DeepMorpher: deep learning-based design space dimensionality reduction for shape optimisation

Abbas, Asad; Rafiee, Ashkan; Haase, Max

doi:10.1080/09544828.2023.2192606

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Idea generation, which starts with problem identification in the early design phase, is the process of creating new, creative, and useful solutions [8,9]. Using deep learning methods in this process reduces design costs and enhances innovation [10][11][12]. In the literature, the number of studies involving idea and concept generation and 3D model generation based on deep learning models is rapidly increasing [13,14].…”

Section: Literature Reviewmentioning

confidence: 99%

A Generative Deep Learning Approach for Improving the Mechanical Performance of Structural Components

Yüksel,

Börklü

2024

Applied Sciences

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This study aimed to improve the mechanical properties of 3D concept designs by combining the design capability of a generative adversarial network with finite element analysis. This approach offers an innovative perspective on the conditioning of generative models while improving design properties and automation. A new design and evaluation framework has been developed for GAN models to generate 3D models with improved mechanical properties. The framework is an iterative process that includes dataset generation, GAN training, and finite element analysis. A “joint” component used in the aerospace industry is considered to demonstrate the proposed method’s effectiveness. Over six iterations, an increase of 20% is recorded in the average safety factor of the designs, and the variety of designs produced is narrowed in the desired direction. These findings suggest that the direct generation of structural components with generative models can expand the potential of deep learning in engineering design. Another innovative aspect of this study is that it provides a new option for the conditioning of data-dependent generative design models.

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Section: Literature Reviewmentioning

confidence: 99%

A Generative Deep Learning Approach for Improving the Mechanical Performance of Structural Components

Yüksel,

Börklü

2024

Applied Sciences

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“…In order to design a product using computational methods, the product needs to be represented in a way that a computer can understand. For ship design, the two most popular modes are parameterized vectors [1,[4][5][6][7][8][9][10][11][12], and free-form deformation techniques [13][14][15][16][17][18]. The benefit of using parameterized design representations for a hull is that the design is defined by a set of tunable parameters that both human designers and computers can interpret.…”

Section: Computational Ship Designmentioning

confidence: 99%

ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints

Bagazinski,

Ahmed

2023

JMSE

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Ship design is a years-long process that requires balancing complex design trade-offs to create a ship that is efficient and effective. Finding new ways to improve the ship design process could lead to significant cost savings in the time and effort required to design a ship, as well as cost savings in the procurement and operation of a ship. One promising technology is generative artificial intelligence, which has been shown to reduce design cycle times and create novel, high-performing designs. In a literature review, generative artificial intelligence was shown to generate ship hulls; however, ship design is particularly difficult, as the hull of a ship requires the consideration of many objectives. This paper presents a study on the generation of parametric ship hull designs using a parametric diffusion model that considers multiple objectives and constraints for hulls. This denoising diffusion probabilistic model (DDPM) generates the tabular parametric design vectors of a ship hull, which are then constructed into a point cloud and mesh for performance evaluation. In addition to a tabular DDPM, this paper details adding guidance to improve the quality of the generated parametric ship hull designs. By leveraging a classifier to guide sample generation, the DDPM produced feasible parametric ship hulls that maintained the coverage of the initial training dataset of ship hulls with a 99.5% rate, a 149× improvement over random sampling of the design vector parameters across the design space. Parametric ship hulls produced using performance guidance saw an average 91.4% reduction in wave drag coefficients and an average 47.9× relative increase in the total displaced volume of the hulls compared to the mean performance of the hulls in the training dataset. The use of a DDPM to generate parametric ship hulls can reduce design times by generating high-performing hull designs for future analysis. These generated hulls have low drag and high volume, which can reduce the cost of operating a ship and increase its potential to generate revenue.

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3D Ship Hull Design Direct Optimization Using Generative Adversarial Network

Trinh,

Hamagami,

Okamoto

2024

JACIII

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The direct optimization of ship hull designs using deep learning algorithms is increasingly expected, as it proposes optimization directions for designers almost instantaneously, without relying on complex, time-consuming, and expensive hydrodynamic simulations. In this study, we proposed a GAN-based 3D ship hull design optimization method. We eliminated the dependence on hydrodynamic simulations by training a separate model to predict ship performance indicators. Instead of a standard discriminator, we applied a relativistic average discriminator to obtain better feedback regarding the anomalous designs. We add two new loss functions for the generator: one restricts design variability, and the other sets improvement targets using feedback from the performance estimation model. In addition, we propose a new training strategy to improve learning effectiveness and avoid instability during training. The experimental results show that our model can optimize the form factor by 5.251% while limiting the deterioration of other indicators and the variability of the ship hull design.

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DeepMorpher: deep learning-based design space dimensionality reduction for shape optimisation

Cited by 3 publications

References 26 publications

A Generative Deep Learning Approach for Improving the Mechanical Performance of Structural Components

A Generative Deep Learning Approach for Improving the Mechanical Performance of Structural Components

ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints

3D Ship Hull Design Direct Optimization Using Generative Adversarial Network

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