ABSTRACT
The digitalization of traditional industrial processes has profoundly influenced every step of the manufacturing value chain during the past two decades, having as its main goal to achieve zero-defected products. Moreover, since dairy production is at the heart of food industry, it is critical to leverage innovative technologies to increase their efficiency and continuously meet the demanding standards from the farm level to market and reduce the amount of waste. Towards this end, we propose a Dairy 4.0 architecture capable of utilising information to detect and prevent flaws to the final dairy products. The architecture layers are based on machine vision and the digital twins technologies, while it respects the zero defect manufacturing (ZDM) approach. The proposed frameworks is structured on a four layer architecture: (i) the physical layer, which consists of dairy farming, dairy production, and dairy storage and logistics, (ii) the acquisition layer that is responsible for collecting contextual information, (iii) the digital twin layer which uses data from the vision system and the physical system to anticipate future occurrences, and finally (iv) the ZDM layer, which functions as an orchestrator and binding agent for all the processed data.
- Muhammad Bilal Ahmed, Farhat Majeed, Cesar Sanin, and Edward Szczerbicki. 2020. Smart virtual product development (SVPD) system to support product inspection planning in industry 4.0. Procedia Computer Science 176 (2020), 2596–2604.Google ScholarCross Ref
- Victor Alonso, Angel Dacal-Nieto, Luís Barreto, António Amaral, and Eduardo Rivero. 2019. Industry 4.0 implications in machine vision metrology: an overview. Procedia Manufacturing 41 (2019), 359–366. 8th Manuf. Eng. Soc. Intern. Conf., MESIC 2019, 19-21 June 2019, Madrid, Spain.Google Scholar
- Vasiliki Balaska, Dimitris Folinas, Fotios K Konstantinidis, and Antonios Gasteratos. 2022. Smart counting of unboxed stocks in the Warehouse 4.0 ecosystem. In 2022 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 1–6.Google ScholarDigital Library
- Vasiliki Balaska, Kosmas Tsiakas, Dimitrios Giakoumis, Ioannis Kostavelis, Dimitrios Folinas, Antonios Gasteratos, and Dimitrios Tzovaras. 2022. A Viewpoint on the Challenges and Solutions for Driverless Last-Mile Delivery. Machines 10, 11 (2022), 1059.Google ScholarCross Ref
- Barbara Rita Barricelli, Elena Casiraghi, and Daniela Fogli. 2019. A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE access 7 (2019), 167653–167671.Google Scholar
- A Bhardwaj, RS Mor, S Singh, and M Dev. 2016. An investigation into the dynamics of supply chain practices in Dairy industry: a pilot study. In Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management, Detroit, Michigan, USA. 1360–1365.Google Scholar
- Mariana E. Cóccola, Natalia P. Basán, Carlos A. Méndez, and Rodolfo G. Dondo. 2022. Optimization of resource flows across the whole supply chain. Application to a case study in the dairy industry. Comput. Chem. Eng. 158 (2022), 107632.Google ScholarCross Ref
- Paul-Arthur Dreyfus, Foivos Psarommatis, Gokan May, and Dimitris Kiritsis. 2022. Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework. International Journal of Production Research 60, 2 (2022), 742–765.Google ScholarCross Ref
- Ralph Early. 1998. Technology of dairy products. Springer Science & Business Media.Google Scholar
- Ana Faria, Liliana Gonçalves, João M. Peixoto, Luciana Peixoto, António G. Brito, and Gilberto Martins. 2017. Resources recovery in the dairy industry: bioelectricity production using a continuous microbial fuel cell. Journal of Cleaner Production 140 (2017), 971–976.Google ScholarCross Ref
- Mohd Javaid, Abid Haleem, Ravi Pratap Singh, Shanay Rab, and Rajiv Suman. 2022. Exploring impact and features of machine vision for progressive industry 4.0 culture. Sensors International 3 (2022), 100132.Google ScholarCross Ref
- Byeongwoo Jeon, Joo-Sung Yoon, Jumyung Um, and Suk-Hwan Suh. 2020. The architecture development of Industry 4.0 compliant smart machine tool system (SMTS). J. Intell. Manuf. 31, 8 (2020), 1837–1859.Google ScholarDigital Library
- Yishuo Jiang, Xinlai Liu, Zicheng Wang, Ming Li, Ray Y. Zhong, and George Q. Huang. 2023. Blockchain-enabled digital twin collaboration platform for fit-out operations in modular integrated construction. Automation in Construction 148 (2023), 104747.Google ScholarCross Ref
