Physics-Informed Bayesian Learning of Electrohydrodynamic Polymer Jet Printing Dynamics

• URL: http://arxiv.org/abs/2204.09513v1
• Date: Sat, 16 Apr 2022 03:29:27 GMT
• Title: Physics-Informed Bayesian Learning of Electrohydrodynamic Polymer Jet Printing Dynamics
• Authors: Athanasios Oikonomou (1 and 4 and 7), Theodoros Loutas (1), Dixia Fan (2), Alysia Garmulewicz (3), George Nounesis (4), Santanu Chaudhuri (5 and 6) and Filippos Tourlomousis (4 and 7 and 8) ((1) Mechanical Engineering, University of Patras, Patras, Greece, (2) Westlake University, Hangzhou, China, (3) Faculty of Economics and Administration, University of Santiago, Chile, (4) National Centre for Scientific Research Demokritos, Agia Paraskevi, Attica, Greece, (5) Civil, Materials, and Environmental Engineering Department, University of Illinois at Chicago, IL, United States, (6) Argonne National Laboratory, Lemont, IL, United States, (7) Superlabs AMKE, Marousi, Attica, Greece, (8) Biological Lattice Industries Corp., Boston, MA, United States)
• Abstract summary: GPJet is an end-to-end physics-informed Bayesian learning framework. It can extract high-fidelity jet features in real-time from video data. It can act as closed-loop sensory feedback to the Machine Learning module of high- and low-fidelity data.
• Score: 2.9641522758725016
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Calibration of highly dynamic multi-physics manufacturing processes such as electro-hydrodynamics-based additive manufacturing (AM) technologies (E-jet printing) is still performed by labor-intensive trial-and-error practices. These practices have hindered the broad adoption of these technologies, demanding a new paradigm of self-calibrating E-jet printing machines. To address this need, we developed GPJet, an end-to-end physics-informed Bayesian learning framework, and tested it on a virtual E-jet printing machine with in-process jet monitoring capabilities. GPJet consists of three modules: a) the Machine Vision module, b) the Physics-Based Modeling Module, and c) the Machine Learning (ML) module. We demonstrate that the Machine Vision module can extract high-fidelity jet features in real-time from video data using an automated parallelized computer vision workflow. In addition, we show that the Machine Vision module, combined with the Physics-based modeling module, can act as closed-loop sensory feedback to the Machine Learning module of high- and low-fidelity data. Powered by our data-centric approach, we demonstrate that the online ML planner can actively learn the jet process dynamics using video and physics with minimum experimental cost. GPJet brings us one step closer to realizing the vision of intelligent AM machines that can efficiently search complex process-structure-property landscapes and create optimized material solutions for a wide range of applications at a fraction of the cost and speed.

