... Email: saiswarupreddy4399@gmail.com Email: aunmesh@purdue.edu Email: ramani@purdue.edu 2 These authors contributed equally to this work. 14 12 2023 27 07 2024 01 08 2024 09 09 2024 Graphical Abstract Figure dataset machine learning computer...

... additive manufacturing knowledge knowledge transferability analysis knowledge transfer machine learning transfer learning McGill University 10.13039/100008582 00157 Mitacs 10.13039/501100004489 IT13369 National Research Council Canada 10.13039/501100000046 NRC INT-015-1...

... but detect spurious vortices (false positives and false negatives), making these methods less robust. To overcome this, we propose a new hybrid machine learning approach in which we use a convolutional neural network to detect vortex regions within surface streamline plots and an additional deep neural...

.... We have developed in-house CNN architectures using the TensorFlow [ 65 ] package for implementing machine learning applications in python . These differ from publicly and commercially available available CAE and UNet architectures, since our implementation involves customized loss functions...

... control. In this paper, a new machine learning framework is developed to predict the mechanical behavior of fabricated metamaterials based on their as-built geometries (represented as high-resolution point clouds). Specifically, the point cloud is first converted into an image profile, which preserves...

...Salah A. Faroughi; Nikhil M. Pawar; Célio Fernandes; Maziar Raissi; Subasish Das; Nima K. Kalantari; Seyed Kourosh Mahjour Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines...

...@stonybrook.edu 1 Corresponding author. Email: anurag.purwar@stonybrook.edu 19 02 2023 01 11 2023 02 11 2023 30 11 2023 planar linkage mechanisms simulation machine learning deep learning object detection Directorate for Engineering 10.13039/100000084 1563413...

... 1 Corresponding author. Email: anurag.purwar@stonybrook.edu 11 02 2023 06 10 2023 06 10 2023 27 10 2023 planar four-bar linkage path synthesis Fourier descriptors wavelets variational autoencoder neural networks machine learning deep learning Division...

... machine learning deep learning manufacturing applications Labor shortage and high rate of labor churn are motivating companies to evaluate the feasibility of automation technologies in the manufacturing sector to maintain capacity and prevent delays. Migration to digital engineering and adoption...

... in mechanism design problems where the use of GANs have been explored. Physics-informed machine learning (PIML) approaches try to generate solutions for complex multi-scale systems by combining observational data with the mathematical models underpinning the physics of the system [ 51 , 52 ]. This field...

.... Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods. Pioneering work has demonstrated that ML provides solutions to governing systems of equations with comparable accuracy to those obtained using...

... polynomial-based features. These features are normalized and visualized using partial dependence plot (PDP) and individual conditional expectation (ICE). Subsequently, ten machine learning classifiers are trained using four features, and their statistical hypothesis test is performed using a 5 × 2 paired t...

...Dehao Liu; Pranav Pusarla; Yan Wang Data sparsity is still the main challenge to apply machine learning models to solve complex scientific and engineering problems. The root cause is the “curse of dimensionality” in training these models. Training algorithms need to explore and exploit in a very...

... Corresponding author. Email: yan.wang@me.gatech.edu Contributed by the Computers and Information Division of ASME for publication in the J ournal of C omputing and I nformation S cience in E ngineering . 01 04 2022 01 10 2022 03 10 2022 08 11 2022 machine learning...

... of International Science and Engineering 10.13039/100000089 2119334 uncertainty quantification robust optimal design stochastic kriging Gaussian process conditional value-at-risk computational foundations for engineering optimization data-driven engineering machine learning 20 01 2022...

... through machine learning. First, this study voxelized 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs...

...-ray computed tomography (XCT) images machine learning artificial intelligence data-driven engineering machine learning for engineering applications National Institute of Standards and Technology 10.13039/100000161 70NANB19H097 With years of development, additive manufacturing...

... considered in the evaluation process to establish the information association between EEG and performance levels. Moreover, intelligent psycho-physiological analysis that incorporates EEG into the fuzzy comprehensive evaluation (FCE) and machine learning methods is adopted within the proposed method...

... 2020 16 12 2020 17 12 2020 09 02 2021 deep learning inspection defect detection image processing machine learning computer-aided manufacturing automation artificial intelligence Detecting defects is a critical capability in manufacturing applications. Ensuring...

...Shrinath Deshpande; Anurag Purwar This paper brings together computer vision, mechanism synthesis, and machine learning to create an image-based variational path synthesis approach for linkage mechanisms. An image-based approach is particularly amenable to mechanism synthesis when the input from...