DeepForge: Leveraging AI for Microstructural Control in Metal Forming via Model Predictive Control

• URL: http://arxiv.org/abs/2402.16119v1
• Date: Sun, 25 Feb 2024 15:37:14 GMT
• Title: DeepForge: Leveraging AI for Microstructural Control in Metal Forming via Model Predictive Control
• Authors: Jan Petrik and Markus Bambach
• Abstract summary: This study presents a novel method for microstructure control in closed die hot forging. It combines Model PredictiveCMP with a machine called DeepForge.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents a novel method for microstructure control in closed die hot forging that combines Model Predictive Control (MPC) with a developed machine learning model called DeepForge. DeepForge uses an architecture that combines 1D convolutional neural networks and gated recurrent units. It uses surface temperature measurements of a workpiece as input to predict microstructure changes during forging. The paper also details DeepForge's architecture and the finite element simulation model used to generate the data set, using a three-stroke forging process. The results demonstrate DeepForge's ability to predict microstructure with a mean absolute error of 0.4$\pm$0.3%. In addition, the study explores the use of MPC to adjust inter-stroke wait times, effectively counteracting temperature disturbances to achieve a target grain size of less than 35 microns within a specific 2D region of the workpiece. These results are then verified experimentally, demonstrating a significant step towards improved control and quality in forging processes where temperature can be used as an additional degree of freedom in the process.

Related papers

• Enhancing Microgrid Performance Prediction with Attention-based Deep Learning Models [0.0]
  This research aims to address microgrid systems' operational challenges, characterized by power oscillations that contribute to grid instability. An integrated strategy is proposed, leveraging the strengths of convolutional and Gated Recurrent Unit (GRU) layers. The framework is anchored by a Multi-Layer Perceptron (MLP) model, which is tasked with comprehensive load forecasting.
  arXiv  Detail & Related papers  (2024-07-20T21:24:11Z)
• To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO [68.69840111477367]
  We present a principled framework for learning a small yet generalizable temperature prediction network (TempNet) to improve LFMs. Our experiments on LLMs and CLIP models demonstrate that TempNet greatly improves the performance of existing solutions or models.
  arXiv  Detail & Related papers  (2024-04-06T09:55:03Z)
• Deep convolutional encoder-decoder hierarchical neural networks for conjugate heat transfer surrogate modeling [0.0]
  Conjugate heat transfer (CHT) models are vital for the design of many engineering systems. High-fidelity CHT models are computationally intensive, which limits their use in applications such as design optimization. We develop a modular deep convolutional encoder-decoder hierarchical (DeepEDH) neural network, a novel deep-learning-based surrogate modeling methodology.
  arXiv  Detail & Related papers  (2023-11-24T21:45:11Z)
• Introducing a Deep Neural Network-based Model Predictive Control Framework for Rapid Controller Implementation [41.38091115195305]
  This work presents the experimental implementation of a deep neural network (DNN) based nonlinear MPC for Homogeneous Charge Compression Ignition (HCCI) combustion control. Using the acados software package to enable the real-time implementation of the MPC on an ARM Cortex A72, the optimization calculations are completed within 1.4 ms. The IMEP trajectory following of the developed controller was excellent, with a root-mean-square error of 0.133 bar, in addition to observing process constraints.
  arXiv  Detail & Related papers  (2023-10-12T15:03:50Z)
• 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)
• A Three-regime Model of Network Pruning [47.92525418773768]
  We use temperature-like and load-like parameters to model the impact of neural network (NN) training hyper parameters on pruning performance. A key empirical result we identify is a sharp transition phenomenon: depending on the value of a load-like parameter in the pruned model, increasing the value of a temperature-like parameter in the pre-pruned model may either enhance or impair subsequent pruning performance. Our model reveals that the dichotomous effect of high temperature is associated with transitions between distinct types of global structures in the post-pruned model.
  arXiv  Detail & Related papers  (2023-05-28T08:09:25Z)
• Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy [2.0445155106382797]
  Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions. CNN based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection.
  arXiv  Detail & Related papers  (2023-05-12T22:49:36Z)
• 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)
• Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression [56.83861738731913]
  Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices. Previous unstructured or structured weight pruning methods can hardly truly accelerate inference. We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
  arXiv  Detail & Related papers  (2021-06-15T17:22:59Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• 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)

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.