Related papers

• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• SIMPACT: Simulation-Enabled Action Planning using Vision-Language Models [60.80050275581661]
  Vision-Language Models (VLMs) exhibit remarkable common-sense and semantic reasoning capabilities.<n>They lack a grounded understanding of physical dynamics.<n>We present S, a test-time, SIMulation-enabled ACTion Planning framework.<n>Our method demonstrates state-of-the-art performance on five challenging, real-world rigid-body and deformable manipulation tasks.
  arXiv  Detail & Related papers  (2025-12-05T18:51:03Z)
• An integrated process for design and control of lunar robotics using AI and simulation [0.48861336570452174]
  We envision an integrated process for developing lunar construction equipment, where physical design and control are explored in parallel.<n>We describe a technical framework that supports this process.<n>It relies on OpenPLX, a readable/writable declarative language that links CAD-models and autonomous systems to real-time 3D simulations of contacting multibody dynamics, machine regolith interaction forces, and non-ideal sensors.
  arXiv  Detail & Related papers  (2025-09-15T19:02:30Z)
• Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization [80.82828320306464]
  Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications.<n>PDD is an iterative, five-step process that consists of: i.e. deriving device behavior from design parameters, ii. simulating device performance, iv. fabricating the optimal device, and v. measuring device performance.<n>PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes.<n>In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD
  arXiv  Detail & Related papers  (2025-06-24T23:32:54Z)
• Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A minireview [0.0]
  Machine learning (ML)-driven approaches are revolutionizing materials discovery, property prediction, and material design. This review highlights real-world applications of automated ML-driven approaches in predicting mechanical, thermal, electrical, and optical properties of materials. Ultimately, the synergy between AI, automated experimentation, and computational modeling transforms the way the materials are discovered, optimized, and designed.
  arXiv  Detail & Related papers  (2025-03-22T15:24:38Z)
• Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation [90.00687889213991]
  Solving complex long-horizon robotic manipulation problems requires sophisticated high-level planning capabilities. Vision-language models (VLMs) pretrained on Internet data could in principle offer a framework for tackling such problems. In this paper, we introduce a novel test-time framework that enhancesVLMs' physical reasoning capabilities for multi-stage manipulation tasks.
  arXiv  Detail & Related papers  (2025-02-23T20:42:15Z)
• A Supervised Machine-Learning Approach For Turboshaft Engine Dynamic Modeling Under Real Flight Conditions [0.0]
  In this work, we explore different Neural Network architectures to model the turboshaft engine of Leonardo's AW189P4 prototype. The models are trained on an extensive database of real flight tests featuring a variety of operational maneuvers. To complement the neural network approach, we apply Sparse Identification of Dynamics (SINDy) to derive a low-dimensional dynamical model.
  arXiv  Detail & Related papers  (2025-02-19T21:45:53Z)
• Pre-Trained Video Generative Models as World Simulators [59.546627730477454]
  We propose Dynamic World Simulation (DWS) to transform pre-trained video generative models into controllable world simulators. To achieve precise alignment between conditioned actions and generated visual changes, we introduce a lightweight, universal action-conditioned module. Experiments demonstrate that DWS can be versatilely applied to both diffusion and autoregressive transformer models.
  arXiv  Detail & Related papers  (2025-02-10T14:49:09Z)
• Performance and Power: Systematic Evaluation of AI Workloads on Accelerators with CARAML [0.0]
  The CARAML benchmark suite is employed to assess performance and energy consumption during the training of large language models and computer vision models. CarAML provides a compact, automated, and reproducible framework for assessing the performance and energy of ML workloads.
  arXiv  Detail & Related papers  (2024-09-19T12:43:18Z)
• Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems [0.0]
  Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints. The basic premise of PIML is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in PIML for dynamical system modeling and control.
  arXiv  Detail & Related papers  (2023-06-24T05:24:48Z)
• A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps [2.6047112351202784]
  We present an active learning process based on multiobjective black-box optimization with continuously updated machine learning models. This workflow is built on open-source technologies for real-time data streaming and modular multiobjective optimization software development. We demonstrate a proof of concept for this workflow through the autonomous operation of a continuous-flow chemistry laboratory.
  arXiv  Detail & Related papers  (2023-04-15T01:34:16Z)
• Prospects of federated machine learning in fluid dynamics [0.0]
  In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science. In this letter, we present a federated machine learning approach that enables localized to collaboratively learn an aggregated shared predictive model. We demonstrate the feasibility and prospects of such decentralized learning approach with an effort to forge a deep learning surrogate model for reconstructingtemporal fields.
  arXiv  Detail & Related papers  (2022-08-15T06:15:04Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms [59.03426963238452]
  We present Real-time Neural MPC, a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model-predictive control pipeline. We show the feasibility of our framework on real-world problems by reducing the positional tracking error by up to 82% when compared to state-of-the-art MPC approaches without neural network dynamics.
  arXiv  Detail & Related papers  (2022-03-15T09:38:15Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Which priors matter? Benchmarking models for learning latent dynamics [70.88999063639146]
  Several methods have proposed to integrate priors from classical mechanics into machine learning models. We take a sober look at the current capabilities of these models. We find that the use of continuous and time-reversible dynamics benefits models of all classes.
  arXiv  Detail & Related papers  (2021-11-09T23:48:21Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Integrating Machine Learning with Physics-Based Modeling [17.392391163553334]
  This article focuses on one particular issue of broad interest: How can we integrate machine learning with physics-based modeling? We discuss the two most important issues for developing machine learning-based physical models: Imposing physical constraints and obtaining optimal datasets. We end with a general discussion on where this integration will lead us, and where the new frontier will be after machine learning is successfully integrated into scientific modeling.
  arXiv  Detail & Related papers  (2020-06-04T02:35:10Z)

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.