KR-20260062253-A - Generative AI-based co-design method for industrial and engineering designers and apparatus thereof

KR20260062253AKR 20260062253 AKR20260062253 AKR 20260062253AKR-20260062253-A

Abstract

An embodiment of the present invention may provide a generative AI-based co-design method for industrial designers and engineers, performed by a computing device, comprising: a process of inputting a reference design for a product into a topology optimization model to obtain an output topology optimization design; and a process of inputting the topology optimization design into a rendering model to obtain an output rendering image.

Inventors

유소영

Assignees

주식회사 나니아랩스

Dates

Publication Date: 20260507
Application Date: 20241028

Claims (16)

In a generative AI-based co-design execution method for industrial designers and engineers performed by a computing device, A process of inputting a reference design for a product into a topology optimization model to obtain an output topology optimization design; A generative AI-based co-design method for industrial designers and engineers, comprising the process of inputting the above-mentioned topology optimization design into a rendering model to obtain an output rendering image.
In paragraph 1, A process of generating a topology-optimized design according to topology optimization conditions by applying topology optimization to the above reference design; A generative AI-based co-design method for industrial designers and engineers, further comprising the process of training the topology optimization model with training data configured as inputs for the above reference design and topology optimization conditions, and configured as outputs for the above topology optimization design.
In paragraph 1, The process of generating a topology-optimized design according to topology optimization conditions by applying topology optimization to the above reference design is, A generative AI-based co-design method for industrial designers and engineers, characterized by applying piece topology optimization after carving the above reference design and generating the above topology optimized design through rotation post-processing.
In paragraph 1, A generative AI-based co-design method for industrial designers and engineers, characterized in that the topology optimization model includes a pre-trained encoder and decoder and a U-net located between the encoder and decoder, and during the training process of the topology optimization model, only the U-net is trained and updated.
In paragraph 1, A generative AI-based co-design method for industrial designers and engineers, characterized in that the rendering model is a pre-trained artificial intelligence model that receives the topology optimization design and style text for the product as input and outputs a realistic rendering image of the product.
In a generative AI-based co-design execution method for industrial designers and engineers performed by a computing device, A process of inputting a rendering design image of a product into a depth model to obtain an output depth map image; A generative AI-based co-design method for industrial designers and engineers, comprising the process of inputting the above depth map image into a 3D shape reconstruction model to obtain an output 3D mesh shape model.
In paragraph 6, A process of acquiring the depth map image paired with the rendering design image above; The process of augmenting the above-mentioned paired rendering design image and depth map image; A process of preprocessing the above-mentioned augmented rendering design image and depth map image; A generative AI-based co-design method for industrial designers and engineers, further comprising the process of training the depth model with training data configured with the preprocessed rendering design image as input and the preprocessed depth map image as output.
In Paragraph 7, A generative AI-based co-design method for industrial designers and engineers, characterized in that the training data includes all images in which, during the preprocessing process, padding is applied to the periphery of the product object image, the background image is replaced with another background image, the background image is replaced with a solid color background, noise is added to the product object image or background image, the scale of the product object image or background image is adjusted, or blur is added to the product object image or background image.
In Paragraph 7, A generative AI-based co-design method for industrial designers and engineers, characterized in that the depth model is a Marigold model based on a stable diffusion architecture fine-tuned using the preprocessed rendering design image and the preprocessed depth map image as training data.
In paragraph 6, The process of inputting the above depth map image into a 3D shape reconstruction model to obtain the output 3D mesh shape modeling is, If the above product has a rotationally symmetric structure with respect to a central axis, A process of obtaining a first area point of the product located parallel to a plane perpendicular to the central axis from the depth map image; A process of acquiring a second area point of the product located parallel to the central axis; A process of obtaining total area points for the product by combining the first area point and the second area point; A generative AI-based co-design method for industrial designers and engineers, comprising the process of obtaining a three-dimensional mesh shape model of the product based on the entire area points.
In a generative AI-based co-design execution method for industrial designers and engineers performed by a computing device, A process of inputting multiple rendering design images of a product into a depth model to obtain multiple output depth map images; A process of inputting the above-mentioned plurality of depth map images into a feature extraction model to obtain a plurality of output feature embeddings; A process of sampling by cluster after clustering the above plurality of feature embeddings; A process of inputting rendering design images sampled for each of the above clusters into a performance evaluation model to obtain output structural performance; A process of inputting rendered design images sampled for each of the above clusters into a style evaluation model to obtain output style-specific scores; A generative AI-based co-design method for industrial designers and engineers, comprising a process of exploring sampled rendering designs by structural performance or style based on scores by structural performance and style of rendering design images sampled by the clusters mentioned above.
In Paragraph 11, A generative AI-based co-design method for industrial designers and engineers, characterized in that the above performance evaluation model is trained using structural performance measured by structural analysis of a 3D mesh shape model generated based on rendering design images sampled for each cluster as training data.
In Paragraph 11, A generative AI-based co-design method for industrial designers and engineers, characterized in that the style evaluation model is trained using product design images and product style keywords as training data, or is trained using image-text pairs unrelated to the product.
In a generative AI-based co-design execution device for industrial designers and engineers comprising a process, the process is, A process of inputting a reference design for a product into a topology optimization model to obtain an output topology optimization design; A generative AI-based co-design execution device for industrial designers and engineers, comprising the process of inputting the above-mentioned topology optimization design into a rendering model to obtain an output rendering image.
In a generative AI-based co-design execution device for industrial designers and engineers comprising a process, the process is, A process of inputting a rendering design image of a product into a depth model to obtain an output depth map image; Generative AI-based co-design execution device for industrial designers and engineers, comprising the process of inputting the above depth map image into a 3D shape reconstruction model to obtain an output 3D mesh shape model.
In a generative AI-based co-design execution device for industrial designers and engineers comprising a process, the process is, A process of inputting multiple rendering design images of a product into a depth model to obtain multiple output depth map images; A process of inputting the above-mentioned plurality of depth map images into a feature extraction model to obtain a plurality of output feature embeddings; A process of sampling by cluster after clustering the above plurality of feature embeddings; A process of inputting rendering design images sampled for each of the above clusters into a performance evaluation model to obtain output structural performance; A process of inputting rendered design images sampled for each of the above clusters into a style evaluation model to obtain output style-specific scores; A generative AI-based co-design execution device for industrial designers and engineers, comprising a process of exploring sampled rendering designs by structural performance or style based on scores by structural performance and style of rendering design images sampled by the clusters mentioned above.

