Software components for smart industry based on microservices: a case study in pH control Process for the beverage industry

Modern industries require constant adaptation to new trends. Thus, they seek greater flexibility and agility to cope with disruptions, as well as to solve needs or meet the demand for growth. Therefore, smart industrial applications require a lot of flexibility to be able to react more quickly to co...

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Autores:
Serrano Magaña, Héctor
González, Apolinar
Ibarra-Junquera, Vrani
Balbastre, Patricia
Martínez Castro, Diego
Simó, José
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/13899
Acceso en línea:
https://hdl.handle.net/10614/13899
https://red.uao.edu.co/
Palabra clave:
Control automático
Automatización industrial
Sistemas de parámetros distribuidos
Automatic control
Distributed parameter systems
Edge computing
Software components
Distributed industrial automation systems
Industry 4.0
Industrial cyber physical systems
Rights
openAccess
License
Derechos reservados - MDPI, 2021
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dc.title.eng.fl_str_mv Software components for smart industry based on microservices: a case study in pH control Process for the beverage industry
title Software components for smart industry based on microservices: a case study in pH control Process for the beverage industry
spellingShingle Software components for smart industry based on microservices: a case study in pH control Process for the beverage industry
Control automático
Automatización industrial
Sistemas de parámetros distribuidos
Automatic control
Distributed parameter systems
Edge computing
Software components
Distributed industrial automation systems
Industry 4.0
Industrial cyber physical systems
title_short Software components for smart industry based on microservices: a case study in pH control Process for the beverage industry
title_full Software components for smart industry based on microservices: a case study in pH control Process for the beverage industry
title_fullStr Software components for smart industry based on microservices: a case study in pH control Process for the beverage industry
title_full_unstemmed Software components for smart industry based on microservices: a case study in pH control Process for the beverage industry
title_sort Software components for smart industry based on microservices: a case study in pH control Process for the beverage industry
dc.creator.fl_str_mv Serrano Magaña, Héctor
González, Apolinar
Ibarra-Junquera, Vrani
Balbastre, Patricia
Martínez Castro, Diego
Simó, José
dc.contributor.author.none.fl_str_mv Serrano Magaña, Héctor
González, Apolinar
Ibarra-Junquera, Vrani
Balbastre, Patricia
Martínez Castro, Diego
Simó, José
dc.subject.spa.fl_str_mv Control automático
Automatización industrial
Sistemas de parámetros distribuidos
topic Control automático
Automatización industrial
Sistemas de parámetros distribuidos
Automatic control
Distributed parameter systems
Edge computing
Software components
Distributed industrial automation systems
Industry 4.0
Industrial cyber physical systems
dc.subject.eng.fl_str_mv Automatic control
Distributed parameter systems
dc.subject.proposal.eng.fl_str_mv Edge computing
Software components
Distributed industrial automation systems
Industry 4.0
Industrial cyber physical systems
description Modern industries require constant adaptation to new trends. Thus, they seek greater flexibility and agility to cope with disruptions, as well as to solve needs or meet the demand for growth. Therefore, smart industrial applications require a lot of flexibility to be able to react more quickly to continuous market changes, offer more personalized products, increase operational efficiency, and achieve optimum operating points that integrate the entire value chain of a process. This requires the capture of new data that are subsequently processed at different levels of the hierarchy of automation processes, with requirements and technologies according to each level. The result is a new challenge related to the addition of new