Manufacturing equipment retrofitting towards Industry 4.0 standards — a systematic overview of the literature

Słowik, Maciej; Sierocka, Honorata

doi:10.2478/emj-2023-0017

Abstract

The main purpose of this paper is a systematic literature review on retrofitting tools, equipment, and infrastructure in the industrial domain. The methods used for the research were a systematic literature review: publication analysis, selection of databases, and appropriate modification of queries in individual databases. Findings were presented using a map of keywords, clusters, and charts. The main result of the conducted research was the identification of the main trends in the retrofitting area. The trends developed within the review can support further research into the direction of retrofitting methods and the factors determining the choice of specific techniques and tools in the digitalisation of manufacturing enterprises.

References

Al-Maeeni, S. S. H., Kuhnhen, C., Engel, B., & Schiller, M. (2020). Smart retrofitting of machine tools in the context of industry 4.0. Procedia CIRP, 88, 369-374. doi: 10.1016/j.procir.2020.05.064
Search in Google Scholar Back to article
Arjoni, D. H., Madani, F. S., Ikeda, G., Carvalho, G. de M., Cobianchi, L. B., Ferreira, L. F. L. R., & Villani, E. (2017). Manufacture Equipment Retrofit to Allow Usage in the Industry 4.0. 2017 2nd International Conference on Cybernetics, Robotics and Control (CRC), 155-161. doi: 10.1109/CRC.2017.46
Search in Google Scholar Back to article
Bergstrom, S. D., & Guenther, D. S. (2008). Retrofit of Power Centers Within an Airport. IEEE Transactions on Industry Applications, 44(6), 1918-1923. doi: 10.1109/TIA.2008.2006340
Search in Google Scholar Back to article
Burresi, G., Ermini, S., Bernabini, D., Lorusso, M., Gelli, F., Frustace, D., & Rizzo, A. (2020). Smart Retrofitting by Design Thinking Applied to an Industry 4.0 Migration Process in a Steel Mill Plant. 2020 9th Mediterranean Conference on Embedded Computing (MECO), 1-6. doi: 10.1109/MECO49872.2020.9134210
Search in Google Scholar Back to article
Camarena-Gil, E., Garrigues, C., & Puig, F. (2020). Innovating in the textile industry: An uncoordinated dance between firms and their territory? Journal of Entrepreneurship, Management and Innovation, 16(3), 47-76. doi: 10.7341/20201632
Search in Google Scholar Back to article
Carlo, F. D., Mazzuto, G., Bevilacqua, M., Ciarapica, F. E., Ortenzi, M., Donato, L. D., Ferraro, A., & Pirozzi, M. (2021). A process plant retrofitting framework in Industry 4.0 perspective. IFAC-PapersOnLine, 54(1), 67-72. doi: 10.1016/j.ifacol.2021.08.007
Search in Google Scholar Back to article
Corne, R., Nath, C., El Mansori, M., & Kurfess, T. (2017). Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling. Journal of Manufacturing Systems, 43, 287-295. doi: 10.1016/j.jmsy.2017.01.004
Search in Google Scholar Back to article
Fisch, C., & Block, J. (2018). Six tips for your (systematic) literature review in business and management research. Management Review Quarterly, 68(2), 103-106. doi: 10.1007/s11301-018-0142-x
Search in Google Scholar Back to article
Guerreiro, B. V., Lins, R. G., Sun, J., & Schmitt, R. (2018). Definition of Smart Retrofitting: First Steps for a Company to Deploy Aspects of Industry 4.0. In A. Hamrol, O. Ciszak, S. Legutko, & M. Jurczyk (Eds.), Advances in Manufacturing (pp. 161-170). Springer International Publishing. doi: 10.1007/978-3-319-68619-6_16Keshav Kolla, S. S. V., Lourenço, D. M., Kumar, A. A., & Plapper, P. (2022). Retrofitting of legacy machines in the context of Industrial Internet of Things (IIoT). Procedia Computer Science, 200, 62-70. doi: 10.1016/j.procs.2022.01.205
Search in Google Scholar Back to article
Gulewicz, M. (2022). Digital twin technology – Awareness, implementation problems and benefits. Engineering Management in Production and Services, 14(1), 63-77. doi: 10.2478/emj-2022-0006
