Design of Manufacturing Systems Based on Digital Shadow and Robust Engineering
Source abstract
Abstract
In the era of digital transformation, industry is facing multiple challenges due to the need for implementation of the Industry 4.0 standards, as well as the volatility of customer demands. The latter has created the need for the design and operation of more complex manufacturing systems and networks. A case study derived from Process Industries (PIs) is adopted in this research work in order to design a framework for flexible design of production lines, automation of quality control points, and improvement of the performance of the manufacturing system. Therefore, a Digital Shadow of a production line is developed to collect, analyze and identify potential issues (bottlenecks). An edge computing system for reliable and low-latency communications is also implemented. The digital model is validated using statistical Design Of Experiments (DOE) and ANalysis Of VAriance (ANOVA). For the assessment of what-if scenarios, the Digital Shadow model will be used in order to evaluate and find the desired solution. Ultimately, the goal of this research work is to improve the design and performance of the industry’s production section, as well as to increase the production rate and the product mix.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Flexible and Reconfigurable Manufacturing Systems · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Rio Patras, Greece
References (74)
Show source-provided references
1. Manufacturing systems complexity analysis methods review10.1080/0951192X.2015.1130245 ↗
2. The future of manufacturing industry: A strategic roadmap toward Industry 4.010.1108/JMTM-02-2018-0057 ↗
3. Mourtzis, D. (2022). Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology, Elsevier.
4. Reconfigurable manufacturing systems: Literature review and research trend10.1016/j.jmsy.2018.09.005 ↗
5. Simulation in the design and operation of manufacturing systems: State of the art and new trends10.1080/00207543.2019.1636321 ↗
6. Industry 4.0: A way from mass customization to mass personalization production10.1007/s40436-017-0204-7 ↗
7. Personalized PSS Design Optimization Based on Digital Twin and Extended Reality10.1016/j.procir.2022.05.267 ↗
8. A customization-oriented framework for design of sustainable product/service system10.1016/j.jclepro.2016.09.111 ↗
9. Whitepaper (2023, January 30). Skill Development for Industry 4.0. Available online: https://www.globalskillsummit.com/whitepaper-summary.pdf.
10. The World Bank (2023). Small and Medium Enterprises (SMEs) Finance, The World Bank.
11. Benitez, B.G., Ayala, F.N., and Frank, G.A. (2022). The Digital Supply Chain, Elsevier.
12. Ericson, Å., Lugnet, J., Solvang, W.D., Kaartinen, H., and Wenngren, J. (2020, January 10–12). Challenges of Industry 4.0 in SME businesses. Proceedings of the 2020 3rd International Symposium on Small-Scale Intelligent Manufacturing Systems (SIMS), IEEE, Gjovik, Norway.10.1109/SIMS49386.2020.9121542 ↗
13. Kusumawardhany, P.A., Baihaqi, I., and Karningsih, P.D. (2022, January 19–22). Frugal Innovation in SMEs: Challenges and Opportunities of Doing More with Less Strategy. Proceedings of the IEEE Technology & Engineering Management Conference-Asia Pacific (TEMSCON-ASPAC), Bangkok, Thailand.10.1109/TEMSCON-ASPAC52831.2022.9916558 ↗
14. Industry 5.0 and Society 5.0—Comparison, complementation and co-evolution10.1016/j.jmsy.2022.07.010 ↗
15. Mourtzis, D., Angelopoulos, J., and Panopoulos, N. (2022). A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies, 15.10.3390/en15176276 ↗
16. Industry 5.0: Prospect and retrospect10.1016/j.jmsy.2022.09.017 ↗
17. Roche, R. (2023, April 19). The Nine Pillars of Industry 4.0-Transforming Industrial Production. Available online: https://circuitdigest.com/article/what-is-industry-4-and-its-nine-technology-pillars.
18. Saptaningtyas, W.W.E., and Rahayu, D.K. (2020, January 10–14). A Proposed Model for Food Manufacturing in SMEs: Facing Industry 5.0. Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, Detroit, MI, USA.
