 2008.

J. G. Monicka, N. O. G. Sekhar, and K. R. Kumar, “Performance evaluation of membership functions on fuzzy logic controlled ac voltage controller for speed control of induction motor drive”, International Journal of Computer Applications, vol. 13, pp. 975-8887, 2011.

H. C. W. Lau, N. Dilupa, and Z. Li., “Development of a hybrid fuzzy genetic algorithm model for solving transportation scheduling problem”, Journal of Information Systems and Technology Management, vol. 12, pp. 505-524, 2015.

R., P. Singh, Kuchhal, S. Choudhury, and A. Gehlot, “Implementation and evaluation of heating system using PID with genetic algorithm”, Indian Journal of Science and Technology, vol. 8, pp. 413-418, 2015.