Improving predictive maintenance benefits from online monitoring of spindles: case study in woodworking machine tool

Autores

  • Thyago Bachim UNIVERSIDADE NOVE DE JULHO - UNINOVE https://orcid.org/0000-0002-8982-545X
  • Mauro Luiz Martens Uninove University, Industrial Engineering Post Graduation Program, São Paulo, São Paulo, Brazil https://orcid.org/0000-0003-1242-8795
  • Salvatore Digiesi Polytechnic University of Bari, Department of Mechanics, Mathematics and Management, Bari, Italy
  • Douglas Favero Trindade SKF do Brasil Ltda – Spindle Service - Cajamar - SP
  • Bruno Ricci SKF do Brasil Ltda – Spindle Service - Cajamar – SP

DOI:

https://doi.org/10.20397/2177-6652/2020.v20i4.2009

Palavras-chave:

Spindle, Maintenance, Industry 4.0, Online Monitoring, Internet of Things, IoT

Resumo

Objective: This article aims to present a case study of the application of an online monitoring system for spindles in a furniture industry, analyzing the benefits to predictive maintenance and business.

 

Methodology/approach: A literature review was carried out followed by a case study

 

Originality/Relevance: Focusing the development of maintenance techniques, especially predictive maintenance and those supported by enabling technologies from Industry 4.0, such as Internet of Things (IoT), it may be possible to carry out online monitoring of spindles with a focus on reducing catastrophic or unplanned events.

 

Main results: The main results are to know the normal behavior of the machine, the possibility of obtaining information in real time, managerial data for sight management, and the possibility of identifying spindle failure before it becomes a catastrophic failure, thus reducing the costs of maintaining the spindles.

 

Theoretical contributions: As a contribution, we discuss the development of the system to digitize the data through the operation available in an outsourced cloud environment. This data can then be returned to the company in the form of dashboards for cash management, developing agility in decision making to facilitate the predictive maintenance in addition to validating the online monitoring system for spindle management in furniture industry processes.

Biografia do Autor

Thyago Bachim, UNIVERSIDADE NOVE DE JULHO - UNINOVE

University Nove de Julho, Industrial Engineering Post Graduation Program, São Paulo, São Paulo, Brazil

 

Mauro Luiz Martens, Uninove University, Industrial Engineering Post Graduation Program, São Paulo, São Paulo, Brazil

Mauro Luiz Martens is a Full Professor and Director of the Industrial Engineering Post Graduation Department at University Nove de Julho (UNINOVE), , in Brazil.  He holds a PhD in Production Engineering from the Polytechnic School of the University of São Paulo (USP), Brazil. He was a Visiting Scholar at Bentley University in 2014. He is a CNPq researcher looking at the relations between project management, sustainability and Industry 4.0.

Referências

Abele, E., Altintas, Y., & Brecher, C. (2010). Machine tool spindle units. CIRP Annals - Manufacturing Technology, 59(2), 781-802. doi:10.1016/j.cirp.2010.05.002.

Alcantara, D. P., & Martens, M. L. (2019). Technology Roadmapping (TRM): a systematic review of the literature focusing on models. Technological Forecasting and Social Change, 138, 127-138. doi:10.1016/j.techfore.2018.08.014.

Bardin, L. (1977). Análise de conteúdo. Lisboa: edições, 70, 225.

Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: a literature review. International Journal of Production Research, 57(15-16), 4719-4742. doi:10.1080/00207543.2017.1402140.

Canizares, E., & Valero, F. A. (2018). Analyzing the Effects of Applying IoT to a Metal-Mechanical Company (Article). Journal of Industrial Engineering and Management-Jiem, 11(2), 308-317. doi:10.3926/jiem.2526.

Cao, H., Zhang, X., & Chen, X. (2017). The concept and progress of intelligent spindles: A review. International Journal of Machine Tools and Manufacture, 112, 21-52. doi:10.1016/j.ijmachtools.2016.10.005.

Chui, M., Löffler, M., & Roberts, R. (2010). The internet of things. McKinsey Quarterly, 2(2010), 1-9.

Civerchia, F., Bocchino, S., Salvadori, C., Rossi, E., Maggiani, L., & Petracca, M. (2017). Industrial Internet of Things monitoring solution for advanced predictive maintenance applications. Journal of Industrial Information Integration, 7, 4-12. doi:10.1016/j.jii.2017.02.003.

Gopalakrishnan, M., Skoogh, A., Salonen, A., & Asp, M. (2019). Machine criticality assessment for productivity improvement: Smart maintenance decision support. International Journal of Productivity and Performance Management, 68(5), 858-878. doi:10.1108/IJPPM-03-2018-0091.

Holub, O., & Hammer, M. (2017). Diagnostics and maintenance of machine tool spindles-new views. MM Science Journal, 2017(December), 2094-2099. doi:10.17973/MMSJ.2017_12_201793.

Janak, L., Stetina, J., Fiala, Z., & Hadas, Z. (2016). Quantities and sensors for machine tool spindle condition monitoring. MM Science Journal, 2016(DECEMBER), 1648-1653. doi:10.17973/MMSJ.2016_12_2016204.

Jeon, B., Yoon, J. S., Um, J., & Suh, S. H. (2020). The architecture development of Industry 4.0 compliant smart machine tool system (SMTS). Journal of Intelligent Manufacturing. doi:10.1007/s10845-020-01539-4.