functionalities in the processes and the interoperability between them. This paper proposes a distributed computational component-based framework that integrates communication, computation, and storage resources and real-time capabilities through container technology, microservices, and the publish/subscribe paradigm, as well as contributing to the development and implementation of industrial automation applications by bridging the gap between generic architectures and physical realizations. The main idea is to enable plug-and-play software components, from predefined components with their interrelationships, to achieve industrial applications without losing or degrading the robustness from previous developments. This paper presents the process of design and implementation with the proposed framework through the implementation of a complex pH control process, ranging from the simulation part to its scaling and implementation to an industrial level, showing the plug-and-play assembly from a definition of components with their relationships to the implementation process with the respective technologies involved. The effectiveness of the proposed framework was experimentally verified in a real production process, showing that the results scaled to an industrial scale comply with the simulated design process. A qualitative comparison with traditional industrial implementations, based on the implementation requirements, was carried out. The implementation was developed in the beverage production plant “Punta Delicia”, located in Colima, Mexico. Finally, the results showed that the platform provided a high-fidelity design, analysis, and testing environment for cyber information flow and their effect on the physical operation of the pH control
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-03
dc.date.accessioned.none.fl_str_mv 2022-05-20T15:42:19Z
dc.date.available.none.fl_str_mv 2022-05-20T15:42:19Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 20799292
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/13899
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Repositorio Educativo Digital
dc.identifier.repourl.spa.fl_str_mv https://red.uao.edu.co/
identifier_str_mv 20799292
Universidad Autónoma de Occidente
Repositorio Educativo Digital
url https://hdl.handle.net/10614/13899
https://red.uao.edu.co/
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.citationendpage.spa.fl_str_mv 21
dc.relation.citationissue.spa.fl_str_mv 7
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 10
dc.relation.cites.eng.fl_str_mv Serrano-Magaña, H.; González-Potes, A.; Ibarra-Junquera, V.; Balbastre, P.; Martínez-Castro, D.; Simó, J. (2021). Software Components for Smart Industry Based on Microservices: A Case Study in pH Control Process for Beverages Industry. Electronics, Vol. 10 (7), pp. 1-21. https://www.mdpi.com/2079-9292/10/7/763
dc.relation.ispartofjournal.eng.fl_str_mv Electronics
dc.relation.references.none.fl_str_mv 1. Rajkumar, R.R.; Lee, I.; Sha, L.; Stankovic, J. Cyber-physical systems: The next computing revolution. In Proceedings of the 47th Design Automation Conference (DAC), ACM, Anaheim, CA, USA, 13–18 June 2010; pp. 731–736. [CrossRef]
2. Khan, W.Z.; Ahmed, E.; Hakak, S.; Yaqoob, I.; Ahmed, A. Edge computing: A survey. Future Gener. Comput. Syst. 2019, 97, 219–235. [CrossRef]
3. Jaskó, S.; Skrop, A.; Holczinger, T.; Chován, T.; Abonyi, J. Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard- and ontology-based methodologies and tools. Comput. Ind. 2020, 123. [CrossRef]
4. Belman-Lopez, C.E.; Jiménez-García, J.A.; Hernández-González, S. Análisis exhaustivo de los principios de diseño en el contexto de Industria 4.0. Rev. Iberoam. Autom. Inform. Ind. 2020, 17, 432–447. [CrossRef]
5. Hizam-Hanafiah, M.; Soomro, M.A. The Situation of Technology Companies in Industry 4.0 and the Open Innovation. J. Open Innov. Technol. Mark. Complex. 2021, 7, 34. [CrossRef]
6. Pahl, C.; Jamshidi, P.; Zimmermann, O. Microservices and Containers. In Software Engineering 2020; Felderer, M., Hasselbring, W., Rabiser, R., Jung, R., Eds.; Gesellschaft fur Informatik: Bonn, Germany, 2020. [CrossRef]