Search in Google Scholar Back to article
Herwan, J., Kano, S., Ryabov, O., Sawada, H., Kasashima, N., & Misaka, T. (2019). Retrofitting old CNC turning with an accelerometer at a remote location towards Industry 4.0. Manufacturing Letters, 21, 56-59. doi: 10.1016/j.mfglet.2019.08.001
Search in Google Scholar Back to article
Hesser, D. F., & Markert, B. (2019). Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks. Manufacturing Letters, 19, 1-4. doi: 10.1016/j.mfglet.2018.11.001
Search in Google Scholar Back to article
Ilari, S., Carlo, F. D., Ciarapica, F. E., & Bevilacqua, M. (2021). Machine Tool Transition from Industry 3.0 to 4.0: A Comparison between Old Machine Retrofitting and the Purchase of New Machines from a Triple Bottom Line Perspective. Sustainability, 13(18), 10441. doi: 10.3390/su131810441
Search in Google Scholar Back to article
Kancharla, C. R., Bekaert, L., Lannoo, J., Vankeirsbilck, J., Vanoost, D., Boydens, J., & Hallez, H. (2021). Augmented Reality Based Machine Monitoring for Legacy Machines: A retrofitting use case. 2021 XXX International Scientific Conference Electronics (ET), 1-5. doi: 10.1109/ET52713.2021.9579936
Search in Google Scholar Back to article
Kang, J.-K., & Suh, S.-H. (1997). Machinability and set-up orientation for five-axis numerically controlled machining of free surfaces. The International Journal of Advanced Manufacturing Technology, 13(5), 311-325. doi: 10.1007/BF01178251
Search in Google Scholar Back to article
Keshav Kolla, S. S. V., Lourenço, D. M., Kumar, A. A., & Plapper, P. (2022). Retrofitting of legacy machines in the context of Industrial Internet of Things (IIoT). Procedia Computer Science, 200, 62-70. doi: 10.1016/j.procs.2022.01.205
Search in Google Scholar Back to article
Lima, F., Massote, A. A., & Maia, R. F. (2019). IoT Energy Retrofit and the Connection of Legacy Machines Inside the Industry 4.0 Concept. IECON 2019 – 45th Annual Conference of the IEEE Industrial Electronics Society, 5499-5504. doi: 10.1109/IECON.2019.8927799
Search in Google Scholar Back to article
Lins, T., Augusto Rabelo Oliveira, R., H. A. Correia, L., & Sa Silva, J. (2018). Industry 4.0 Retrofitting. 2018 VIII Brazilian Symposium on Computing Systems Engineering (SBESC), 8-15. doi: 10.1109/SBESC.2018.00011
Search in Google Scholar Back to article
Medina, B. E., & Manera, L. T. (2017). Retrofit of air conditioning systems through an Wireless Sensor and Actuator Network: An IoT-based application for smart buildings. 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), 49-53. doi: 10.1109/ICNSC.2017.8000066
Search in Google Scholar Back to article
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2020). Recycling and retrofitting for industrial equipment based on augmented reality. Procedia CIRP, 90, 606-610. doi: 10.1016/j.procir.2020.02.134
Search in Google Scholar Back to article
Niemeyer, C. L., Gehrke, I., Müller, K., Küsters, D., & Gries, T. (2020). Getting Small Medium Enterprises started on Industry 4.0 using retrofitting solutions. Procedia Manufacturing, 45, 208-214. doi: 10.1016/j.promfg.2020.04.096
Search in Google Scholar Back to article
Nightingale, A. (2009). A guide to systematic literature reviews. Surgery (Oxford), 27(9), 381-384. doi: 10.1016/j.mpsur.2009.07.005
Search in Google Scholar Back to article
Okoli, C. (2015). A Guide to Conducting a Standalone Systematic Literature Review. Communications of the Association for Information Systems, 37. doi: 10.17705/1CAIS.03743
Search in Google Scholar Back to article
Olsen, T. L., & Tomlin, B. (2020). Industry 4.0: Opportunities and Challenges for Operations Management. Manufacturing & Service Operations Management, 22(1), 113-122. doi: 10.1287/msom.2019.0796
Search in Google Scholar Back to article
Panda, S. K., Blome, A., Wisniewski, L., & Meyer, A. (2019). IoT Retrofitting Approach for the Food Industry. 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 1639-1642. doi: 10.1109/ETFA.2019.8869093