- Tero Kaarlela, Sakari Pieskä, and Tomi Pitkäaho. 2020. Digital Twin and Virtual Reality for Safety Training. In 11th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2020, Mariehamn, Finland, September 23-25, 2020. IEEE, 115–120.Google Scholar
- Ron S. Kenett and Jacob Bortman. 2022. The digital twin in Industry 4.0: A wide-angle perspective. Qual. Reliab. Eng. Int. 38, 3 (2022), 1357–1366.Google ScholarCross Ref
- Fotios K Konstantinidis, Vasiliki Balaska, Symeon Symeonidis, Spyridon G Mouroutsos, and Antonios Gasteratos. 2022. AROWA: An autonomous robot framework for Warehouse 4.0 health and safety inspection operations. In 2022 30th Mediterranean Conference on Control and Automation (MED). IEEE, 494–499.Google ScholarCross Ref
- Fotios K Konstantinidis, Vasiliki Balaska, Symeon Symeonidis, Dimitrios Tsilis, Spyridon G Mouroutsos, Loukas Bampis, Athanasios Psomoulis, and Antonios Gasteratos. 2023. Automating dairy production lines with the yoghurt cups recognition and detection process in the Industry 4.0 era. Procedia Computer Science 217 (2023), 918–927.Google ScholarDigital Library
- Fotios K Konstantinidis, Ioannis Kansizoglou, Nicholas Santavas, Spyridon G Mouroutsos, and Antonios Gasteratos. 2020. Marma: A mobile augmented reality maintenance assistant for fast-track repair procedures in the context of industry 4.0. Machines 8, 4 (2020), 88.Google ScholarCross Ref
- Fotios K Konstantinidis, Ioannis Kansizoglou, Konstantinos A Tsintotas, Spyridon G Mouroutsos, and Antonios Gasteratos. 2021. The role of machine vision in industry 4.0: A textile manufacturing perspective. In 2021 IEEE Inter. Conf. on Imaging Systems and Techniques (IST). IEEE, 1–6.Google ScholarDigital Library
- Fotios K. Konstantinidis, Spyridon G. Mouroutsos, and Antonios Gasteratos. 2021. The Role of Machine Vision in Industry 4.0: an automotive manufacturing perspective. In IEEE Inter. Conference on Imaging Systems and Techniques, IST 2021, Kaohsiung, Taiwan, August 24-26, 2021. IEEE, 1–6.Google ScholarDigital Library
- Fotios K Konstantinidis, Nikolaos Myrillas, Spyridon G Mouroutsos, Dimitrios Koulouriotis, and Antonios Gasteratos. 2022. Assessment of industry 4.0 for modern manufacturing ecosystem: A systematic survey of surveys. Machines 10, 9 (2022), 746.Google ScholarCross Ref
- Werner Kritzinger, Matthias Karner, Georg Traar, Jan Henjes, and Wilfried Sihn. 2018. Digital Twin in manufacturing: A categorical literature review and classification. Ifac-PapersOnline 51, 11 (2018), 1016–1022.Google ScholarCross Ref
- Mengnan Liu, Shuiliang Fang, Huiyue Dong, and Cunzhi Xu. 2021. Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems 58 (2021), 346–361.Google ScholarCross Ref
- Mohit Malik, Vijay Kumar Gahlawat, Rahul S Mor, Vijay Dahiya, and Mukheshwar Yadav. 2022. Application of Optimization Techniques in the Dairy Supply Chain: A Systematic Review. Logistics 6, 4 (2022).Google Scholar
- Shailendra Mishra and Sunil Kumar Sharma. 2023. Advanced contribution of IoT in agricultural production for the development of smart livestock environments. Internet of Things 22 (2023), 100724.Google ScholarCross Ref
- Suresh Neethirajan and Bas Kemp. 2021. Digital Twins in Livestock Farming. Animals 11, 4 (2021).Google Scholar
- Foivos Psarommatis. 2021. A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM. Journal of Manufacturing Systems 59 (2021), 507–521.Google ScholarCross Ref
- Foivos Psarommatis, Francisco Fraile, and Farhad Ameri. 2023. Zero Defect Manufacturing ontology: A preliminary version based on standardized terms. Computers in Industry 145 (2023), 103832.Google ScholarDigital Library
- Foivos Psarommatis and Gokan May. 2022. A literature review and design methodology for digital twins in the era of zero defect manufacturing. International Journal of Production Research (2022), 1–21.Google Scholar
- Foivos Psarommatis and Gokan May. 2022. A standardized approach for measuring the performance and flexibility of digital twins. International Journal of Production Research (2022), 1–16.Google ScholarCross Ref
- Foivos Psarommatis, Gökan May, Paul-Arthur Dreyfus, and Dimitris Kiritsis. 2020. Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research. International journal of production research 58, 1 (2020), 1–17.Google ScholarCross Ref
- Foivos Psarommatis, Sylvain Prouvost, Gökan May, and Dimitris Kiritsis. 2020. Product quality improvement policies in industry 4.0: characteristics, enabling factors, barriers, and evolution toward zero defect manufacturing. Frontiers in Computer Science 2 (2020), 26.Google ScholarCross Ref