7. Januário, F.; Cardoso, A.; Gil, P. A Distributed Multi-Agent Framework for Resilience Enhancement in Cyber-Physical Systems. IEEE Access 2019, 7, 31342–31357. [CrossRef]
8. El Hariri, M.; Youssef, T.; Saleh, M.; Faddel, S.; Habib, H.; Mohammed, O.A. A Framework for Analyzing and Testing Cyber–Physical Interactions for Smart Grid Applications. Electronics 2019, 8, 1455. [CrossRef]
9. Ungurean, I.; Gaitan, N.C. A software architecture for the industrial internet of things—A conceptual model. Sensors 2020, 20, 5603. [CrossRef] [PubMed]
10. Coito, T.; Martins, M.S.; Viegas, J.L.; Firme, B.; Figueiredo, J.; Vieira, S.M.; Sousa, J.M. A Middleware Platform for Intelligent Automation: An Industrial Prototype Implementation. Comput. Ind. 2020, 123, 103329. [CrossRef]
11. Beregi, R.; Pedone, G.; Mezgár, I. A novel fluid architecture for cyber-physical production systems. Int. J. Comput. Integr. Manuf. 2019, 32, 340–351. [CrossRef]
12. Chen, G.; Wang, P.; Feng, B.; Li, Y.; Liu, D. The framework design of smart factory in discrete manufacturing industry based on cyber-physical system. Int. J. Comput. Integr. Manuf. 2020, 33, 79–101. [CrossRef]
13. Merdan, M.; Hoebert, T.; List, E.; Lepuschitz, W. Knowledge-based cyber-physical systems for assembly automation. Prod. Manuf. Res. 2019, 7, 223–254. [CrossRef]
14. Sanin, C.; Haoxi, Z.; Shafiq, I.; Waris, M.M.; Silva de Oliveira, C.; Szczerbicki, E. Experience based knowledge representation for Internet of Things and Cyber Physical Systems with case studies. Future Gener. Comput. Syst. 2019, 92, 604–616. [CrossRef]
15. Peres, R.S.; Dionisio Rocha, A.; Leitao, P.; Barata, J. IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0. Comput. Ind. 2018, 101, 138–146. [CrossRef]
16. Lass, S.; Gronau, N. A factory operating system for extending existing factories to Industry 4.0. Comput. Ind. 2020, 115, 103128. [CrossRef]
17. Boyes, H.; Hallaq, B.; Cunningham, J.;Watson, T. The industrial internet of things (IIoT): An analysis framework. Comput. Ind. 2018, 101, 1–12. [CrossRef]
18. Goldschmidt, T.; Hauck-Stattelmann, S.; Malakuti, S.; Grüner, S. Container-based architecture for flexible industrial control applications. J. Syst. Archit. 2018, 84, 28–36. [CrossRef]
19. Hofer, F.; Sehr, M.; Sangiovanni-Vincentelli, A.; Russo, B. Industrial Control via Application Containers: Maintaining determinism in IAAS. arxiv 2020, arXiv:2005.01890v1.
20. González-Nalda, P.; Etxeberria-Agiriano, I.; Calvo, I.; Otero, M.C. modular CPS architecture design based on ROS and Docker. Int. J. Interact. Des. Manuf. 2017, 11, 949–955. [CrossRef]
21. Wan, X.; Guan, X.; Wang, T.; Bai, G.; Choi, B.Y. Application deployment using Microservice and Docker containers: Framework and optimization. J. Netw. Comput. Appl. 2018, 119, 97–109. [CrossRef]
22. Abeni, L.; Balsini, A.; Cucinotta, T. Container-Based Real-Time Scheduling in the Linux Kernel. ACM SIGBED Rev. 2019, 16. [CrossRef]
23. Anjali, F.N.U.; Caraza-Harter, T.; Swift, M.M.Blending Containers and Virtual Machines: A Study of Firecracker and GVisor; Association for Computing Machinery: New York, NY, USA, 2020. [CrossRef]
24. Kozhirbayev, Z.; Sinnott, R.O. A performance comparison of container-based technologies for the Cloud. Future Gener. Comput. Syst. 2017, 68, 175–182. [CrossRef]
25. Aheleroff, S.; Xu, X.; Lu, Y.; Aristizabal, M.; Pablo Velásquez, J.; Joa, B.; Valencia, Y. IoT-enabled smart appliances under industry 4.0: A case study. Adv. Eng. Inf. 2020, 43, 101043. [CrossRef]
