IIoT Framework for SME level Injection Molding Industry in the Context of Industry 4.0

  • W.D. Madhuka Priyashan Lecturer, Department of Mechanical and Manufacturing Engineering, University of Ruhuna, SRI LANKA
  • Navod Neranjan Thilakarathne Lecturer, Department of ICT, Faculty of Technology, University of Colombo, SRI LANKA
Keywords: Industry 4.0, Internet of Things (IoT), Industrial IoT, IoT for SMEs


The Internet of Things (IoT) is a hype topic for nearly a decade now. Broadly growing, millions of devices get direct access to the Internet provides plenty of applications such as smart homes or mobile health management. This trend can also be found in the industry where IoT components hardened for these environments are introduced, called Industrial IoT (IIoT) devices which can be either sensors or actors, as well as mobile equipment such as smartphones, tablets, and smart glasses. Consequently, mobile communication becomes universal in smart factories. IIoT devices provide massive data on temperature, pressure, machine states, etc. But still, most of the SME level industries in the Asian region are new to these technological advancements. They still operate their facilities ith conventional setups without absorbing the new opportunities which are presented by IoT.

In the plastic injection molding industry, process parameters perform a significant role in the quality of the output product. During the manufacturing process, these process parameters have to deal with various factors such as quality and type of materials, requirement tolerance levels of the output product, Environmental conditions like temperature and humidity, etc. Injection molding has been a challenging process for many SME level manufacturers to produce products while meeting the quality requirements at the lowest cost. Most of them are unable to reach the global market in the injection molding industry due to the non-availability of the proper methods to determine the process parameters for injection molding. During production, quality characteristics may differ due to drifting or shifting of processing conditions caused by machine wear, environmental change, or operator fatigue. By determining the optimal process parameter settings productivity and quality will increase while reducing the cost of production.

In this paper, we suggest an Industrial IoT framework that can develop for small- and medium-sized enterprises (SMEs) level industries to optimize their production facility. With the presented framework SME level industries can start to inherit IoT devices into their production floor to manage and monitor production parameters in real-time while improving the quality of the production.


Download data is not yet available.


J. Qin, Y. Liu, & R. Grosvenor. (2016). A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP, 52, 173–178. DOI: 10.1016/j.procir.2016.08.005.

V. Roblek, M. Meško, & A. Krapež. (2016). A complex view of industry 4.0. SAGE Open, 6(2). DOI: 10.1177/2158244016653987.

S. Vaidya, P. Ambad, & S. Bhosle. (2018). Industry 4.0 - A glimpse. Procedia Manuf., 20, 233–238. DOI: 10.1016/j.promfg.2018.02.034.

A. Rojko. (2017). Industry 4.0 concept: Background and overview. Int. J. Interact. Mob. Technol., 11(5), 77–90. DOI: 10.3991/ijim.v11i5.7072.

J. Lee, H. A. Kao, & S. Yang. (2014). Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP, 16, 3–8. DOI: 10.1016/j.procir.2014.02.001.

K. Sipsas, K. Alexopoulos, V. Xanthakis, & G. Chryssolouris. (2016). Collaborative maintenance in flow-line manufacturing environments: An industry 4.0 approach. Procedia CIRP, 55, 236–241. DOI: 10.1016/j.procir.2016.09.013.

F. Othman, P. Partial, S. Mitigation, U. Interleaved, & B. Converter. (2016). Jurnal teknologi. DOI: 10.11113/jt.v78.9285.

F. Almada-lobo. (2015). The industry 4 . 0 revolution and the future of manufacturing execution systems (MES). Cyber-Physical Systems, 4, 16–21.

M. Weyrich, J. Schmidt, & C. Ebert. (2014). Machine-to-machine communication. IEEE Software, 31(4), 19–23.

K. Fysarakis, I. Askoxylakis, O. Soultatos, I. Papaefstathiou, C. Manifavas, & V. Katos. (2016). Which IoT protocol ?. In: IEEE Glob. Commun. Conf.. Available at: http://dx.doi.org/10.1109/GLOCOM.2016.7842383VN-readcube.com.

