A scientific and reasonable painting process is the key to ensure the quality of the ship’s construction. The design of the ship painting process mainly includes the selection of coating matching, the development of surface treatment level, the development of secondary descaling grade and the design of the process routine. Ship painting is one of the three pillars of modern shipbuilding and is used throughout ship construction. 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). Furthermore, the accurate classification of painting objects by RF is achieved. The results show that the performance of DBSCAN is significantly improved. Effectiveness is verified by taking the outer plate above the waterline of a shipyard H1127/7 as the object. Finally, the recommendation of the painting process is realized based on the multi-objective evaluation function. Then, a painting object classification model is constructed based on the random forest (RF). ![]() The grey wolf optimization (GWO) is introduced to realize the adaptive determination of clustering parameters and avoid the deviation of clustering results. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to form categories of painting objects by cluster analysis. Therefore, an intelligent design algorithm for the ship painting process is proposed in this paper. It is not conducive to scientific management of the painting process and effective control of painting cost. Currently, the design of the painting process relies on the experience of technologists. Its quality is directly related to the service life and maintenance cost of the ship. The painting process is an essential part of the shipbuilding process.
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