Deflection Intelligent Prediction for High-Strength Steel Saddle Plate Forming Applicable to Reducing Ship Weight

Author:

Wang Shun1ORCID,Dai Jinliang1,Xu Zhikang1,Wang Ji2ORCID,Li Rui2,Wang Jiayan1

Affiliation:

1. Naval Architecture and Ocean Engineering College, Dalian Maritime University, Dalian 116026, China

2. School of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian 116024, China

Abstract

The application of high-strength steel plates can reduce ship weight, and the saddle plate is one of the most common types of double-curved hull plates. To fill the research gap regarding high-strength steel saddle plates, two prediction models are established here to predict deformation in saddle plate forming. Deflection is a key parameter reflecting the overall deformation of a curved plate. Therefore, first of all, the influencing factors of the line heating of high-strength steel saddle plates were analyzed. The influence of plate geometric parameters and forming parameters on deflection was researched. Second, a multiple linear regression model between deflection and the geometric parameters and forming parameters of high-strength steel saddle plates was established. Finally, to solve the problem of a large error in the multivariate regression model for extrapolation, an intelligent prediction program for deflection based on a support vector machine (SVM) was developed using the Python language. The results show that the error of the multiple regression model was less than 5% for data interpolation. The error of the intelligent prediction model for deflection was less than 5% for data extrapolation. This research can provide data support for the automatic forming of marine saddle plates.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Major Project of Science and Technology of Liaoning Province

High Technology Ship Scientific Research Project of the Ministry of Industry and Information Technology of China

Publisher

MDPI AG

Subject

General Materials Science

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