Search in Google Scholar Back to article
Panda, S. K., Wisniewski, L., Ehrlich, M., Majumder, M., & Jasperneite, J. (2020). Plug & Play Retrofitting Approach for Data Integration to the Cloud. 2020 16^th IEEE International Conference on Factory Communication Systems (WFCS), 1-8. doi: 10.1109/WFCS47810.2020.9114523
Search in Google Scholar Back to article
Pandiyan, V., Caesarendra, W., Tjahjowidodo, T., & Tan, H. H. (2018). In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. Journal of Manufacturing Processes, 31, 199-213. doi: 10.1016/j.jmapro.2017.11.014
Search in Google Scholar Back to article
Pisching, M. A., Pessoa, M. A. O., Junqueira, F., dos Santos Filho, D. J., & Miyagi, P. E. (2018). An architecture based on RAMI 4.0 to discover equipment to process operations required by products. Computers & Industrial Engineering, 125, 574-591. doi: 10.1016/j. cie.2017.12.029
Search in Google Scholar Back to article
Quatrano, A., De, S., Rivera, Z. B., & Guida, D. (2017). Development and implementation of a control system for a retrofitted CNC machine by using Arduino. FME Transaction, 45(4), 565-571. doi: 10.5937/fmet1704565Q
Search in Google Scholar Back to article
Sanghavi, D., Parikh, S., & Raj, S. A. (2019). Industry 4.0: Tools and Implementation. doi: 10.24425/MPER.2019.129593
Search in Google Scholar Back to article
Sridevi, S., Dhanasekar, J., & Manikandan, G. (2015). A methodology of retrofitting for CNC vertical milling machine. 2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE), 1-4. doi: 10.1109/RACE.2015.7097257
Search in Google Scholar Back to article
Stock, T., & Seliger, G. (2016). Opportunities of Sustainable Manufacturing in Industry 4.0. Procedia CIRP, 40, 536-541. doi: 10.1016/j.procir.2016.01.129
Search in Google Scholar Back to article
Szpilko, D., & Ejdys, J. (2022). European Green Deal – research directions. a systematic literature review. Ekonomia i Środowisko - Economics and Environment, 81(2), 8-38. doi: 10.34659/eis.2022.81.2.455
Search in Google Scholar Back to article
Tantscher, D., & Mayer, B. (2022). Digital Retrofitting of legacy machines: A holistic procedure model for industrial companies. CIRP Journal of Manufacturing Science and Technology, 36, 35-44. doi: 10.1016/j.cirpj.2021.10.011
Search in Google Scholar Back to article
Torres-Carrión, P. V., González-González, C. S., Aciar, S., & Rodríguez-Morales, G. (2018). Methodology for systematic literature review applied to engineering and education. 2018 IEEE Global Engineering Education Conference (EDUCON), 1364-1373. doi: 10.1109/EDUCON.2018.8363388
Search in Google Scholar Back to article
Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests. Journal of Manufacturing Science and Engineering, 139(7). doi: 10.1115/1.4036350
Search in Google Scholar Back to article
Xiao, Y., & Watson, M. (2019). Guidance on Conducting a Systematic Literature Review. Journal of Planning Education and Research, 39(1), 93-112. doi: 10.1177/0739456X17723971
Search in Google Scholar Back to article
Younkin, G., & Hesla, E. (2008). Origin of Numerical Control [History]. IEEE Industry Applications Magazine, 14(5), 10-12. doi: 10.1109/MIAS.2008.927525
Search in Google Scholar Back to article
Zambetti, M., Khan, M. A., Pinto, R., & Wuest, T. (2020). Enabling servitization by retrofitting legacy equipment for Industry 4.0 applications: Benefits and barriers for OEMs. Procedia Manufacturing, 48, 1047-1053. doi: 10.1016/j.promfg.2020.05.144
Search in Google Scholar Back to article
Xie, H., Shi, W., Choudhary, H., Fu, H., & Guo, X. (2019). Big Data Analysis for Retrofit Projects in Smart Cities. 2019 3rd International Conference on Smart Grid and Smart Cities (ICSGSC), 1-5. doi: 10.1109/ICSGSC.2019.00-28
Search in Google Scholar Back to article

Manufacturing equipment retrofitting towards Industry 4.0 standards — a systematic overview of the literature

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