...Jana I. Saadi; Maria C. Yang Generative design tools empowered by recent advancements in artificial intelligence (AI) offer the opportunity for human designers and design tools to collaborate in new, more advanced modes throughout various stages of the product design process to facilitate...

... approach to bridge the gap. This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely generative pre...

...Chenxi Yuan; Tucker Marion; Mohsen Moghaddam Generative adversarial networks (GANs) have shown remarkable success in various generative design tasks, from topology optimization to material design, and shape parametrization. However, most generative design approaches based on GANs lack evaluation...

... 2022 09 12 2022 design automation artificial intelligence deep learning generalizability agent-based design data-driven design design representation generative design machine learning Monte Carlo Tree Search Solving design problems is one of the most ubiquitous processes...

... to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance...

...-Axis Parametrized Helical Additive Manufacturing ,” J. Mater. Process. Technol. , 304 , p. 117565 . 10.1016/j.jmatprotec.2022.117565 [38] Gunpinar , E. , Coskun , U. C. , Ozsipahi , M. , and Gunpinar , S. , 2019 , “ A Generative Design and Drag Coefficient Prediction System...

.... , and Hong , J. , 2019 , “ Non-Iterative Structural Topology Optimization Using Deep Learning ,” Comput. Aided Des. , 115 , pp. 172 – 180 . 10.1016/j.cad.2019.05.038 [24] Li , B. , Tang , W. , Ding , S. , and Hong , J. , 2020 , “ A Generative Design Method for Structural...

...Xingang Li; Charles Xie; Zhenghui Sha In this paper, we present a predictive and generative design approach for supporting the conceptual design of product shapes in 3D meshes. We develop a target-embedding variational autoencoder (TEVAE) neural network architecture, which consists of two modules...

... ournal of M echanical D esign . 14 03 2022 21 04 2022 24 04 2022 13 06 2022 computer-aided design conceptual design soft robotics design automation generative design shape grammars There is an increasing interest in the design of soft robots since they have...

... converge, saving computational cost. We consider a baseline for DGMs in TO, in which an existing generative architecture is applied to a dataset of TO-generated designs and the network is trained to generate samples mimicking the training data. A few papers fall within this category of methods: Rawat...

... performance evaluation functions, design generation, design exploration, and image post-processing methods are proposed with the help of domain knowledge of the tread pattern. The numerical results show that the proposed automatic design framework successfully creates various tread patterns satisfying...

... optimization generative design machine learning uncertainty modeling 21 04 2021 13 10 2021 15 10 2021 06 12 2021 Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . Email: fuge@umd.edu Email: wchen459...

... will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades. inverse design invertible neural network machine learning probabilistic modeling Gaussian process gas turbine aerodynamic design data-driven design design automation design methodology generative design...

... 19 12 2020 03 02 2021 artificial intelligence design optimization generative design machine learning Topology optimization of solid structures involves generating optimized shapes by minimizing an objective function such as compliance or mass within a material domain, subject...

...Wei Chen; Faez Ahmed Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: (1) generated designs lack diversity and do not cover all areas of the design...

..., the char-RNN also generated designs differing from the training set at a rate of over 99%, showing generalized learning. A formal language using a context-free grammar G defines a space of possible assembly configurations using a Backus-Naur form (BNF) notation [ 16 ]. The generative grammar G...

... optimization generative design mechanism synthesis robot design robotic systems Passive dynamic mechanisms, i.e., mechanisms without actuation, can perform well-defined tasks in a disturbance-free environment. Examples exist in the domains of passive walking [ 1 – 3 ] and brachiating, which...

...-aided design conceptual design design automation generative design metamodeling 01 03 2019 12 08 2019 13 08 2019 09 09 2019 Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . Email: ctucker4@psu.edu...

...Shahroz Khan; Erkan Gunpinar; Masaki Moriguchi; Hiromasa Suzuki In this paper, a generative design approach is proposed that involves the users’ psychological aspect in the design space exploration stage to create distinct design alternatives. Users’ perceptual judgment about designs is extracted...

...Sangeun Oh; Yongsu Jung; Seongsin Kim; Ikjin Lee; Namwoo Kang Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work...