Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators

• URL: http://arxiv.org/abs/2307.01804v1
• Date: Tue, 4 Jul 2023 16:17:59 GMT
• Title: Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators
• Authors: Jiangce Chen, Wenzhuo Xu, Martha Baldwin, Bj\"orn Nijhuis, Ton van den Boogaard, Noelia Grande Guti\'errez, Sneha Prabha Narra, Christopher McComb
• Abstract summary: This paper presents a data-driven model that captures the local temperature evolution during the additive manufacturing process. It is tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process. The results demonstrate that the model achieves high fidelity as measured by $R2$ and maintains generalizability to geometries that were not included in the training process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, the complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. Additionally, many models report a low mean square error (MSE) across the entire domain (part). However, in each time step, most areas of the domain do not experience significant changes in temperature, except for the heat-affected zones near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated. This paper presents a data-driven model that uses Fourier Neural Operator to capture the local temperature evolution during the additive manufacturing process. In addition, the authors propose to evaluate the model using the $R^2$ metric, which provides a relative measure of the model's performance compared to using mean temperature as a prediction. The model was tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process, and the results demonstrate that the model achieves high fidelity as measured by $R^2$ and maintains generalizability to geometries that were not included in the training process.

Related papers

• Optimizing Temperature for Language Models with Multi-Sample Inference [47.14991144052361]
  This paper addresses the challenge of automatically identifying the (near)-optimal temperature for different large language models. We provide a comprehensive analysis of temperature's role in performance optimization, considering variations in model architectures, datasets, task types, model sizes, and predictive accuracy. We propose a novel entropy-based metric for automated temperature optimization, which consistently outperforms fixed-temperature baselines.
  arXiv  Detail & Related papers  (2025-02-07T19:35:25Z)
• Decrypting the temperature field in flow boiling with latent diffusion models [1.9190568044682759]
  This paper presents an innovative method using Latent Diffusion Models (LDMs) to generate temperature fields from phase indicator maps. By leveraging the BubbleML dataset from numerical simulations, the LDM phase field data translates into corresponding temperature distributions. The resulting model effectively reconstructs complex temperature fields at interfaces.
  arXiv  Detail & Related papers  (2025-01-27T21:18:05Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator [15.313871831214902]
  The PIML model or hybrid model presented here consists of a neural network which predicts reduced nodalizations given on-orbit thermal load conditions. We compare the computational performance and accuracy of the hybrid model to a data-driven neural net model, and a high-fidelity finite-difference model of a prototype Earth-orbiting small spacecraft. The PIML based active nodalization approach provides significantly better generalization than the neural net model and coarse mesh model, while reducing computing cost by up to 1.7x compared to the high-fidelity model.
  arXiv  Detail & Related papers  (2024-07-08T16:38:52Z)
• Long Horizon Temperature Scaling [90.03310732189543]
  Long Horizon Temperature Scaling (LHTS) is a novel approach for sampling from temperature-scaled joint distributions. We derive a temperature-dependent LHTS objective, and show that finetuning a model on a range of temperatures produces a single model capable of generation with a controllable long horizon temperature parameter.
  arXiv  Detail & Related papers  (2023-02-07T18:59:32Z)
• A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain [12.264200001067797]
  We propose a novel physics and data co-driven surrogate modeling method for temperature field prediction. Numerical results demonstrate that our method can significantly improve accuracy prediction on a smaller dataset.
  arXiv  Detail & Related papers  (2022-03-15T08:43:24Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry [70.10343492784465]
  It is necessary to expose to the process engineer, not solely the model predictions, but also their interpretability. Model-agnostic local interpretability solutions based on LIME have recently emerged to improve the original method. We present in this paper a novel approach, VAE-LIME, for local interpretability of data-driven models forecasting the temperature of the hot metal produced by a blast furnace.
  arXiv  Detail & Related papers  (2020-07-15T07:07:07Z)
• Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning [0.0]
  Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task. Overheating results in severe motor deterioration and is thus of high concern for the machine's control strategy and its design. Several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles.
  arXiv  Detail & Related papers  (2020-01-17T11:41:02Z)

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.