Hull Plate Bending Springback Prediction Based on Artificial Neural Network

Su, Shao Juan; Hu, Yong; Wang, Cheng Fang; Liu, Bin

doi:10.4028/www.scientific.net/amr.988.309

Cited by 2 publications

(1 citation statement)

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“…The method has lower time required for optimizing of process conditions [20]. In addtion, during the process of 3D curved hull plate forming, springback will cause serious influence on the forming accuracy, it is necessary to solve the problem of springback in the hull plate forming process by establishing a nonlinear model of plate and springback based on BP neural network [21]. Artificial neural networks (ANN) is not only an computationally efficient analysis tool, but also can predict the buckling load of laminated composite stiffened panels subjected to in-plane shear loading [22].…”

Section: Related Workmentioning

confidence: 99%

ARIMA-FEM Method with Prediction Function to Solve the Stress–Strain of Perforated Elastic Metal Plates

Chen

Dai

Zheng

2022

Metals

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Stress analysis and deformation prediction have always been the focuses of the field of mechanics. The accurate force prediction in plate deformation plays important role in the production, processing and performance analysis of materials. In this paper, we propose an ARIMA-FEM method, which can be used to solve some mechanical problems of 2D porous elastic plate. We have given a detailed theory and solving steps of ARIMA-FEM. In addition, three numerical examples are given to predict the stress–strain of thin porous elastic metal plates. This article uses CST, LST and Q4 elements to discrete the rectangular plates, square plates and circle plates with holes. As for variable force prediction, this paper compared with linear regression, nonlinear regression and neural network prediction, and the results show that the ARIMA method has a higher prediction accuracy. Furthermore, we calculate the numerical solution at four mesh scales, and the numerical convergence is consistent with the theoretical convergence, which also shows the effectiveness of our method. The image smoothing algorithm is applied to keep edge information with high resolution, which can more concisely describe the plate internal changes. Finally, the application scope of ARIMA-FEM, model expansion, superconvergence analysis and other issues have been given enlightening views in the discussion section. In fact, this algorithm combined statistics and mechanics. It also reflects the knowledge integration of interdisciplinary and uses it better to serve practical applications.

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Section: Related Workmentioning

confidence: 99%