Abstract
Topology optimization is a powerful tool for producing an optimal free-form design from input mechanical constraints. However, in its traditional-density-based approach, it does not feature a proper definition for the external boundary. Therefore, the integration of shape-related constraints remains hard. It requires the experts’ intervention to interpret the generated designs into parametric shapes; thus, making the design process time-consuming. With the growing role of additive manufacturing in the industry, developing a design approach considering mechanical and geometrical constraints simultaneously becomes an interesting way to integrate manufacturing and aesthetics constraints into mechanical design. In this paper, we propose to generate mechanically and geometrically valid designs using a deep-learning solution trained via a dual-discriminator Generative Adversarial Network (GAN) framework. This Deep-learning-geometrical-driven solution generates designs very similar to traditional topology optimization’s outputs in a fraction of time.
Moreover, it allows an easy shape fine-tuning by a simple increase/decrease of the input geometrical condition (here the total-bar-count), a task that a traditional topology optimization cannot achieve.
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Notes
- 1.
The architecture of the residual unit block used in this work is detailed in [32].
- 2.
The stem and inception/reduction blocks used defers from the original paper [27] only by the number of input/output feature maps.
- 3.
This code is available on the GitHub repository: https://github.com/dbetteb/TOP_OPT.git.
- 4.
A loaded node \(n_e\) located at line \(i\) and column \(j\) tilted \(\theta \) degrees has \(F_x(i,j) = cos(\theta )\) and \(F_y(i,j) = sin(\theta )\); the magnitude of the loads were set to \(1.0\) \(N\).
- 5.
A fixed bar is a bar where boundary conditions are applied. A loaded bar is a bar where a load is applied.
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This work was supported by Expleo France.
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Almasri, W., Bettebghor, D., Ababsa, F., Danglade, F., Adjed, F. (2021). Deep Learning Architecture for Topological Optimized Mechanical Design Generation with Complex Shape Criterion. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_19
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