Leading-Edge Production Engineering Technologies

Ovundah King Wofuru-Nyenke 1
1Department of Mechanical Engineering, Rivers State University, Port Harcourt, Nigeria.

International Journal of Engineering Analysis and Computations (IJEAC)
Volume 1, Issue 1, Pages 27 - 35
Published: 2 June 2025

Download

Abstract

Production engineering technologies are ever advancing to tackle problems encountered in manufacturing of products and rendering of services. This paper presents a number of challenges encountered by producers, and the industrial revolutions that these producers have kickstarted to handle these production challenges, while also identifying leading-edge production engineering technologies that have enabled these technological revolutions. The methodology employed was the systematic literature review of scholarly articles published between 2010 and 2021. The result of the research was the identification of some leading-edge production engineering technologies that are helping producers improve productivity such as robotics, smart factories and Internet of Things (IoT); Artificial Intelligence and predictive maintenance; 3D printing and additive manufacturing.

Keywords:
Additive Manufacturing Internet of Things Production Engineering Robotics Smart Factories
APA Referencing Format

Wofuru-Nyenke, O. K. (2025). Leading-Edge Production Engineering Technologies. International Journal of Engineering Analysis and Computations (IJEAC), 1(1), 27-35.

References

  1. Alberto, N., Domingues, M.F., Marques, C., André, P. & Antunes, P. (2018) Optical fiber magnetic field sensors based on magnetic fluid: A review. Sensors, 18 (12), 1 – 27.
  2. Al-Jlibawi, A., Othman, I.M.L., Al-Huseiny, M.S., Bin Aris, I. & Noor, S.B.M. (2019). Efficient soft sensor modelling for advanced manufacturing systems by applying hybrid intelligent soft computing techniques. International Journal of Simulation Systems Science & Technology, 19 (3).
  3. Bukhsh, Z.A., Saeed, A., Stipanovic, I. & Doree, A.G. (2019). Predictive maintenance using tree-based classification techniques: A case of railway switches. Transportation Research Part C: Emerging Technologies, 101, 35 – 54.
  4. Carvalho, T.P., Soares, F.A.A.M.N., Vita, R., Francisco, R.D.P., Basto, J.P. & Alcalá, S.G.S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers and Industrial Engineeering, 137, 1 – 10.
  5. Chien, C.F. & Chen, C.C. (2020) Data-driven framework for tool health monitoring and maintenance strategy for smart manufacturing. IEEE Transactions on Semiconductor Manufacturing, 33, 1 – 9.
  6. Christiansen, B. (2020). 5 Business Automation Mistakes: How to Avoid Them. Keap. Retrieved from https://keap.com/business-success-blog/marketing/automation/automation-mistakes
  7. Cottone, P., Re, G.L., Maida, G. & Morana, M. (2013). Motion sensors for activity recognition in an ambient-intelligence scenario. Proceedings of the 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), San Diego, CA, USA, 18–22 March 2013, 646 – 651.
  8. Dilmegani, C. (2020). Top 12 Use Cases/Applications of AI in Manufacturing. AI Multiple. Retrieved from https://research.aimultiple.com/manufacturing-ai/
  9. Economist. (2011). Print Me a Stradivarius: How a New Manufacturing Technology Will Change the World. http://www.economist.com/node/18114327.
  10. Farahani, H., Wagiran, R. & Hamidon, M.N. (2014). Humidity sensors principle, mechanism, and fabrication technologies: A comprehensive review. Sensors, 14, 7881 – 7939.
  11. Garrett, B. (2014). 3D Printing: New Economic Paradigms and Strategic Shifts. Global Policy, 5 (1), 70 – 75.
  12. Gebler, M., Uiterkamp, A.J.M.S. & Visser, C. (2014). A Global Sustainability Perspective on 3D Printing Technologies. Energy Policy, 74, 158 – 167.
  13. Gillis, A.S. (2021). What is Internet of Things? IoT Agenda. https://internetofthingsagenda.techtarget.com/definition/Internet-of-Things-IoT.
  14. Indri, M., Lachello, L., Lazzero, I., Sibona, F. & Trapani, S. (2019). Smart sensors applications for a new paradigm of a production line. Sensors, 19, 1 – 27.
  15. Jia, L., Chen, R., Xu, J., Zhang, L., Chen, X., Bi, N., Gou, J. & Zhao, T. (2021). A stick-like intelligent multicolor nano-sensor for the detection of tetracycline: The integration of nano-clay and carbon dots. Journal of Hazardous Materials. 413.
  16. Jureschi, C.M., Linares, J., Boulmaali, A., Dahoo, P.R., Rotaru, A. & Garcia, Y. (2016). Pressure and temperature sensors using two spin crossover materials. Sensors, 16, 1 – 27.
  17. Kantasa-ard, A., Nouiri, M., Bekrar, A., Ait el cadi, A., & Sallez, Y. (2020). Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand. International Journal of Production Research, 1 – 25.
  18. Kaptan, C., Kantarci, B., Soyata, T. & Boukerche, A. (2018). Emulating smart city sensors using soft sensing and machine intelligence: A case study in public transportation. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 20–24 May 2018; 1 – 7.
