...Proceedings of the ASME 2024 International Mechanical Engineering Congress and Exposition IMECE2024 November 17-21, 2024, Portland, Oregon IMECE2024-145983 ACCELERATING MICROSTRUCTURE AND MELT POOL PREDICTION IN LASER POWDER BED FUSION PROCESSES USING DATA DRIVEN NEURAL NETWORK Abdul Qadeer1, S...

...Proceedings of the ASME 2023 International Mechanical Engineering Congress and Exposition IMECE2023 October 29-November 2, 2023, New Orleans, Louisiana IMECE2023-112497 PREDICTING MULTI-MODE DYNAMIC RESPONSES OF STRUCTURES USING LONG SHORT-TERM MEMORY NEURAL NETWORKS Yabin Liao, Aviad Golan, Mark...

... prediction model, utilizing a neural network generated dataset to minimize errors in planning the motion. As a case study for implementing the guidance algorithm, a freeway entrance ramp scenario was simulated utilizing MATLAB. The guidance strategy addresses the limitations of GPS-based autonomous vehicle...

... Abstract Under a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC), a neural network-based adaptive control system has been developed and demonstrated for active wing...

... in the same line. The model can classify the objects from the production line and can also be used to classify them wherever required. data analysis experimental work neural networks Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition IMECE2022 October 30...

... are found pertaining to machining characteristics of the EDDFSG process over traditional modelling techniques. neural networks data analysis experimental work design optimization Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition IMECE2022 October 30...

... Abstract Many engineered and physical systems contain uncertain parameters and knowing their accurate value is essential to system analysis and design. In this paper, we propose a new approach that combines an artificial neural network with a Bayesian algorithm to achieve better computational...

... Abstract This paper presents an initial investigation on the feasibility of modeling structural dynamics of complex structures using the Long Short-Time Memory (LSTM) deep learning neural networks, and predicting the structures’ vibration responses due to random excitation. LSTM networks...

... solutions. In summary, ADP uses function approximators, such as neural networks, to approximate optimal control solutions. ADP can then converge to the near-optimal solution using techniques such as reinforcement learning (RL). The two main challenges in using this approach are finding a proper training...

...: [email protected] ABSTRACT Based on experimental data, a data error estimation model is developed for an existing neural network time dependent hydrodynamic force model. The force model, based on forces measured in forced motion experiments, is used to approximate the time dependent forces on a cylinder...

...MED-Vol. 10, Manufacturing Science and Engineering 1999 ASME 1999 MANUFACTURING PROCESS OPTIMIZATION WITH ARTIFICIAL NEURAL NETWORKS Wilfried Sihn & Thomas Schmidt Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) Department 110, Nobelstr. 12, D-70596 Stuttgart, GERMANY E...

.... The fast Fourier transform (FFT) of the vibration signals showing variation in amplitude of the harmonics as time progresses are presented for comparison with the full time signal feature extraction. A hybrid particle-swarm artificial Neural Networks (neuro-particle swarm) is used to identify both...

... to train and test a self organising map neural network. Results obtained from the neural network demonstrated a classification success, never lower than 99.3%, indicate that it is not only possible to detect the presence of an eyebrow by monitoring the flame, but it is also possible to give an indication...

... the correction of settings, which are introduced to classic control structures in a fuzzy control system. The non-linear process model, implemented in the controller, is based on the basis of fuzzy neural networks. This structure enables to design, learn and tune NARMAX type models (Nonlinear Auto Regressive...

... 28 03 2008 The strut is one of the most important components in a vehicle suspension system. Since it is highly non-linear it is difficult to predict its performance characteristics using a physical mathematical model. However, neural networks have been successfully used as universal...

..., and inner ear, which consists of cochlea and organ of corti. In this paper, a novel approach for human auditory model is developed that is based on the concepts of fuzzy logic for simulating basilar membrane and stereocilia, and a feed-forward neural network for simulating outer hair cell of the inner ear...