... machine learning models, particularly in the analog electrical circuit synthesis community, have attempted to leverage such lumped-element models in design synthesis or forward modeling tasks. They either learn faster neural network proxies for the forward dynamics of human-crafted lumped-element...

... costs. manufacturing life cycle analysis and design neural networks sustainable design Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2024 August 25-28, 2024, Washington, DC DETC2024-143046...

... Abstract Recent published works in mechanism synthesis have shown promising results by employing machine learning-based methods such as deep neural networks. As a foundation for further research, an extensive literature review on the application of machine learning algorithms for planar...

... of traditional methods but also opens new avenues for mechanism design, providing a data-driven tool for exploring alternative designs and evaluating their performance in real-time. planar four-bar linkage path synthesis conditional variational autoencoder neural networks machine learning deep...

... of the input, large number of mechanism parameters, and highly non-linear relationship between the input and output. This paper introduces a neural network approach for the path synthesis of spatial RSCR mechanisms. We detail the method of database generation with the development of a simulator, data space...

... trained with experimental characterization of battery cells. This paper in particular presents the hardware selected for a 48 V 25 Ah battery, but the general architecture of the device and the methodology of the training procedure can be extended to any battery size. algorithms neural networks...

... selection, we propose here, a visual representation of engineering catalogs using neural networks. In particular, we employ variational autoencoders (VAEs) to project catalog data onto a lower-dimensional latent space. The latent space can then be visualized to explore the underlying structure...

... by the DQN. Consequently, singularity was avoided by selecting suitable solution types, and the angular velocity was minimized. Keywords: Robotics, Computer-Aided Engineering, Neural Networks 1. INTRODUCTION Industrial robots are expected to expand their application fields to deal with variable mixes...

...Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2023 August 20-23, 2023, Boston, Massachusetts DETC2023-113308 BAYESIAN MESH OPTIMIZATION FOR GRAPH NEURAL NETWORKS TO ENHANCE ENGINEERING PERFORMANCE...

... Abstract In the past decades, neural networks have rapidly grown in popularity as a way to model complex non-linear relationships. The computational efficiently and flexibility of neural networks has made them popular for machine learning-based optimization methods. As such the derivative...

... Abstract This paper focuses on the representation and synthesis of coupler curves of planar mechanisms using a deep neural network. While the path synthesis of planar mechanisms is not a new problem, the effective representation of coupler curves in the context of neural networks has not been...

... Abstract The State of Charge (SOC) estimation in Lithium-ion batteries is a challenging task that is currently assessed with different methods in a vast variety of applications. This paper presents the design and assessment of two SOC estimation methods, based on Artificial Neural Networks...

... Abstract Resource-constrained electronic systems are present in many semi- and fully-autonomous systems and are tasked with computationally heavy tasks such as neural network image processing. Without sufficient cooling, these tasks often increase device temperature up to a predetermined...

... differential equation of time-domain elastodynamics with a moving load. Time-domain correlation function-based features are built in order to train classifiers such as Artificial Neural Networks and Support-Vector Machines and perform damage detection. The method is tested on a bridge-shaped structure...

...-objective optimization, In this framework, a design approach based on rigorous structural optimization, genetic algorithm, neural networks. optimization methods is required for the design and development of lightweight and safe wheels for road vehicles. Structural optimization techniques can be effectively...

.... The proposed approach exploits a Genetic Algorithm (GA) in combination with Artificial Neural Networks (ANNs) for SOC estimation. Specifically, the training parameters of a Nonlinear Auto-Regressive with Exogenous inputs (NARX) ANN are chosen by the GA-based optimization. As a consequence of the GA-based...

... to physical principles. However, these physics-based models tend to be too computationally expensive for real-time process control. Hence, in this work, a hybrid model utilizing neural networks is proposed and demonstrated to be an accurate and efficient alternative for predicting melt pool geometries in AM...

... the abundance of data to discover effective control designs. In this paper, we investigate the efficacy of a data-driven approach towards in-situ modeling of melt-pool geometry. Specifically, we propose a new methodology that uses a deep neural network architecture to predict melt pool geometries with linear...

... such a system on the sum of all digital engineering documents suggests a future where design failures are less likely to be repeated and past successes may be consistently used to forward innovation. design automation design representation functional reasoning neural networks product development...

... capabilities to knowledge-based mechanical power transmission CAD programs. design automation gearboxes MCAE neural networks DEVELOPMENT OF A MECHANICAL POWER TRANSMISSION DESIGN SUPERVISOR SYSTEM John R. Goulding and Hormoz Zarefar Department of Mechanical Engineering Portland State University...