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).
Highlights
Ship painting is one of the three pillars of modern shipbuilding and is used throughout ship construction [1]
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 [2]
The design of the ship painting process mainly relies on the experience of technologists and no scientific process design flow and specification have been formed
Summary
Ship painting is one of the three pillars of modern shipbuilding and is used throughout ship construction [1]. It is important to adopt the advanced concept and intelligent algorithm to realize accurate recommendations of the painting process for scientific management. This is the way to promote the intelligent development of ship painting. Step 1: For each decision tree, N training samples with P attributes are sampled N2.2t.iCmleusstewriinthg opfuPta-binatcinkgrOepbejetcittisoBnasseudsoinngIDthBeSCbAooNtstrap sampling method to construct the saSminpcelepMai,nwtinhgeroebsjoemctse dofotnhoetdpaotassaerses ncaevteegrodrryalwabnealsf,teRrFNcatinmneost obfesdaimrepcltilnyguasned dfoor ncloatssbiefcicoamtieontr.aTihneinrgefsoarme, pclues,tewrhaincahlyasreisciaslfleirdstopuetr-ofof-rbmaegddoantat.he painting objects to form categSotreipes2.:CTlhuestdereicnisgioanlgtorreiethTmis, agsenaetryapteedofournthsueptrearivniisnegd sletarsnaminpg,leis.tRheanpdromcelsys soefledcitpviadtitnrigbtuhtessafrmomplethsetPinattotrkibculutesste(rps
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