Lee, G. Y., Kim, M., Quan, Y. J., Kim, M. S., Kim, T. J. Y., Yoon, H. S., et al. (2018). Machine health management in smart factory: A review. Journal of Mechanical Science and Technology, 32(3), 987-1009. doi:10.1007/s12206-018-0201-1.

Lee, J., Kao, H. A., & Yang, S. 16 (2014a) Service innovation and smart analytics for Industry 4.0 and big data environment C3 - Procedia CIRP' 6th CIRP Conference on Industrial Product Service Systems, IPSS 2014. Windsor, ON: Elsevier, pp. 3-8. Available at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905408361&doi=10.1016%2fj.procir.2014.02.001&partnerID=40&md5=0843fd1f4a451db2c0975802ac8bc258.

Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., & Liao, H. (2006). Intelligent prognostics tools and e-maintenance. Computers in Industry, 57(6), 476-489. doi:10.1016/j.compind.2006.02.014.

Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014b). Prognostics and health management design for rotary machinery systems - Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1-2), 314-334. doi:10.1016/j.ymssp.2013.06.004.

Li, Z., Wang, Y., & Wang, K. S. (2017). Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Advances in Manufacturing, 5(4), 377-387. doi:10.1007/s40436-017-0203-8.

Liao, Y., Loures, E. D. F. R., & Deschamps, F. (2018). Industrial Internet of Things: A Systematic Literature Review and Insights. IEEE Internet of Things Journal, 5(6), 4515-4525. doi:10.1109/JIOT.2018.2834151.

Liu, C., Vengayil, H., Zhong, R. Y., & Xu, X. (2018). A systematic development method for cyber-physical machine tools. Journal of Manufacturing Systems, 48, 13-24. doi:10.1016/j.jmsy.2018.02.001.

Liu, C., & Xu, X. 63 (2017) Cyber-physical Machine Tool - The Era of Machine Tool 4.0 C3 - Procedia CIRP' Y. Wang, M. M. Tseng, & H. Y. Tsai 50th CIRP Conference on Manufacturing Systems, CIRP CMS 2017. Elsevier B.V., pp. 70-75. Available at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028665818&doi=10.1016%2fj.procir.2017.03.078&partnerID=40&md5=ceedec617435f521b2497626614e5ce7.

Mosyurchak, A., Veselkov, V., Turygin, A., & Hammer, M. (2017). Prognosis of behaviour of machine tool spindles, their diagnostics and maintenance. MM Science Journal, 2017(December), 2100-2104. doi:10.17973/MMSJ.2017_12_201794.

Mourtzis, D., Milas, N., & Athinaios, N. 78 (2018) Towards Machine Shop 4.0: A General Machine Model for CNC machine-tools through OPC-UA C3 - Procedia CIRP' A. Simeone, & P. C. Priarone 6th CIRP Global Web Conference, CIRPe 2018. Elsevier B.V., pp. 301-306. Available at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059906664&doi=10.1016%2fj.procir.2018.09.045&partnerID=40&md5=e6574727e6ea35aaea7947c679ea6a19.

Nagy, J., Olah, J., Erdei, E., Mate, D., & Popp, J. (2018). The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain-The Case of Hungary (Article). Sustainability, 10(10). doi:10.3390/su10103491.

Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127-182. doi:10.1007/s10845-018-1433-8.

Rastegari, A., Archenti, A., & Mobin, M. (2017) Condition based maintenance of machine tools: Vibration monitoring of spindle units C3 - Proceedings - Annual Reliability and Maintainability Symposium 2017 Annual Reliability and Maintainability Symposium, RAMS 2017. Institute of Electrical and Electronics Engineers Inc. Available at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018572925&doi=10.1109%2fRAM.2017.7889683&partnerID=40&md5=b9d53a769f56f23705c28b2b97f2ed37.

Roy, R., Stark, R., Tracht, K., Takata, S., & Mori, M. (2016). Continuous maintenance and the future – Foundations and technological challenges. CIRP Annals - Manufacturing Technology, 65(2), 667-688. doi:10.1016/j.cirp.2016.06.006.

Sadasivam, L., Archenti, A., & Sandberg, U. (2018). Machine tool ability representation: A review. Journal of Machine Engineering, 18(2), 5-16. doi:10.5604/01.3001.0012.0919.

Xu, X. (2017). Machine Tool 4.0 for the new era of manufacturing. International Journal of Advanced Manufacturing Technology, 92(5-8), 1893-1900. doi:10.1007/s00170-017-0300-7.

Yin, R.K. (2014). Case Study Research: Design and Methods, 5ª ed. Sage Publications Inc, California.

Ziada, Y., Yang, J., & DeGroat-Ives, D. (2017). Predicted Machining Dynamics for Powertrain Machining (Article). Sae International Journal of Passenger Cars-Mechanical Systems, 10(2), 534-540. doi:10.4271/2017-01-1330.

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Publicado

2020-11-24

Como Citar

Bachim, T., Martens, M. L., Digiesi, S., Trindade, D. F., & Ricci, B. (2020). Improving predictive maintenance benefits from online monitoring of spindles: case study in woodworking machine tool. Revista Gestão & Tecnologia, 20(4), 7–34. https://doi.org/10.20397/2177-6652/2020.v20i4.2009

Edição

v. 20 n. 4 (2020): Outubro/Dezembro

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ARTIGO

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