19. Push or Pull? The nature of innovation process in the Hungarian food SMEs10.1016/j.jik.2018.03.007 ↗
20. European Commission (2018). The Impact of Private R&D on the Performance of Food-Processing Firms, Joint Research Centre. JRC Technical Reports.
21. Statista, Retail & Trade, Food & Beverage (2023, January 30). Research and Development Expenditure in Food, Beverages and Tobacco Product Businesses in the United Kingdom (UK) from 2002 to 2020. Available online: https://www.statista.com/statistics.
22. (2023, January 30). World Data Bank, Food, Beverages and Tobacco (% of Value Added in Manufacturing)—United Kingdom, Japan, Germany, Hungary, Italy. Available online: https://data.worldbank.org.
23. Nikolaidis, A. (2023, January 30). Greece and the Industry 4.0 Intelligent Automations, Sector of Industry, Development, Networks & Regional Policy, SEV. Available online: https://en.sev.org.gr.
24. Stavropoulos, P., and Mourtzis, D. (2022). Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology, Elsevier.
25. Segovia, M., and Garcia-Alfaro, J. (2022). Design, modeling and implementation of digital twins. Sensors, 22.10.3390/s22145396 ↗
26. Singh, M., Fuenmayor, E., Hinchy, E.P., Qiao, Y., Murray, N., and Devine, D. (2021). Digital twin: Origin to future. Appl. Syst. Innov., 4.10.3390/asi4020036 ↗
27. The digital shadow as enabler for data analytics in product life cycle management10.1016/j.procir.2019.01.083 ↗
28. Digital Twin in manufacturing: A categorical literature review and classification10.1016/j.ifacol.2018.08.474 ↗
29. The Digital Shadow of production–A concept for the effective and efficient information supply in dynamic industrial environments10.1016/j.procir.2018.03.188 ↗
30. Towards Digital Shadows for production planning and control in injection molding10.1016/j.cirpj.2022.05.003 ↗
31. Schuh, G., Anderl, R., Gausemeier, J., Hompel, M., and Wahlster, W. (2017). Industrie 4.0 Maturity Index: Managing the Digital Transformation of Companies, Herbert Utz Verlag GmbH.
32. Edge computing: A survey10.1016/j.future.2019.02.050 ↗
33. The role of edge computing in internet of things10.1109/MCOM.2018.1700906 ↗
34. Software survey: VOSviewer, A computer program for bibliometric mapping10.1007/s11192-009-0146-3 ↗
35. An ecosystem for Digital Shadows in manufacturing10.1016/j.procir.2021.11.140 ↗
36. The Digital Shadow: Developing a universal model for the automated optimization of cyber-physical production systems based on real-time data10.1016/j.procir.2020.03.069 ↗
37. The Digital Shadow of services: A reference model for comprehensive data collection in MRO services of machine manufacturers10.1016/j.procir.2018.03.318 ↗
38. A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective10.1016/j.jmsy.2020.07.018 ↗
39. A Digital Shadow cloud-based application to enhance quality control in manufacturing10.1016/j.ifacol.2020.12.2809 ↗
40. Physics-based Digital Twins for autonomous thermal food processing: Efficient, non-intrusive reduced-order modeling10.1016/j.ifset.2022.103143 ↗
41. Digital Twins in greenhouse horticulture: A review10.1016/j.compag.2022.107183 ↗
42. Digital Twins in Agriculture: A State-of-the-art review10.1016/j.atech.2022.100094 ↗
43. Machine Learning-Based Digital Twin for Monitoring Fruit Quality Evolution10.1016/j.procs.2022.01.200 ↗
44. Brecher, C., Buchsbaum, M., and Storms, S. (2019, January 20–24). Control from the cloud: Edge computing, services and digital shadow for automation technologies. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.10.1109/ICRA.2019.8793488 ↗
45. Digital twin-aided intelligent offloading with edge selection in mobile edge computing10.1109/LWC.2022.3146207 ↗
46. An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario10.1016/j.adhoc.2019.102047 ↗
47. Design and Development of an Edge-Computing Platform Towards 5G Technology Adoption for Improving Equipment Predictive Maintenance10.1016/j.procs.2022.01.259 ↗
48. Mourtzis, D., Angelopoulos, J., and Panopoulos, N. (2021). Robust engineering for the design of resilient manufacturing systems. Appl. Sci., 11.10.3390/app11073067 ↗
49. An energy consumption approach in a manufacturing process using design of experiments10.1080/0951192X.2018.1493234 ↗
50. Assessing the mechanical performance of different cable bolts based on design of experiments techniques and analysis of variance10.1016/j.ijrmms.2020.104307 ↗
51. A design of experiments Cyber–Physical System for energy modelling and optimisation in end-milling machining10.1016/j.rcim.2022.102469 ↗
52. Evolution and future of manufacturing systems10.1016/j.cirp.2021.05.008 ↗
53. A Simulation Study for Investigation of Routing Flexibility on Performance in Flexible Manufacturing System Environment
54. Professor Pan’s Research Group (2023, January 30). Modeling and Simulation of Food Processing Technologies. Available online: https://research.engineering.ucdavis.edu.
