{"title":"Curved Hull Plate Classification for Determining Forming Method using Deep Learning","authors":"Byeong-Eun Kim, S. Son, C. Ryu, J. Shin","doi":"10.5957/JSPD.04180011","DOIUrl":null,"url":null,"abstract":"Curved hull plate forming, the process of forming a flat plate into a curved surface that can fit into the outer shell of a ship's hull, can be achieved through either cold or thermal forming processes, with the latter processes further subcategorizable into line or triangle heating. The appropriate forming process is determined from the plate shape and surface classification, which must be determined in advance to establish a precise production plan. In this study, an algorithm to extract two-dimensional features of constant size from three-dimensional design information was developed to enable the application of machine and deep learning technologies to hull plates with arbitrary polygonal shapes. Several candidate classifiers were implemented by applying learning algorithms to datasets comprising calculated features and labels corresponding to various hull plate types, with the performance of each classifier evaluated using cross-validation. A classifier applying a convolution neural network as a deep learning technology was found to have the highest prediction accuracy, which exceeded the accuracies obtained in previous hull plate classification studies. The results of this study demonstrate that it is possible to automatically classify hull plates with high accuracy using deep learning technologies and that a perfect level of classification accuracy can be approached by obtaining further plate data.\n \n \n The outer shell of a ship is composed of hull plates that are generally formed as curved surfaces. To produce a curved surface from a flat steel plate, a curved hull plate-forming process involving the application of heat or pressure to the plate must be undertaken. Such forming processes can be categorized as either cold forming, in which the plate is bent using physical pressure, or thermal forming, in which bending stress is generated by applying heat to the plate. The former process is generally used to bend plates into cylindrical shapes using a rolling machine, whereas the latter is used to form more complex curved surfaces. In most shipyards, thermal forming is performed by skilled workers who apply direct heat to plates using a torch; accordingly, thermal forming is more difficult and time-consuming than machine-based cold forming and often constitutes a crucial bottleneck process in shipyard operation.\n","PeriodicalId":48791,"journal":{"name":"Journal of Ship Production and Design","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ship Production and Design","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5957/JSPD.04180011","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
引用次数: 2
Abstract
Curved hull plate forming, the process of forming a flat plate into a curved surface that can fit into the outer shell of a ship's hull, can be achieved through either cold or thermal forming processes, with the latter processes further subcategorizable into line or triangle heating. The appropriate forming process is determined from the plate shape and surface classification, which must be determined in advance to establish a precise production plan. In this study, an algorithm to extract two-dimensional features of constant size from three-dimensional design information was developed to enable the application of machine and deep learning technologies to hull plates with arbitrary polygonal shapes. Several candidate classifiers were implemented by applying learning algorithms to datasets comprising calculated features and labels corresponding to various hull plate types, with the performance of each classifier evaluated using cross-validation. A classifier applying a convolution neural network as a deep learning technology was found to have the highest prediction accuracy, which exceeded the accuracies obtained in previous hull plate classification studies. The results of this study demonstrate that it is possible to automatically classify hull plates with high accuracy using deep learning technologies and that a perfect level of classification accuracy can be approached by obtaining further plate data.
The outer shell of a ship is composed of hull plates that are generally formed as curved surfaces. To produce a curved surface from a flat steel plate, a curved hull plate-forming process involving the application of heat or pressure to the plate must be undertaken. Such forming processes can be categorized as either cold forming, in which the plate is bent using physical pressure, or thermal forming, in which bending stress is generated by applying heat to the plate. The former process is generally used to bend plates into cylindrical shapes using a rolling machine, whereas the latter is used to form more complex curved surfaces. In most shipyards, thermal forming is performed by skilled workers who apply direct heat to plates using a torch; accordingly, thermal forming is more difficult and time-consuming than machine-based cold forming and often constitutes a crucial bottleneck process in shipyard operation.
期刊介绍:
Original and timely technical papers addressing problems of shipyard techniques and production of merchant and naval ships appear in this quarterly publication. Since its inception, the Journal of Ship Production and Design (formerly the Journal of Ship Production) has been a forum for peer-reviewed, professionally edited papers from academic and industry sources. As such it has influenced the worldwide development of ship production engineering as a fully qualified professional discipline. The expanded scope seeks papers in additional areas, specifically ship design, including design for production, plus other marine technology topics, such as ship operations, shipping economics, and safety. Each issue contains a well-rounded selection of technical papers relevant to marine professionals.