  19. Khan, A., & Turowski, K. (2016). A Survey of Current Challenges in Manufacturing Industry and Preparation for Industry 4.0. Advances in Intelligent Systems and Computing, 15–26.
  20. Kiangala, K.S. & Wang, Z. (2018). Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts. International Journal of Advanced Manufacturing Technology, 97, 3251 – 3271.
  21. Kozłowski, E., Mazurkiewicz, D., Żabiński, T., Prucnal, S. & Sęp, J. (2020). Machining sensor data management for operation-level predictive model. Expert Systems with Applications, 159.
  22. Luo, Z., Hu, X., Borisenko, V.E., Chu, J., Tian, X., Luo, C., Xu, H., Li, Q., Li, Q. & Zhang, J. (2019). Structure-property relationships in graphene-based strain and pressure sensors for potential artificial intelligence applications. Sensors, 19, 1 – 27.
  23. Mennel, L., Symonowicz, J., Wachter, S., Polyushkin, D.K., Molina-Mendoza, A.J. & Mueller, T. (2020). Ultrafast machine vision with 2D material neural network image sensors. Nature. 579, 62 – 66.
  24. Musselman, M. & Djurdjanovic, D. (2012) Tension monitoring in a belt-driven automated material handling system. CIRP Journal of Manufacturing Science and Technology, 5, 67 – 76.
  25. Olsson, N.O.E., Shafqat, A., Arica, E. & Økland, A. (2019). 3D-Printing Technology in Construction: Results from a Survey. Proceedings of the 10th Nordic Conference on Construction Economics and Organization (Emerald Reach Proceedings Series, Vol. 2), Emerald Publishing Limited, Bingley, 349 – 356.
  26. Pech, M., Vrchota, J. & Bednar, J. (2021). Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Sensors, 21(4).
  27. Rossi, B. (2018). What will Industry 5.0 mean for manufacturing? Raconteur. https://www.raconteur.net/manufacturing/manufacturing-gets-personal-industry-5-0/
  28. Ryu, S. & Kim, S.C. (2020). Impact sound-based surface identification using smart audio sensors with deep neural networks. IEEE Sensors Journal, 20, 10936 – 10944.
  29. Sadiki, S., Ramadany, M., Faccio, M., Amegouz, D. & Boutahari, S. (2019) Running smart monitoring maintenance application using cooja simulator. International Journal of Engineering Research in Africa. 42, 149 – 159.
  30. Salvatore, G.A., Sülzle, J., Kirchgessner, N., Hopf, R., Magno, M., Tröster, G., Valle, F.D., Cantarella, G., Robotti, F. & Jokic, P. (2017). Biodegradable and highly deformable temperature sensors for the internet of things. Advanced Functional Materials, 27(35), 1 – 10.
  31. Selcuk, S. (2016). Predictive maintenance, its implementation and latest trends. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(9), 1670 – 1679.
  32. Sergiyenko, O., Tyrsa, V., Flores-Fuentes, W., Rodriguez-Quiñonez, J. & Mercorelli, P. (2018) Machine vision sensors. Journal of Sensors, 2018, 1 – 2.
  33. Shi, Z., Xie, Y., Xue, W., Chen, Y., Fu, L., & Xu, X. (2020). Smart factory in Industry 4.0. Systems Research and Behavioral Science. 1 – 11.
  34. Shoaib, M., Bosch, S., Incel, O.D., Scholten, J. & Havinga, P.J.M. (2014). Fusion of smartphone motion sensors for physical activity recognition. Sensors 2014, 14, 10146 – 10176.
  35. Singh, K., Sharma, S., Shriwastava, S., Singla, P., Gupta M. & Tripathi, C.C. (2021). Significance of nano-materials, designs consideration and fabrication techniques on performances of strain sensors-A review. Materials Science in Semiconductor Processing, 123.
  36. Spiliotis, E. & Makridakis, S. (2020). Comparison of Statistical and Machine Learning Methods for Daily SKU Demand Forecasting. Operational Research. 20.
  37. Thakkar, S., Dumée, L.F., Gupta, M., Singh, B.R. & Yang, W. (2021). Nano–enabled sensors for detection of arsenic in water. Water Research, 188.
  38. Tortorella, G.L. (2018). An empirical analysis of total quality management and total productive maintenance in industry 4.0. In Proceedings of the International Conference on Industrial Engineering and Operations Management (IEOM), Pretoria/Johannesburg, South Africa, 29 October–1 November 2018; 742 – 753.
  39. Tumbleston, J. R., Shirvanyants, D., Ermoshkin, N., Janusziewicz, R., Johnson, A. R., Kelly, D., Chen, K., Pinschmidt, R., Rolland, J.P., Ermoshkin, A., Samulski, E.T. & DeSimone, J. M. (2015). Continuous liquid interface production of 3D objects. Science, 347(6228), 1349 – 1352.
  40. Uhlmann, E., Laghmouchi, A., Geisert, C. & Hohwieler, E. (2017). Smart wireless sensor network and configuration of algorithms for condition monitoring applications. Journal of Machine Engineering, 17, 45 – 55.
  41. Villalobos, K., Suykens, J. & Illarramendi, A. (2020). A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach. Journal of Intelligent Manufacturing. 1–22.
  42. Yin, Y., Stecke, K. E., & Li, D. (2017). The evolution of production systems from Industry 2.0 through Industry 4.0. International Journal of Production Research, 56(1-2), 848 – 861.
  43. Zhang, Y., Cheng, Y., Wang, X.V., Zhong, R.Y., Zhang, Y. & Tao, F. (2019). Data-driven smart production line and its common factors. International Journal of Advanced Manufacturing Technology, 103, 1211 – 1223.