Abstract
Engineering design research integrating artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study proposes a deep learning-based CAD/CAE framework in the conceptual design phase that automatically generates 3D CAD designs and evaluates their engineering performance. The proposed framework comprises seven stages: (1) 2D generative design, (2) dimensionality reduction, (3) design of experiment in latent space, (4) CAD automation, (5) CAE automation, (6) transfer learning, and (7) visualization and analysis. The proposed framework is demonstrated through a road wheel design case study and indicates that AI can be practically incorporated into an end-use product design project. Engineers and industrial designers can jointly review a large number of generated 3D CAD models by using this framework along with the engineering performance results estimated by AI and find conceptual design candidates for the subsequent detailed design stage.
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Acknowledgements
The authors would like to thank Hyundai Motor Company’s Jiun Lee, Sangmin Lee, Min Kyoo Kang, ChangGon Kim, and ChulWoo Jung for their valuable feedback and ideas on our research. We would also like to thank Altair Korea’s Jeongsun Lee and Seung-hoon Lee for their help in automating the CAE process.
Funding
This work was supported by Hyundai Motor Company and the National Research Foundation of Korea (NRF) grants funded by the Korean government (grant numbers 2017R1C1B2005266, 2018R1A5A7025409).
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Appendices
Appendix 1.
1.1 Data augmentation effect on autoencoder
The effect of data augmentation was confirmed. We compared the learning curve without data augmentation and the case with data augmentation, as shown in Fig. 27. The loss value for data augmentation is relatively small. We checked the example of reconstruction without data augmentation, as shown in Fig. 28. These images are the same in Fig. 6 but with relatively poor quality.
Comparison of loss value between data augmentation and without data augmentation
Reconstructed wheel image without data augmentation
Appendix 2.
1.1 Detailed algorithm for sorting and grouping points
We introduce a detailed algorithm to select and group each point. The following initial preparations are required: The first coordinate (x0, y0) of array A, where the whole point was stored, is declared as the initial value. The coordinates (x0, y0) used as the initial value (x _ init, y_init) are deleted from array A. An array is created to store the points to be grouped. The initial value (x _ init, y _ init) is stored in the i-th array of the group array. At this time, i is zero. At the end of the initial operation, the for loop is executed as follows.
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The initial value is declared as a fixed point, and the nearest fixed point is obtained in array A.
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The closest point is declared as a new initial value and deleted from array A.
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The distance between the fixed point and the initial value (the closest point to the fixed point) is calculated. If the distance between the fixed point and the initial value does not exceed the threshold, the initial value is stored in the i-th group array. Otherwise, a new (i + 1)-th group array is created, and the initial value is stored in the (i + 1)-th group array. The for loop process is repeated until array A is empty. Any point cannot belong to another group at the same time because it is declared as the initial value and deleted from array A.
For determining the distance threshold, an initial test was conducted at equal intervals of 10 steps from 10 to 100. Thus, all points were completely separated into each spoke-shaped group when the threshold reached 10. A second test was conducted at equal intervals of five steps from 1 to 10 to confirm the precise distance threshold. In the second test, all points were completely separated from the threshold of three. Therefore, the final threshold was chosen as five, which is the next value of the lowest threshold. Figure 29 shows the group’s separation results for each threshold.
Tests for determining the distance threshold
At the end of the above grouping process, all points are sorted in order. We can then take one point of the group array along the same interval and save it as a new array to store fewer points than the existing array. The spline curve becomes a closed curve when the first coordinates of the stored new array are inserted at the end. Closed curves are recognized as surfaces in CAD software, enabling “body” generation. Figure 30 shows examples of spline, reduced point spline, and closed curves.
Example of spline curve
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Yoo, S., Lee, S., Kim, S. et al. Integrating deep learning into CAD/CAE system: generative design and evaluation of 3D conceptual wheel. Struct Multidisc Optim 64, 2725–2747 (2021). https://doi.org/10.1007/s00158-021-02953-9
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DOI: https://doi.org/10.1007/s00158-021-02953-9