26. Chen, B.;Wan, J.; Shu, L.; Li, P.; Mukherjee, M.; Yin, B. Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges. IEEE Access 2017, 6, 6505–6519. [CrossRef] 27. Dai, W.; Wang, P.; Sun, W.; Wu, X.; Zhang, H.; Vyatkin, V.; Yang, G. Semantic Integration of Plug-and-Play Software Components for Industrial Edges Based on Microservices. IEEE Access 2019, 7, 125882–125892. [CrossRef] 28. Alam, M.; Rufino, J.; Ferreira, J.; Ahmed, S.H.; Shah, N.; Chen, Y. Orchestration of Microservices for IoT Using Docker and Edge Computing. IEEE Commun. Mag. 2018, 56, 118–123. [CrossRef] 29. Pontarolli, R.P.; Bigheti, J.A.; Fernandes, M.M.; Domingues, F.O.; Risso, S.L.; Godoy, E.P. Microservice Orchestration for Process Control in Industry 4.0. In Proceedings of the 2020 IEEE InternationalWorkshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2020—Proceedings, Roma, Italy, 3–5 June 2020; pp. 245–249. [CrossRef] 30. Benayache, A.; Bilami, A.; Barkat, S.; Lorenz, P.; Taleb, H. MsM: A microservice middleware for smart WSN-based IoT application. J. Netw. Comput. Appl. 2019, 144, 138–154. [CrossRef] 31. Krämer, M.; Frese, S.; Kuijper, A. Implementing secure applications in smart city clouds using microservices. Future Gener. Comput. Syst. 2019, 99, 308–320. [CrossRef] 32. Ren, H.L.; Jiao, Y.P. Study on the Distributed Real-Time and Embedded System Middleware Based on the DDS. In Advanced Materials Research; Materials Science and Information Technology; Trans Tech Publications Ltd.: Bach, Switzerland, 2012; Volume 433, pp. 7522–7525. 33. Amoretti, M.; Pecori, R.; Protskaya, Y.; Veltri, L.; Zanichelli, F. A Scalable and Secure Publish/Subscribe-based Framework for Industrial IoT. IEEE Trans. Ind. Inf. 2020, 17, 3815–3825. [CrossRef] 34. Calabretta, M.; Pecori, R.; Vecchio, M.; Veltri, L. MQTT-Auth: A Token-based Solution to Endow MQTT with Authentication and Authorization Capabilities. J. Commun. Softw. Syst. 2018, 14, 320–331. [CrossRef] 35. Ibarra-Junquera, V.; Jørgensen, S.; Virgen-Ortíz, J.; Escalante-Minakata, P.; Osuna-Castro, J. Following an optimal batch bioreactor operations model. Chem. Eng. Process. Process Intensif. 2012, 62, 114–128. [CrossRef] 36. González-Potes, A.; Mata-López, W.A.; Ibarra-Junquera, V.; Ochoa-Brust, A.M.; Martínez-Castro, D.; Crespo, A. Distributed multi-agent architecture for real-time wireless control networks of multiple plants. Eng. Appl. Artif. Intell. 2016, 56, 142–156. [CrossRef] 37. Kwan, C.; Lewis, F.; Yeung, K. Adaptive control of induction motors without flux measurements. Automatica 1996, 32, 903–908. [CrossRef] 38. Kwan, C.; Lewis, F.L. Robust backstepping control of nonlinear systems using neural networks. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2000, 30, 753–766. [CrossRef] 39. Polycarpou, M.; Zhang, X.; Xu, R.; Yang, Y.; Kwan, C. A neural network based approach to adaptive fault tolerant flight control. In Proceedings of the 2004 IEEE International Symposium on Intelligent Control, Taipei, Taiwan, 2–4 September 2004; pp. 61–66. [CrossRef] 40. Nejati, A.; Shahrokhi, M.; Mehrabani, A. Comparison between backstepping and input–output linearization techniques for pH process control. J. Process Control 2012, 22, 263–271. [CrossRef] 41. Wright, R.A.; Kravaris, C. On-line identification and nonlinear control of an industrial pH process. J. Process Control 2001, 11, 361–374. [CrossRef] 42. Ali, S.; Qaisar, S.; Saeed, H.; Khan, M.; Naeem, M.; Anpalagan, A. Network challenges for cyber physical systems with tiny wireless devices: A case study on reliable pipeline condition monitoring. Sensors 2015, 15, 7172–7205. [CrossRef] [PubMed] 43. Nguyen, T.; Chidambara, V.A.; Andreasen, S.Z.; Golabi, M.; Huynh, V.N.; Linh, Q.T.; Bang, D.D.; Wolff, A. Point-of-care devices for pathogen detections: The three most important factors to realise towards commercialization. TrAC Trends Anal. Chem. 2020, 131, 116004. [CrossRef] 44. Juneja, P.K.; Sunori, S.K.; Sharma, A.; Sharma, A.; Pathak, H.; Joshi, V.; Bhasin, P. A Review on Control System Applications in Industrial Processes. IOP Conf. Ser. Mater. Sci. Eng. 2021, 1022, 012010. [CrossRef] 45. Abdullah, N.H.S.; Karsiti, M.N.; Ibrahim, R. A review of pH neutralization process control. In Proceedings of the 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012), Kuala Lumpur, Malaysia, 12–14 June 2012; Volume 2, pp. 594–598. [CrossRef]
dc.rights.spa.fl_str_mv Derechos reservados - MDPI, 2021
