Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions

• URL: http://arxiv.org/abs/2402.14882v1
• Date: Thu, 22 Feb 2024 03:31:00 GMT
• Title: Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions
• Authors: Sumin Lee, Jihoon Kim, Namwoo Kang
• Abstract summary: We propose a deep learning-based generative model for generating multiple crank-rocker four-bar linkage mechanisms. The proposed model is based on a conditional generative adversarial network (cGAN) with modifications for mechanism synthesis. The results demonstrate that the proposed model successfully generates multiple distinct mechanisms that satisfy specific kinematic and quasi-static requirements.
• Score: 22.164394511786874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mechanisms are essential components designed to perform specific tasks in various mechanical systems. However, designing a mechanism that satisfies certain kinematic or quasi-static requirements is a challenging task. The kinematic requirements may include the workspace of a mechanism, while the quasi-static requirements of a mechanism may include its torque transmission, which refers to the ability of the mechanism to transfer power and torque effectively. In this paper, we propose a deep learning-based generative model for generating multiple crank-rocker four-bar linkage mechanisms that satisfy both the kinematic and quasi-static requirements aforementioned. The proposed model is based on a conditional generative adversarial network (cGAN) with modifications for mechanism synthesis, which is trained to learn the relationship between the requirements of a mechanism with respect to linkage lengths. The results demonstrate that the proposed model successfully generates multiple distinct mechanisms that satisfy specific kinematic and quasi-static requirements. To evaluate the novelty of our approach, we provide a comparison of the samples synthesized by the proposed cGAN, traditional cVAE and NSGA-II. Our approach has several advantages over traditional design methods. It enables designers to efficiently generate multiple diverse and feasible design candidates while exploring a large design space. Also, the proposed model considers both the kinematic and quasi-static requirements, which can lead to more efficient and effective mechanisms for real-world use, making it a promising tool for linkage mechanism design.

Related papers

• Deep Reinforcement Learning for the Design of Metamaterial Mechanisms with Functional Compliance Control [1.3654846342364308]
  This study develops an efficient design methodology for compliant mechanisms using deep reinforcement learning (RL) The FEA data are learned through the RL method to obtain optimal compliant mechanisms for desired functional requirements.
  arXiv  Detail & Related papers  (2024-08-08T11:18:40Z)
• Compete and Compose: Learning Independent Mechanisms for Modular World Models [57.94106862271727]
  We present COMET, a modular world model which leverages reusable, independent mechanisms across different environments. COMET is trained on multiple environments with varying dynamics via a two-step process: competition and composition. We show that COMET is able to adapt to new environments with varying numbers of objects with improved sample efficiency compared to more conventional finetuning approaches.
  arXiv  Detail & Related papers  (2024-04-23T15:03:37Z)
• Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications [1.2661010067882734]
  We present, from relevant literature, technical details of using a deep learning approach for mechanism design. We demonstrate the power of this approach for three illustrative case studies.
  arXiv  Detail & Related papers  (2024-01-11T06:09:32Z)
• Refined Mechanism Design for Approximately Structured Priors via Active Regression [50.71772232237571]
  We consider the problem of a revenue-maximizing seller with a large number of items for sale to $n$ strategic bidders. It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously difficult to characterize or compute.
  arXiv  Detail & Related papers  (2023-10-11T20:34:17Z)
• Bayesian Modelling Approaches for Quantum States -- The Ultimate Gaussian Process States Handbook [0.0]
  This thesis discusses novel tools and techniques for the (classical) modelling of quantum many-body wavefunctions. It is outlined how synergies with standard machine learning approaches can be exploited to enable an automated inference of the most relevant intrinsic characteristics. The resulting model carries a high degree of interpretability and offers an easily applicable tool for study of quantum systems.
  arXiv  Detail & Related papers  (2023-08-15T09:37:58Z)
• Robust Hamiltonian Engineering for Interacting Qudit Systems [50.591267188664666]
  We develop a formalism for the robust dynamical decoupling and Hamiltonian engineering of strongly interacting qudit systems. We experimentally demonstrate these techniques in a strongly-interacting, disordered ensemble of spin-1 nitrogen-vacancy centers.
  arXiv  Detail & Related papers  (2023-05-16T19:12:41Z)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• Vertical Layering of Quantized Neural Networks for Heterogeneous Inference [57.42762335081385]
  We study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. We can theoretically achieve any precision network for on-demand service while only needing to train and maintain one model.
  arXiv  Detail & Related papers  (2022-12-10T15:57:38Z)
• Recommender Systems meet Mechanism Design [29.132299904090868]
  We consider a multi-item mechanism design problem where the bidders' value distributions can be approximated by a topic model. We provide an extension of the framework that allows us to exploit the expressive power of topic models to reduce the effective dimensionality of the problem.
  arXiv  Detail & Related papers  (2021-10-25T00:03:30Z)
• Transformers with Competitive Ensembles of Independent Mechanisms [97.93090139318294]
  We propose a new Transformer layer which divides the hidden representation and parameters into multiple mechanisms, which only exchange information through attention. We study TIM on a large-scale BERT model, on the Image Transformer, and on speech enhancement and find evidence for semantically meaningful specialization as well as improved performance.
  arXiv  Detail & Related papers  (2021-02-27T21:48:46Z)
• Reinforcement Learning of Sequential Price Mechanisms [24.302600030585275]
  We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms. We show that our approach can learn optimal or near-optimal mechanisms in several experimental settings.
  arXiv  Detail & Related papers  (2020-10-02T19:57:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.