- Foivos Psarommatis, João Sousa, João Pedro Mendonça, and Dimitris Kiritsis. 2022. Zero-defect manufacturing the approach for higher manufacturing sustainability in the era of industry 4.0: a position paper. International Journal of Production Research 60, 1 (2022), 73–91.Google ScholarCross Ref
- Christos Pylianidis, Val Snow, Hiske Overweg, Sjoukje A. Osinga, John Kean, and Ioannis N. Athanasiadis. 2022. Simulation-assisted machine learning for operational digital twins. Environ. Model. Softw. 148 (2022), 105274.Google ScholarDigital Library
- Hannah Ritchie, Pablo Rosado, and Max Roser. 2017. Meat and dairy production. Our World in Data (2017).Google Scholar
- Alena Rozhkova and Julia Olentsova. 2020. Development of new technological solutions for the dairy industry. In E3S Web of Conferences, Vol. 161. EDP Sciences, 01086.Google Scholar
- Sufiyan Sajid, Abid Haleem, Shashi Bahl, Mohd Javaid, Tarun Goyal, and Manoj Mittal. 2021. Data science applications for predictive maintenance and materials science in context to Industry 4.0. Materials Today: Proceedings 45 (2021), 4898–4905.Google ScholarCross Ref
- Çağrı Sel and Bilge Bilgen. 2015. Quantitative models for supply chain management within dairy industry: a review and discussion. European Journal of Industrial Engineering 9, 5 (2015), 561–594.Google ScholarCross Ref
- Rohit Sharma, Sachin S. Kamble, Angappa Gunasekaran, Vikas Kumar, and Anil Kumar. 2020. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res. 119 (2020), 104926.Google ScholarCross Ref
- Ricardo Luhm Silva, Osiris Canciglieri Junior, and Marcelo Rudek. 2022. A road map for planning-deploying machine vision artifacts in the context of industry 4.0. Journal of Industrial and Production Engineering 39, 3 (2022), 167–180.Google ScholarCross Ref
- João Sousa, Artem A Nazarenko, Christian Grunewald, Foivos Psarommatis, Francisco Fraile, Olga Meyer, and João Sarraipa. 2022. Zero-defect manufacturing terminology standardization: Definition, improvement, and harmonization. Frontiers in Manufacturing Engineering 2 (2022).Google Scholar
- Fei Tao, Jiangfeng Cheng, Qinglin Qi, Meng Zhang, He Zhang, and Fangyuan Sui. 2018. Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology 94 (2018), 3563–3576.Google ScholarCross Ref
- Konstantinos K Tsintotas, Ioannis Kansizoglou, Fotios K Konstantinidis, Loukas Bampis, Spyridon G Mouroutsos, Georgios Ch. Syrakoulis, Foivos Psarromatis, Yiannis Aloimonos, and Antonios Gasteratos. 2023. Active vision: A promising technology for achieving zero-defect manufacturing. Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA ’23), July 5–7, 2023, Corfu, Greece.Google Scholar
- Cor Verdouw, Bedir Tekinerdogan, Adrie Beulens, and Sjaak Wolfert. 2021. Digital twins in smart farming. Agricultural Systems 189 (2021), 103046.Google ScholarCross Ref
- Jonas L. Vilas-Boas, Joel J.P.C. Rodrigues, and Antonio M. Alberti. 2023. Convergence of Distributed Ledger Technologies with Digital Twins, IoT, and AI for fresh food logistics: Challenges and opportunities. Journal of Industrial Information Integration 31 (2023), 100393.Google ScholarCross Ref
Index Terms
- Achieving Zero Defected Products in Diary 4.0 using Digital Twin and Machine Vision
Recommendations
Automating dairy production lines with the yoghurt cups recognition and detection process in the Industry 4.0 era
AbstractThe explosion of the digitisation of traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of ...
Enhancing and securing cyber‐physical systems and Industry 4.0 through digital twins: A critical review
AbstractDue to the fierce competitive global market, enterprises need to face and overcome new challenges and requirements to stay ahead of competition. Cyber‐physical systems, Internet of things, and digital twins are some of the contemporary ...
This study presents a critical review regarding the use of digital twins as a means to improve, reinforce and secure cyber‐physical systems and Industry 4.0. Therefore, it provides an overview of the related fields and technologies, goes over the results ...
Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues
HighlightsThis paper reviews the current status and advancement of Digital Twin-driven smart manufacturing, with highlights on the following aspects:
- Presented the connotation of ...
AbstractThis paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the ...
Comments