Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach

• URL: http://arxiv.org/abs/2411.13366v2
• Date: Thu, 21 Nov 2024 09:27:08 GMT
• Title: Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach
• Authors: Sasa Ilic, Abdulkerim Karaman, Johannes Pöppelbaum, Jan Niclas Reimann, Michael Marré, Andreas Schwung,
• Abstract summary: We first provide a thorough analysis of nosing processes and the influencing parameters. We then set-up a Finite Element Method simulation to better analyse the effects of varying process parameters. We present a novel modeling framework based on specifically designed graph neural networks as surrogate models.
• Score: 2.7763199324745966
• License:
• Abstract: This study presents a novel approach for predicting wall thickness changes in tubes during the nosing process. Specifically, we first provide a thorough analysis of nosing processes and the influencing parameters. We further set-up a Finite Element Method (FEM) simulation to better analyse the effects of varying process parameters. As however traditional FEM simulations, while accurate, are time-consuming and computationally intensive, which renders them inapplicable for real-time application, we present a novel modeling framework based on specifically designed graph neural networks as surrogate models. To this end, we extend the neural network architecture by directly incorporating information about the nosing process by adding different types of edges and their corresponding encoders to model object interactions. This augmentation enhances model accuracy and opens the possibility for employing precise surrogate models within closed-loop production processes. The proposed approach is evaluated using a new evaluation metric termed area between thickness curves (ABTC). The results demonstrate promising performance and highlight the potential of neural networks as surrogate models in predicting wall thickness changes during nosing forging processes.

Related papers

• 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)
• Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition [0.8796261172196743]
  We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems. As a demonstrative application, we pursue the modeling of cathodic electrophoretic deposition (EPD), commonly known as e-coating.
  arXiv  Detail & Related papers  (2024-01-16T14:58:21Z)
• Deep Learning for Fast Inference of Mechanistic Models' Parameters [0.28675177318965045]
  We propose using Deep Neural Networks (NN) for directly predicting parameters of mechanistic models given observations. We consider a training procedure that combines Neural Networks and mechanistic models. We find that, while Neural Network estimates are slightly improved by further fitting, these estimates are measurably better than the fitting procedure alone.
  arXiv  Detail & Related papers  (2023-12-05T22:16:54Z)
• Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks [63.596602299263935]
  We present a learning algorithm that uses bone-driven motion networks to predict the deformation of loose-fitting garment meshes at interactive rates. We show that our method outperforms state-of-the-art methods in terms of prediction accuracy of mesh deformations by about 20% in RMSE and 10% in Hausdorff distance and STED.
  arXiv  Detail & Related papers  (2022-05-03T07:54:39Z)
• A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements [3.65211252467094]
  We present a data-driven correlation to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios. A material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve leaflet. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material properties learned implicitly from the data and naturally embedded in the network parameters.
  arXiv  Detail & Related papers  (2022-04-01T04:56:41Z)
• Dynamically-Scaled Deep Canonical Correlation Analysis [77.34726150561087]
  Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model.
  arXiv  Detail & Related papers  (2022-03-23T12:52:49Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Combining data assimilation and machine learning to estimate parameters of a convective-scale model [0.0]
  Errors in the representation of clouds in convection-permitting numerical weather prediction models can be introduced by different sources. In this work, we look at the problem of parameter estimation through an artificial intelligence lens by training two types of artificial neural networks.
  arXiv  Detail & Related papers  (2021-09-07T09:17:29Z)
• Masking Neural Networks Using Reachability Graphs to Predict Process Events [0.0]
  Decay Replay Mining is a deep learning method that utilizes process model notations to predict the next event. This paper proposes an approach to further interlock the process model of Replay Mining with its neural network for next event prediction.
  arXiv  Detail & Related papers  (2021-08-01T09:06:55Z)
• Sparse Flows: Pruning Continuous-depth Models [107.98191032466544]
  We show that pruning improves generalization for neural ODEs in generative modeling. We also show that pruning finds minimal and efficient neural ODE representations with up to 98% less parameters compared to the original network, without loss of accuracy.
  arXiv  Detail & Related papers  (2021-06-24T01:40:17Z)
• Physics-constrained deep neural network method for estimating parameters in a redox flow battery [68.8204255655161]
  We present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium flow battery (VRFB) We show that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training.
  arXiv  Detail & Related papers  (2021-06-21T23:42:58Z)

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.