Exploring optimal support conditions of plates in manufacturing using classical and Machine Learning-driven optimization techniques
Year: 2026
Type: article
Abstract: Large metal plates, such as those utilized in the shipbuilding industry, frequently exhibit undesirable deformations during manufacturing steps or the storage process due to their substantial self-weight. These deformations can, on occasion, be so significant that seamless further processing of the plates becomes unfeasible. Such cases require complex work steps, resulting in additional costs due to labor and delays. Ideal support or storage conditions can mitigate these negative effects. However, they cannot always be realized due to the large number and dimensions of the plates. Consequently, numerical analyses can be employed to predict the deformations that occur in relation to specific support conditions. These analyses can then provide an optimal storage recommendation that can be implemented in accordance with the actual on-site possibilities. The geometry of the investigated plates was captured using a three-dimensional (3D) laser scanning device. After adequate preprocessing steps and reverse engineering of the data, the geometries were integrated into a deformation analysis framework based on Isogeometric Analysis (IGA). The intention of using IGA is to exploit its advantages when modeling the plate geometry, such as its accurate and smooth representation. While IGA as well as classical and ML-assisted optimization are established individually, there has been no systematic exploration of their combined application to support condition optimization. The influence of different support conditions on the deformation of the plates is investigated and demonstrated.
Source: Procedia CIRP
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7207576584