M. Pearce, B. Mutlu, J. Shah, & R. Radwin. (2018). Optimizing makespan and ergonomics in integrating collaborative robots into manufacturing processes. IEEE Trans. Autom. Sci. Eng., 15(4), 1772–1784. DOI: 10.1109/TASE.2018.2789820.

F. Wortmann & K. Flüchter. (2015). Internet of things: Technology and value added. Bus. Inf. Syst. Eng., 57(3), 221–224. DOI: 10.1007/s12599-015-0383-3.

M. H. Asghar, A. Negi, & N. Mohammadzadeh. (2015). Principle application and vision in Internet of Things (IoT). Int. Conf. Comput. Commun. Autom., pp. 427–431. DOI: 10.1109/CCAA.2015.7148413.

D. Giusto. (2010). A. lera, g. morabito, l. atzori (eds.) the internet of things. Springer.

S. Jeschke, C. Brecher, T. Meisen, D. Özdemir, & T. Eschert. (2017). Industrial internet of things and cyber manufacturing systems. DOI: 10.1007/978-3-319-42559-7_1.

L. Atzori, A. Iera, & G. Morabito. (2010). The internet of things: A survey. Comput. Networks, 54(15), 2787–2805. DOI: 10.1016/j.comnet.2010.05.010.

Kevin Asthon. (2010). That 'Internet of things’ thing. RFID J., 4986. Available at: http://www.rfidjournal.com/article/print/4986.

W. D. Fang, W. He, W. Chen, L. H. Shan, & F. Y. Ma. (2016). Research on the application-driven architecture in internet of things. Front. Artif. Intell. Appl., 293, 458–465. DOI: 10.3233/978-1-61499-722-1-458.

R. B. Cialdini. (2010). Scientific American, a division of nature America, Inc. Sci. Am., 248(2), 121–131.

H. Kopetz. (2011). Chapter 13 - Internet of things. Real-Time Syst. DOI: 10.1007/978-1-4419-8237-7.

Z. Bi, L. Da Xu, & C. Wang. (2014). Internet of things for enterprise systems of modern manufacturing. IEEE Trans. Ind. Informatics, 10(2), 1537–1546. DOI: 10.1109/TII.2014.2300338.

F. Khodadadi, R. Buyya, & A. V. Dastjerdi. (2017). Internet of things: An overview. arXiv, DOI: 10.9790/0661-180405117121.

L. Da Xu, W. He, & S. Li. (2014). Internet of things in industries: A survey. IEEE Trans. Ind. Informatics, 10(4), 2233–2243. DOI: 10.1109/TII.2014.2300753.

Injection Molding Process, Defects, Plastic. Available at: https://www.custompartnet.com/wu/InjectionMolding. Accessed on: Dec. 11, 2020.

Everything You Need To Know About Injection Molding. Available at: https://www.creativemechanisms.com/blog/everything-you-need-to-know-about-injection-molding. Accessed on: Dec. 12, 2020.

P. Postawa & T. Stachowiak. (2015). Mould temperature control during injection moulding proceb. AIP Conf. Proc., 1664. DOI: 10.1063/1.4918487.

J. M. Fischer. (2013). Causes of molded part variation. In: Handbook of Molded Part Shrinkage and Warpage, Elsevier, pp. 81–98.

Y. Li, Z. Sun, L. Han, & N. Mei. (2017). Fuzzy comprehensive evaluation method for energy management systems based on an internet of things. IEEE Access, 5(c), 21312–21322. DOI: 10.1109/ACCESS.2017.2728081.

M. Ben-Daya, E. Hassini, & Z. Bahroun. (2019). Internet of things and supply chain management: A literature review. Int. J. Prod. Res., 57(15–16), 4719–4742. DOI: 10.1080/00207543.2017.1402140.

H. Lee, K. Ryu, & Y. Cho. (2017). A framework of a smart injection molding system based on real-time data. Procedia Manuf., 11, 1004–1011. DOI: 10.1016/j.promfg.2017.07.206.

D. Behnke, M. Muller, P. B. Bok, & J. Bonnet. (2018). Intelligent network services enabling industrial IoT systems for flexible smart manufacturing. Int. Conf. Wirel. Mob. Comput. Netw. Commun. DOI: 10.1109/WiMOB.2018.8589088.