Description

Generative AI-based co-design method for industrial and engineering designers and apparatus thereof The present invention relates to a generative AI-based co-design execution method and apparatus for industrial designers and engineers, and more specifically, to a generative AI-based co-design execution method and apparatus for industrial designers and engineers that generates a product concept design, reconstructs a 3D shape from an image, and explores a design. Automotive wheel design is a crucial factor influencing a vehicle's appearance and performance, making a balance between aesthetic and engineering requirements essential. The conventional wheel design process, which involves the sequential progression of creative styling by exterior designers and structural analysis by engineers, has been time-consuming and costly due to repetitive modifications, making it difficult to find the optimal balance. Recently, research on AI-based design assistance has become active due to the advancement of foundation models. However, while text-based design generative models such as DALL-E and Midjourney can be helpful in providing visual inspiration, they have limitations in applying to the entire design process because they fail to consider engineering constraints and lack connectivity with 3D modeling. FIG. 1 is a block diagram of a generative AI-based co-design execution device for industrial designers and engineers according to an embodiment of the present invention. FIG. 2 is a flowchart of a generative AI-based co-design method for industrial designers and engineers according to an embodiment of the present invention. FIG. 3 is a functional block diagram of a generative AI-based co-design execution device for industrial designers and engineers according to an embodiment of the present invention. FIG. 4 is an overall conceptual diagram of a generative AI-based co-design execution framework for industrial designers and engineers according to an embodiment of the present invention. FIG. 5 is a flowchart of a method for generating a product concept design according to an embodiment of the present invention. FIG. 6 is a conceptual diagram of a design generation method using topology optimization according to an embodiment of the present invention. FIG. 7 is a conceptual block diagram of a topology optimization model according to an embodiment of the present invention. FIG. 8 is a conceptual block diagram of a rendering model according to an embodiment of the present invention. FIG. 9 is a flowchart of a 3D shape reconstruction method according to an embodiment of the present invention. Figure 10 is a conceptual diagram of generating a depth map image paired with a product image. FIG. 11 is a conceptual block diagram of a depth model according to an embodiment of the present invention. Figure 12 is an example of augmenting a depth map image paired with a product image. Figure 13 is an example of constructing training data by preprocessing depth map images paired with product images. Figure 14a is an example of the learning process of the Marigold model. Figure 14b is an example of the cognitive process of the Marigold model. Figure 15 is an example comparing the performance of the Marigold model (baseline), the Depth Anything model (baseline), and the fine-tuned Marigold model. FIG. 16 is a flowchart of a method for obtaining a three-dimensional shape according to an embodiment of the present invention. FIG. 17 is an example of the process of acquiring a first region according to an embodiment of the present invention. FIG. 18 is an example of the process of acquiring a second region according to an embodiment of the present invention. FIG. 19 is an example of a process of combining regions according to an embodiment of the present invention. FIG. 20 is an example of scaling and alignment of a first region and a second region according to an embodiment of the present invention. FIG. 21 is an example of a process for generating a three-dimensional mesh shape of a product according to an embodiment of the present invention. Figure 22 is an example of a three-dimensional mesh shape of a product generated from a product image through the S200 process. FIG. 23 is a flowchart of a design search method according to an embodiment of the present invention. FIG. 24 is a conceptual diagram of a design search method according to an embodiment of the present invention. Figure 25 is an example of a design space based on low-dimensional feature embeddings for a product design. Figure 26 is an example of product design clustering. Figure 27 is an example of design sampling from clustered product designs. Figure 28 is a flowchart of a method for style-engineering dual evaluation of a product. Figure 29 is an example of converting a 3D shape model from a mesh type to a NURBS type. Figure 30 is an example of evaluating structural performance through structural analysis of a three-dimensional shape model. FIG. 31 is a conceptual diagram o