55. Bosman, A. (2023, January 30). Modelling and Simulation of Food Processes. Campden BRI. Available online: https://www.campdenbri.co.uk.
56. Design and control of food job-shop processing systems: A simulation analysis in the catering industry10.1108/IJLM-11-2015-0204 ↗
57. Predictive maintenance key control parameters for achieving efficient Zero Defect Manufacturing10.1016/j.procir.2021.11.014 ↗
58. Modelling and analysing the water jet machining parameters of aluminium nano composite by ANOVA and Taguchi10.1016/j.matpr.2021.04.570 ↗
59. Kumar, D., Murthy, K., Kore, S.D., and Nandy, A. (2023). Effect of thread angle besides other process parameters in electromagnetically crimped threaded surfaced tube-to-tube joint: FEM modelling and ANOVA analysis. Mater. Today Proc.10.1016/j.matpr.2023.02.074 ↗
60. Optimize the Turning Parameter Using Taguchi Methodology10.32628/IJSRST218553 ↗
61. Pagone, E., Haddad, Y., Barsotti, L., Dini, G., and Salonitis, K. (2023). A stochastic evaluation framework to improve the robustness of manufacturing systems. Int. J. Comput. Integr. Manuf., 1–19.10.1080/0951192X.2022.2162592 ↗
62. A joint production and carbon trading policy for unreliable manufacturing systems under cap-and-trade regulation10.1016/j.jclepro.2021.125973 ↗
63. Udroiu, R., Braga, I.C., and Nedelcu, A. (2019). Evaluating the quality surface performance of additive manufacturing systems: Methodology and a material jetting case study. Materials, 12.10.3390/ma12060995 ↗
64. 7 Robust design: Experiments for improving quality10.1016/S0169-7161(96)13009-1 ↗
65. Tomy, L., Chesneau, C., and Madhav, A.K. (2021). Statistical Techniques for Environmental Sciences: A Review. Math. Comput. Appl., 26.10.3390/mca26040074 ↗
66. Taguchi, G. (1978, January 20–24). Off-Line and On-Line Quality Control Systems. Proceedings of the International Conference on Quality Control, Tokyo, Japan.
67. Taguchi, G., and Shih-Chung, T. (1992). Introduction to Quality Engineering: Bringing Quality Engineering Upstream, American Society of Mechanical Engineering.
68. The Taguchi approach to parameter design
69. Quality engineering using robust design
70. (2023, April 11). Witness Lanner. Available online: https://www.lanner.com/en-us/technology/witness-simulation-software.html.
71. Montgomery, D.C. (2017). Design and Analysis of Experiments, John Wiley & Sons.
72. (2023, April 11). Mathworks, Help Center, ANOVA. Available online: https://www.mathworks.com/help/stats/anova.html.
73. (2023, April 11). Mathworks, Help Center, Design of Experiments. Available online: https://www.mathworks.com/help/stats/design-of-experiments-1.html.
74. D’Orazio, L., Schirald, M.M., and Varisco, M. (2018, January 12–14). KPIs in Operations Management: Extending the ISO22400 Standard Scope. Proceedings of the Industrial Systems Engineering Conference, Palermo, Italy.