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spelling Serrano Magaña, Héctor8c1f49bb3aa5c488fd555b24002d54d4González, Apolinarc71d2127efe309f1c72ed360a78af749Ibarra-Junquera, Vrani775c0fcd76dad13f53399833b2132d2aBalbastre, Patricia4d46ff6604243e9e804a15a62483dddfMartínez Castro, Diegovirtual::2993-1Simó, José9dcb5e8e16daf2ef5948a6d60fca775b2022-05-20T15:42:19Z2022-05-20T15:42:19Z2021-0320799292https://hdl.handle.net/10614/13899Universidad Autónoma de OccidenteRepositorio Educativo Digitalhttps://red.uao.edu.co/Modern industries require constant adaptation to new trends. Thus, they seek greater flexibility and agility to cope with disruptions, as well as to solve needs or meet the demand for growth. Therefore, smart industrial applications require a lot of flexibility to be able to react more quickly to continuous market changes, offer more personalized products, increase operational efficiency, and achieve optimum operating points that integrate the entire value chain of a process. This requires the capture of new data that are subsequently processed at different levels of the hierarchy of automation processes, with requirements and technologies according to each level. The result is a new challenge related to the addition of new functionalities in the processes and the interoperability between them. This paper proposes a distributed computational component-based framework that integrates communication, computation, and storage resources and real-time capabilities through container technology, microservices, and the publish/subscribe paradigm, as well as contributing to the development and implementation of industrial automation applications by bridging the gap between generic architectures and physical realizations. The main idea is to enable plug-and-play software components, from predefined components with their interrelationships, to achieve industrial applications without losing or degrading the robustness from previous developments. This paper presents the process of design and implementation with the proposed framework through the implementation of a complex pH control process, ranging from the simulation part to its scaling and implementation to an industrial level, showing the plug-and-play assembly from a definition of components with their relationships to the implementation process with the respective technologies involved. The effectiveness of the proposed framework was experimentally verified in a real production process, showing that the results scaled to an industrial scale comply with the simulated design process. A qualitative comparison with traditional industrial implementations, based on the implementation requirements, was carried out. The implementation was developed in the beverage production plant “Punta Delicia”, located in Colima, Mexico. Finally, the results showed that the platform provided a high-fidelity design, analysis, and testing environment for cyber information flow and their effect on the physical operation of the pH control21 páginasapplication/pdfengMDPIDerechos reservados - MDPI, 2021https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Control automáticoAutomatización industrialSistemas de parámetros distribuidosAutomatic controlDistributed parameter systemsEdge computingSoftware componentsDistributed industrial automation systemsIndustry 4.0Industrial cyber physical systemsSoftware components for smart industry based on microservices: a case study in pH control Process for the beverage industryArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85217110Serrano-Magaña, H.; González-Potes, A.; Ibarra-Junquera, V.; Balbastre, P.; Martínez-Castro, D.; Simó, J. (2021). Software Components for Smart Industry Based on Microservices: A Case Study in pH Control Process for Beverages Industry. Electronics, Vol. 10 (7), pp. 1-21. https://www.mdpi.com/2079-9292/10/7/763Electronics1. Rajkumar, R.R.; Lee, I.; Sha, L.; Stankovic, J. Cyber-physical systems: The next computing revolution. In Proceedings of the 47th Design Automation Conference (DAC), ACM, Anaheim, CA, USA, 13–18 June 2010; pp. 731–736. [CrossRef]2. Khan, W.Z.; Ahmed, E.; Hakak, S.; Yaqoob, I.; Ahmed, A. Edge computing: A survey. Future Gener. Comput. Syst. 2019, 97, 219–235. [CrossRef]3. Jaskó, S.; Skrop, A.; Holczinger, T.; Chován, T.; Abonyi, J. Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard- and ontology-based methodologies and tools. Comput. Ind. 2020, 123. [CrossRef]4. Belman-Lopez, C.E.; Jiménez-García, J.A.; Hernández-González, S. Análisis exhaustivo de los principios de diseño en el contexto de Industria 4.0. Rev. Iberoam. Autom. Inform. Ind. 2020, 17, 432–447. [CrossRef]5. Hizam-Hanafiah, M.; Soomro, M.A. The Situation of Technology Companies in Industry 4.0 and the Open Innovation. J. Open Innov. Technol. Mark. Complex. 2021, 7, 34. [CrossRef]6. Pahl, C.; Jamshidi, P.; Zimmermann, O. Microservices and Containers. In Software Engineering 2020; Felderer, M., Hasselbring, W., Rabiser, R., Jung, R., Eds.; Gesellschaft fur Informatik: Bonn, Germany, 2020. [CrossRef]7. Januário, F.; Cardoso, A.; Gil, P. A Distributed Multi-Agent Framework for Resilience Enhancement in Cyber-Physical Systems. IEEE Access 2019, 7, 31342–31357. [CrossRef]8. El Hariri, M.; Youssef, T.; Saleh, M.; Faddel, S.; Habib, H.; Mohammed, O.A. A Framework for Analyzing and Testing Cyber–Physical Interactions for Smart Grid Applications. Electronics 2019, 8, 1455. [CrossRef]9. Ungurean, I.; Gaitan, N.C. A software architecture for the industrial internet of things—A conceptual model. Sensors 2020, 20, 5603. [CrossRef] [PubMed]10. Coito, T.; Martins, M.S.; Viegas, J.L.; Firme, B.; Figueiredo, J.; Vieira, S.M.; Sousa, J.M. A Middleware Platform for Intelligent Automation: An Industrial Prototype Implementation. Comput. Ind. 2020, 123, 103329. [CrossRef]11. Beregi, R.; Pedone, G.; Mezgár, I. A novel fluid architecture for cyber-physical production systems. Int. J. Comput. Integr. Manuf. 2019, 32, 340–351. [CrossRef]12. Chen, G.; Wang, P.; Feng, B.; Li, Y.; Liu, D. The framework design of smart factory in discrete manufacturing industry based on cyber-physical system. Int. J. Comput. Integr. Manuf. 2020, 33, 79–101. [CrossRef]13. Merdan, M.; Hoebert, T.; List, E.; Lepuschitz, W. Knowledge-based cyber-physical systems for assembly automation. Prod. Manuf. Res. 2019, 7, 223–254. [CrossRef]14. Sanin, C.; Haoxi, Z.; Shafiq, I.; Waris, M.M.; Silva de Oliveira, C.; Szczerbicki, E. Experience based knowledge representation for Internet of Things and Cyber Physical Systems with case studies. 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[CrossRef]Comunidad generalPublication16469e35-6f18-4e0c-acfe-e8a2e314fedfvirtual::2993-116469e35-6f18-4e0c-acfe-e8a2e314fedfvirtual::2993-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000195928virtual::2993-1LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/adc8e6a7-7e8c-4d5d-891e-420ad677314a/download20b5ba22b1117f71589c7318baa2c560MD52ORIGINALSoftware components for smart industry based on microservices a case study in pH control process for the beverage industry.pdfSoftware components for smart industry based on microservices a case study in pH control process for the beverage industry.pdfTexto archivo completo del artículo de revista, PDFapplication/pdf51115021https://red.uao.edu.co/bitstreams/f1d06311-923a-4ad3-b65b-5243fb902391/download0072a617da802d9af769cb127e520a15MD53TEXTSoftware components for smart industry based on microservices a case study in pH control process for the beverage industry.pdf.txtSoftware components for smart industry based on microservices a case study in pH control process for the beverage industry.pdf.txtExtracted texttext/plain58494https://red.uao.edu.co/bitstreams/e7bd673f-8d89-4c60-852c-94a64252089a/download16bbf300d85d18e6c1d541968ae5e98bMD54THUMBNAILSoftware components for smart industry based on microservices a case study in pH control process for the beverage industry.pdf.jpgSoftware components for smart industry based on microservices a case study in pH control process for the beverage industry.pdf.jpgGenerated Thumbnailimage/jpeg15581https://red.uao.edu.co/bitstreams/2b2de1b0-c269-4fd3-ba19-d85b334e2e88/downloade3d2c55d9353190d71d4a0cbb9edb409MD5510614/13899oai:red.uao.edu.co:10614/138992024-03-07 16:43:07.114https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - MDPI, 2021open.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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