M. Charest, R. Finn, & R. Dubay. (2018). Integration of artificial intelligence in an injection molding process for on-line process parameter adjustment. 12th Annu. IEEE Int. Syst. Conf. SysCon 2018 - Proc., pp. 1–6. DOI: 10.1109/SYSCON.2018.8369500.

W. Chen, M. Nguyen, & P. Tai. (2018). An intelligent manufacturing system for injection molding. Proc. Eng. Technol. Innov., 8, pp. 9–14.

H. Lee, Y. Liau, & K. Ryu. (2018). Real-time parameter optimization based on neural network for smart injection molding. IOP Conference Series: Materials Science and Engineering, 324(1). DOI: 10.1088/1757-899X/324/1/012076.

Z. Song, Y. Sun, J. Wan, L. Huang, Y. Xu, & C. H. Hsu. (2019). Exploring robustness management of social internet of things for customization manufacturing. Futur. Gener. Comput. Syst., 92, 846–856. DOI: 10.1016/j.future.2017.10.030.

H. Lee. (2019). Effective dynamic control strategy of a key supplier with multiple downstream manufacturers using industrial internet of things and cloud system. Processes, 7(3). DOI: 10.3390/PR7030172.

Y. Zhang, D. Xi, H. Yang, F. Tao, & Z. Wang. (2019). Cloud manufacturing based service encapsulation and optimal configuration method for injection molding machine. J. Intell. Manuf., 30(7), 2681–2699. DOI: 10.1007/s10845-017-1322-6.

V. M. K. Werner, R. Krumpholz, C. Rehekampff, T. Scherzer, & M. Eblenkamp. (2019). Thermoplastic encapsulations of a sensor platform by high-temperature injection molding up to 360°C. Polym. Eng. Sci., 59(7), 1315–1331. DOI: 10.1002/pen.25114.

K. T. Park et al. (2020). Cyber physical energy system for saving energy of the dyeing process with industrial internet of things and manufacturing big data. Int. J. Precis. Eng. Manuf. - Green Technol., 7(1), 219–238. DOI: 10.1007/s40684-019-00084-7.

C. Beecks, F. Berns, & K. W. Schmidt. (2019). Ptolemaic indexing for managing and querying internet of things (IoT) data. In: Proc. - 2019 IEEE Int. Conf. Big Data, Big Data, pp. 4148–4151. DOI: 10.1109/BigData47090.2019.9005725.

G. R. et. al. Kanagachidambaresan. (2020). Internet of things for industry 4.0 design, challenges and solutions.

Eclipse Mosquitto. (2020). https://mosquitto.org/. Accessed on: Dec. 12, 2020.

Node-RED. Available at: https://nodered.org/. Accessed on: Dec. 12, 2020.

InfluxData Consent Manager. Available at: https://www.influxdata.com/. Accessed on: Dec. 12, 2020.

MAX6675 Datasheet, PDF - Alldatasheet. Available at: https://www.alldatasheet.com/view.jsp?Searchword=Max6675&gclid=Cj0KCQiAzsz-BRCCARIsANotFgOVD1KuHOHVKt8PUPAXLe7x2M50uHVikrnQqZqGS2z7rV40QbPBDiwaAgcdEALw_wcB. Accessed on: Dec. 12, 2020.

IoT HMI-Xiamen Haiwell Technology Co., Ltd. Available at: http://en.haiwell.com/hwproducts/184-en.html. Accessed on: Dec. 12, 2020.

T Series - Standard PLC-Xiamen Haiwell Technology Co., Ltd. Available at: http://en.haiwell.com/hwproducts/T_Series_PLC-en.html. Accessed on: Dec. 12, 2020

How to Cite
W.D. Madhuka Priyashan, & Navod Neranjan Thilakarathne. (2020). IIoT Framework for SME level Injection Molding Industry in the Context of Industry 4.0. International Journal of Engineering and Management Research, 10(6), 61-68. https://doi.org/10.31033/ijemr.10.6.9