Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams

• URL: http://arxiv.org/abs/2308.14861v1
• Date: Mon, 28 Aug 2023 19:31:53 GMT
• Title: Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams
• Authors: Lynn Cherif, Mutahar Safdar, Guy Lamouche, Priti Wanjara, Padma Paul, Gentry Wood, Max Zimmermann, Florian Hannesen, Yaoyao Fiona Zhao
• Abstract summary: This paper introduces research and puts into practice some leading deep learning models that can be adapted for the classification of melt pool image. It investigates two-stream networks comprising spatial and temporal streams, a recurrent spatial network and a factorized 3D convolutional neural network. The capacity of these models to generalize when exposed to perturbations in melt pool image data is examined using datatemporal techniques grounded in real-world process scenarios.
• Score: 1.83192584562129
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent applications of machine learning in metal additive manufacturing (MAM) have demonstrated significant potential in addressing critical barriers to the widespread adoption of MAM technology. Recent research in this field emphasizes the importance of utilizing melt pool signatures for real-time defect prediction. While high-quality melt pool image data holds the promise of enabling precise predictions, there has been limited exploration into the utilization of cutting-edge spatiotemporal models that can harness the inherent transient and sequential characteristics of the additive manufacturing process. This research introduces and puts into practice some of the leading deep spatiotemporal learning models that can be adapted for the classification of melt pool image streams originating from various materials, systems, and applications. Specifically, it investigates two-stream networks comprising spatial and temporal streams, a recurrent spatial network, and a factorized 3D convolutional neural network. The capacity of these models to generalize when exposed to perturbations in melt pool image data is examined using data perturbation techniques grounded in real-world process scenarios. The implemented architectures demonstrate the ability to capture the spatiotemporal features of melt pool image sequences. However, among these models, only the Kinetics400 pre-trained SlowFast network, categorized as a two-stream network, exhibits robust generalization capabilities in the presence of data perturbations.

Related papers

• Machine learning-enabled velocity model building with uncertainty quantification [0.41942958779358674]
  Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications. Traditional velocity model building methods are powerful but often struggle with the inherent complexities of the inverse problem. We propose a scalable methodology that integrates generative modeling, in the form of Diffusion networks, with physics-informed summary statistics.
  arXiv  Detail & Related papers  (2024-11-11T01:36:48Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers [40.80426609561942]
  Insufficient overlap between the melt pools produced during Laser Powder Bed Fusion (L-PBF) can lead to lack-of-fusion defects. We introduce a machine learning framework to correlate in-situ two-color thermal images observed via high-speed color imaging to the two-dimensional profile of the melt pool cross-section.
  arXiv  Detail & Related papers  (2024-04-26T20:55:39Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Pre-training via Denoising for Molecular Property Prediction [53.409242538744444]
  We describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium. Inspired by recent advances in noise regularization, our pre-training objective is based on denoising.
  arXiv  Detail & Related papers  (2022-05-31T22:28:34Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation [47.984368369734995]
  We introduce a novel recurrent encoding-decoding neural network architecture for event-based optical flow estimation. The network is end-to-end trained with self-supervised learning on the Multi-Vehicle Stereo Event Camera dataset. We have shown that it outperforms all the existing state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2021-09-10T13:37:37Z)
• Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS [1.6274397329511197]
  This paper presents a novel methodology for permeability prediction from micro-CT scans of geological rock samples. The training data set for CNNs dedicated to permeability prediction consists of permeability labels that are typically generated by classical lattice Boltzmann methods (LBM) We instead perform direct numerical simulation (DNS) by solving the stationary Stokes equation in an efficient and distributed-parallel manner.
  arXiv  Detail & Related papers  (2021-09-04T08:43:19Z)
• Connect the Dots: In Situ 4D Seismic Monitoring of CO$_2$ Storage with Spatio-temporal CNNs [16.596385405707977]
  4D seismic imaging has been widely used in CO$$ sequestration projects to monitor the fluid flow in the volumetric region that is not sampled by wells. We develop neural-network-based models that can produce high-fidelity interpolated images effectively and efficiently. Our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized optical flow.
  arXiv  Detail & Related papers  (2021-05-25T02:38:22Z)
• Finite volume method network for acceleration of unsteady computational fluid dynamics: non-reacting and reacting flows [0.0]
  A neural network model with a unique network architecture and physics-informed loss function was developed to accelerate CFD simulations. Under the reacting flow dataset, the computational speed of this network model was measured to be about 10 times faster than that of the CFD solver.
  arXiv  Detail & Related papers  (2021-05-07T15:33:49Z)
• On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification [63.716247731036745]
  echo state networks have attracted many stares through time, mainly due to the simplicity and computational efficiency of their learning algorithm. This work addresses this issue by conducting an explainability study of Echo State Networks when applied to learning tasks with time series, image and video data. Specifically, the study proposes three different techniques capable of eliciting understandable information about the knowledge grasped by these recurrent models.
  arXiv  Detail & Related papers  (2021-02-17T08:56:33Z)

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.