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

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
14--26
Opis fizyczny
Bibliogr. 40 poz., tab., wykr.
Twórcy
  • Bialystok University of Technology, Poland
  • Bialystok University of Technology, Poland
Bibliografia
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Nightingale, A. (2009). A guide to systematic literature reviews. Surgery (Oxford), 27(9), 381-384. doi: 10.1016/j.mpsur.2009.07.005
  • 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
  • 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
  • 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
  • Panda, S. K., Wisniewski, L., Ehrlich, M., Majumder, M., & Jasperneite, J. (2020). Plug & Play Retrofitting Approach for Data Integration to the Cloud. 2020 16th IEEE International Conference on Factory Communication Systems (WFCS), 1-8. doi: 10.1109/ WFCS47810.2020.9114523
  • 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
  • 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
  • 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
  • Sanghavi, D., Parikh, S., & Raj, S. A. (2019). Industry 4.0: Tools and Implementation. doi: 10.24425/ MPER.2019.129593
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Younkin, G., & Hesla, E. (2008). Origin of Numerical Control [History]. IEEE Industry Applications Magazine, 14(5), 10-12. doi: 10.1109/MIAS.2008.927525
  • 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
  • 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
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cffc1e9e-3c41-46ae-9da0-5e7da32481c1
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