The Need for Ecosystem 4.0 to Support Maintenance 4.0: An Aviation Assembly Line Case
Source abstract
Abstract
Manufacturing and assembling aircraft require hundreds of different machines for various process applications. The machines have different complexity and often different ages; however, they have to ensure a higher precision than other industrial fields. Recent technology advancement in maintenance approaches offers a wide range of opportunities to provide performance and availability. The paper discusses how the maintenance technologies applicable to the various machines need to be appropriately supported by a production environment, called “ecosystem”, that allows their integration within the process and their synergy with the operators. (1) A background analysis of the aircraft production environment is offered. (2) A possible framework for designing a proper ecosystem 4.0 for integrating maintenance activities with design solutions and data gathering is provided. (3) A case study based on the assembly line of specific aircraft is adopted for testing the validity of the framework. (4) Finally, a discussion highlights the critical points of the research, underlying future work.
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
Logistics Engineering Laboratory, Aeronautics Institute of Technology, São José dos Campos, SP 12.228-900, Brazil
Design, Production and Management Department, University of TWENTE, 7522 NN Enschede, The Netherlands
References (29)
Show source-provided references
1. Schoenberger, R. (2021, March 22). From Cars to Planes—Aerospace Manufacturing and Design. Available online: https://www.aerospacemanufacturinganddesign.com/article/amd1114-aerospace-assembly-automation/.
2. The Growing Use of Robots by the Aerospace Industry10.1108/IR-08-2018-0160 ↗
3. A Novel Jigless Process Applied to a Robotic Cell for Aircraft Structural Assembly10.1007/s00170-020-05700-4 ↗
4. Duffuaa, S., and Raouf, A. (2015). Planning and Control of Maintenance Systems: Modelling and Analysis, Springer International Publishing. [2nd ed.].10.1007/978-3-319-19803-3 ↗
5. Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., and Nee, A.Y.C. (2019). Enabling Technologies and Tools for Digital Twin. J. Manuf. Syst.
6. He, B., and Bai, K.-J. (2020). Digital Twin-Based Sustainable Intelligent Manufacturing: A Review. Adv. Manuf.10.1007/s40436-020-00302-5 ↗
7. Big Data Analytics for Smart Factories of the Future10.1016/j.cirp.2020.05.002 ↗
8. A Holistic End-to-End Prescriptive Maintenance Framework10.1007/s41688-020-00045-z ↗
9. Marques, H., and Giacotto, A. (2019). Prescriptive Maintenance: Building Alternative Plans for Smart Operations, Swedish Society of Aeronautics and Astronautics (FTF).10.3384/ecp19162027 ↗
10. Solving Integrated Production and Condition-Based Maintenance Planning Problems by MIP Modeling10.1007/s10696-016-9244-8 ↗
11. A Procedural Approach for Realizing Prescriptive Maintenance Planning in Manufacturing Industries10.1016/j.cirp.2017.04.007 ↗
12. Optimized Maintenance Decision-Making—A Simulation-Supported Prescriptive Analytics Approach Based on Probabilistic Cost-Benefit Analysis10.36001/phme.2020.v5i1.1269 ↗
13. Smart Remanufacturing: A Review and Research Framework10.1108/JMTM-06-2019-0205 ↗
14. Disruptive Maintenance Engineering 4.010.1108/IJQRM-09-2019-0304 ↗
15. Industrial Internet of Things in the Production Environment of a Shipyard 4.010.1007/s00170-020-05229-6 ↗
16. Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives towards Industry 4.010.1016/j.inffus.2018.10.005 ↗
17. Chang, H.-M., Huang, C., and Torng, C.-C. (2013). Lean Production Implement Model for Aerospace Manufacturing Suppliers. Int. J. Innov. Manag. Technol.10.7763/IJIMT.2013.V4.400 ↗
18. Mixed Integer Programming Models for Planning Maintenance at Offshore Wind Farms under Uncertainty10.1016/j.trc.2019.12.014 ↗
19. Sarh, B. (1998). Wing Structural Assembly Methodology, SAE International.10.4271/982156 ↗
20. Kampa, A., Gołda, G., and Paprocka, I. (2017). Discrete Event Simulation Method as a Tool for Improvement of Manufacturing Systems. Computers, 6.10.3390/computers6010010 ↗
21. Kampa, A. (2018). The Review of Reliability Factors Related to Industrial Robo. Robot. Autom. Eng. J., 3.10.19080/RAEJ.2018.03.555624 ↗
22. Reliability Analysis of a Repairable Robot System10.1108/13552519610120432 ↗
23. A Report on Robot Reliability and Safety in Canada: A Survey of Robot Users10.1108/13552510010311336 ↗
24. Machine Criticality Based Maintenance Prioritization: Identifying Productivity Improvement Potential10.1108/IJPPM-07-2017-0168 ↗
25. Optimization of the Joint Selective Maintenance and Repairperson Assignment Problem under Imperfect Maintenance10.1016/j.cie.2018.09.012 ↗
26. (2021, March 11). ILO Benchmarking of Qualifications Frameworks: A Report on Potential Comparability between the Bangladesh Technical and Vocational Qualifications Framework and Qualifications Frameworks of Other Countries of Origin and Destination of Migrant Workers—Skills and Employability Network. Available online: http://apskills.ilo.org/resources/benchmarking-of-qualifications-frameworks-a-report-on-potential-comparability-between-the-bangladesh-technical-and-vocational-qualifications-framework-and-qualifications-frameworks-of-other-countries-of-origin-and-destination-of-migrant-workers.
27. (2021, March 11). Apple MacOS Big Sur. Available online: https://www.apple.com/macos/big-sur/.
28. (2021, March 11). Gurobi Software Downloads and License Center. Available online: https://www.gurobi.com/downloads/.
29. (2021, March 11). Mathworks MATLAB & Simulink. Available online: https://www.mathworks.com/products/matlab.html.