Online Two-stage Thermal History Prediction Method for Metal Additive Manufacturing of Thin Walls

• URL: http://arxiv.org/abs/2310.16125v2
• Date: Fri, 17 Nov 2023 19:46:15 GMT
• Title: Online Two-stage Thermal History Prediction Method for Metal Additive Manufacturing of Thin Walls
• Authors: Yifan Tang, M. Rahmani Dehaghani, Pouyan Sajadi, Shahriar Bakrani Balani, Akshay Dhalpe, Suraj Panicker, Di Wu, Eric Coatanea, G. Gary Wang
• Abstract summary: This paper proposes an online two-stage thermal history prediction method, which could be integrated into a metal AM process for performance control. Fifteen wire arc AM experiments and nine simulations are designed for thin walls with a fixed length and unidirectional printing of each layer. The proposed prediction method could construct the thermal history of a yet-to-print layer within 0.1 seconds on a low-cost desktop computer.
• Score: 3.8529213379135148
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper aims to propose an online two-stage thermal history prediction method, which could be integrated into a metal AM process for performance control. Based on the similarity of temperature curves (curve segments of a temperature profile of one point) between any two successive layers, the first stage of the proposed method designs a layer-to-layer prediction model to estimate the temperature curves of the yet-to-print layer from measured temperatures of certain points on the previously printed layer. With measured/predicted temperature profiles of several points on the same layer, the second stage proposes a reduced order model (ROM) (intra-layer prediction model) to decompose and construct the temperature profiles of all points on the same layer, which could be used to build the temperature field of the entire layer. The training of ROM is performed with an extreme learning machine (ELM) for computational efficiency. Fifteen wire arc AM experiments and nine simulations are designed for thin walls with a fixed length and unidirectional printing of each layer. The test results indicate that the proposed prediction method could construct the thermal history of a yet-to-print layer within 0.1 seconds on a low-cost desktop computer. Meanwhile, the method has acceptable generalization capability in most cases from lower layers to higher layers in the same simulation, as well as from one simulation to a new simulation on different AM process parameters. More importantly, after fine-tuning the proposed method with limited experimental data, the relative errors of all predicted temperature profiles on a new experiment are smaller than 0.09, which demonstrates the applicability and generalization of the proposed two-stage thermal history prediction method in online applications for metal AM.

Related papers

• A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface [0.0]
  This paper aims to investigate the efficacy of various Model Order Reduction (MOR) and deep learning techniques in predicting heat transfer in a pulsed jet impinging on a concave surface. To this end, this work introduces two predictive approaches, one employing a Fast Fourier Transformation augmented Artificial Neural Network (FFT-ANN) for predicting the average Nusselt number under constant-frequency scenarios. The POD-LSTM method proves to be a robust solution for predicting the local heat transfer rate under random-frequency impingement scenarios, capturing both the trend and value of temporal modes.
  arXiv  Detail & Related papers  (2024-02-16T12:41:31Z)
• Bayesian inference of composition-dependent phase diagrams [47.79947989845143]
  We develop a method in which Bayesian inference is employed to combine thermodynamic data from molecular dynamics (MD), melting point simulations, and phonon calculations, process these data, and yield a temperature-concentration phase diagram. The developed algorithm was successfully tested on two binary systems, Ge-Si and K-Na, in the full range of concentrations and temperatures.
  arXiv  Detail & Related papers  (2023-09-03T20:57:10Z)
• Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators [0.0]
  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.
  arXiv  Detail & Related papers  (2023-07-04T16:17:59Z)
• 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)
• Long Short-Term Memory Neural Network for Temperature Prediction in Laser Powder Bed Additive Manufacturing [0.0]
  We propose a novel means to predict the temperature gradient distributions during the printing process by making use of neural networks. The aim of this is to avoid extreme and inhomogeneous temperature distribution that may occur across the plate in the course of the printing process.
  arXiv  Detail & Related papers  (2023-01-30T14:06:14Z)
• Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate [0.0]
  This study compares different methods to predict the simultaneous effects of conductive and radiative heat transfer in a PMMA sample. Results demonstrate that this artificial intelligence method is accurate and fast in predicting problems.
  arXiv  Detail & Related papers  (2022-04-12T22:44:53Z)
• A deep learning method based on patchwise training for reconstructing temperature field [10.422905687540172]
  This work proposes a novel deep learning method based on patchwise training to reconstruct the temperature field of electronic equipment accurately from limited observation. The maximum absolute errors of the reconstructed temperature field are less than 1K under the patchwise training approach.
  arXiv  Detail & Related papers  (2022-01-26T10:42:23Z)
• Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration [57.568461777747515]
  We introduce a novel calibration method, Parametrized Temperature Scaling (PTS) We demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.
  arXiv  Detail & Related papers  (2021-02-24T10:18:30Z)
• Adiabatic Sensing Technique for Optimal Temperature Estimation using Trapped Ions [64.31011847952006]
  We propose an adiabatic method for optimal phonon temperature estimation using trapped ions. The relevant information of the phonon thermal distributions can be transferred to the collective spin-degree of freedom. We show that each of the thermal state probabilities is adiabatically mapped onto the respective collective spin-excitation configuration.
  arXiv  Detail & Related papers  (2020-12-16T12:58:08Z)
• Learning to predict metal deformations in hot-rolling processes [59.00006390882099]
  Hot-rolling is a metal forming process that produces a cross-section from an input through a sequence of deformations. In current practice, the rolling sequence and the geometry of their rolls are needed to achieve a given cross-section. We propose a supervised learning approach to predict a given by a set of rolls with given geometry.
  arXiv  Detail & Related papers  (2020-07-22T13:33:44Z)
• Real-time estimation of the optically detected magnetic resonance shift in diamond quantum thermometry [47.50219326456544]
  We investigate the real-time estimation protocols for the frequency shift of optically detected magnetic resonance (ODMR) of nitrogen-vacancy centers in nanodiamonds (NDs) Efficiently integrating multipoint ODMR measurements and ND particle tracking into fluorescence microscopy has recently demonstrated stable monitoring of the temperature inside living animals.
  arXiv  Detail & Related papers  (2020-06-12T01:44:35Z)

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.