Ship Painting Process Design Based on IDBSACN-RF
Source abstract
Abstract
The painting process is an essential part of the shipbuilding process. Its quality is directly related to the service life and maintenance cost of the ship. Currently, the design of the painting process relies on the experience of technologists. It is not conducive to scientific management of the painting process and effective control of painting cost. Therefore, an intelligent design algorithm for the ship painting process is proposed in this paper. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to form categories of painting objects by cluster analysis. The grey wolf optimization (GWO) is introduced to realize the adaptive determination of clustering parameters and avoid the deviation of clustering results. Then, a painting object classification model is constructed based on the random forest (RF). Finally, the recommendation of the painting process is realized based on the multi-objective evaluation function. Effectiveness is verified by taking the outer plate above the waterline of a shipyard H1127/7 as the object. The results show that the performance of DBSCAN is significantly improved. Furthermore, the accurate classification of painting objects by RF is achieved. The experiment proves that the dry film thickness qualification rate obtained by the painting process designed by IDBSCAN-RF is 92.3%, which meets the requirements of the performance standard of protective coatings (PSPC).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Structural Integrity and Reliability Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Shanghai Shipbuilding Technology Research Institute, Shanghai 200032, China
Funding
Ministry of Industry and Information Technology High-Tech Ship Research Project: Research on Development and Application of Digital Process Design System for Ship Coating · National Natural Science Foundation of China · Natural Science Foundation of Jiangsu Province
References (40)
Show source-provided references
1. Analysis of Hull Coating Renewal in Ship Repairing10.5957/JSPD.33.3.150037 ↗
2. Coating matching recommendation based on improved fuzzy comprehensive evaluation and collaborative filtering algorithm10.1038/s41598-021-93628-4 ↗
3. Biofilm community structure and the associated drag penalties of a groomed fouling release ship hull coating10.1080/08927014.2017.1417395 ↗
4. Minimizing the Cost of Coating Ships10.5957/JSPD.29.3.130041 ↗
5. Toward energy-efficient online Complete Coverage Path Planning of a ship hull maintenance robot based on Glasius Bio-inspired Neural Network10.1016/j.eswa.2021.115940 ↗
6. Muthugala, M.A.V.J., Le, A.V., Cruz, E.S., Elara, M.R., Veerajagadheswar, P., and Kumar, M. (2020). A Self-Organizing Fuzzy Logic Classifier for Benchmarking Robot-Aided Blasting of Ship Hulls. Sensors, 20.10.3390/s20113215 ↗
7. Kern, T., Krhač, E., Senegačnik, M., and Urh, B. (2019). Digitalizing the Paints and Coatings Development Process. Processes, 7.10.3390/pr7080539 ↗
8. Evaluating vehicle painting plans in an automobile assembly plant using an integrated AHP-PROMETHEE approach10.1111/itor.12179 ↗
9. Inventory and life cycle assessment of an Italian automotive painting process10.1007/s10098-019-01780-3 ↗
10. Tourism Crafts Design System and Intelligent Technology Based on Virtual Design and Genetic Algorithm
11. Mathematical modeling and intelligent optimization of submerged arc welding process parameters using hybrid PSO-GA evolutionary algorithms10.1007/s00521-019-04404-5 ↗
12. Chaotic particle swarm optimization for assembly sequence planning10.1016/j.rcim.2009.05.003 ↗
13. An intelligent interactive approach for assembly process planning based on hierarchical classification of parts10.1007/s00170-013-5426-7 ↗
14. Optimization of electrospinning process using intelligent control systems10.3233/IFS-2012-0578 ↗
15. Application of intelligent water drop algorithm in process planning optimization10.1007/s00170-019-04850-4 ↗
16. Development of mathematical model with a genetic algorithm for automatic GMA welding process10.1007/s00170-014-5842-3 ↗
17. Intelligent generation method of 3D machining process based on process knowledge10.1080/0951192X.2019.1690687 ↗
18. Research on Sentiment Classification Algorithms on Online Review
19. Hybridized classification algorithms for data classification applications: A review10.1016/j.eij.2020.07.004 ↗
20. A comparison of random forest based algorithms: Random credal random forest versus oblique random forest10.1007/s00500-018-3628-5 ↗
21. Large group activity security risk assessment and risk early warning based on random forest algorithm10.1016/j.patrec.2021.01.008 ↗
22. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery10.1177/0142331217708242 ↗
23. Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach10.1007/s11269-018-2102-6 ↗
24. Automatic clustering algorithms: A systematic review and bibliometric analysis of relevant literature10.1007/s00521-020-05395-4 ↗
25. An efficient automated incremental density-based algorithm for clustering and classification10.1016/j.future.2020.08.031 ↗
26. Sampling approaches for applying DBSCAN to large datasets10.1016/j.patrec.2018.12.010 ↗
27. Grey Wolf Optimizer10.1016/j.advengsoft.2013.12.007 ↗
28. Grey wolf optimizer: A review of recent variants and applications10.1007/s00521-017-3272-5 ↗
29. A survey on swarm intelligence approaches to feature selection in data mining10.1016/j.swevo.2020.100663 ↗
30. Parameter Analysis for the Lateral Thickness of the Coated Layer to Improve Product Quality in Large Area Roll-to-Roll Slot-Die Coating Process10.7736/KSPE.2015.32.2.159 ↗
31. Calculation of coating consumption quota for ship painting: A CS-GBRT approach10.1007/s11998-020-00376-7 ↗
32. Differentiation degree combination weighting method for investment decision-making risk assessment in power grid construction projects10.1016/j.gloei.2019.11.022 ↗
33. Developing an evaluation method for SCADA-Controlled urban gas infrastructure hierarchical design using multi-level fuzzy comprehensive evaluation10.1016/j.ijcip.2020.100375 ↗
34. A new TMY generation method based on the entropy-based TOPSIS theory for different climatic zones in China10.1016/j.energy.2021.120723 ↗
35. Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model10.1016/S2095-3119(20)63483-9 ↗
36. Kim, J.-H., Choi, J.-H., Yoo, K.-H., Loh, W.-K., and Nasridinov, A. (2019). A Fast Algorithm for Identifying Density-Based Clustering Structures Using a Constraint Graph. Electronics, 8.10.3390/electronics8101094 ↗
37. A new hybridization of DBSCAN and fuzzy earthworm optimization algorithm for data cube clustering10.1007/s00500-020-04881-0 ↗
38. A new hybrid method based on partitioning-based DBSCAN and ant clustering10.1016/j.eswa.2011.01.135 ↗
39. Research on the role of AHP- entropy method in the identification and evaluation of China tariff source risk10.3233/JIFS-169399 ↗
40. The influence of soluble salts content on the performance of an epoxy coating system via accelerated corrosion tests