Learning to predict metal deformations in hot-rolling processes
- URL: http://arxiv.org/abs/2007.14471v1
- Date: Wed, 22 Jul 2020 13:33:44 GMT
- Title: Learning to predict metal deformations in hot-rolling processes
- Authors: R. Omar Chavez-Garcia, Emian Furger, Samuele Kronauer, Christian
Brianza, Marco Scarf\`o, Luca Diviani and Alessandro Giusti
- Abstract summary: 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.
- Score: 59.00006390882099
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Hot-rolling is a metal forming process that produces a workpiece with a
desired target cross-section from an input workpiece through a sequence of
plastic deformations; each deformation is generated by a stand composed of
opposing rolls with a specific geometry. In current practice, the rolling
sequence (i.e., the sequence of stands and the geometry of their rolls) needed
to achieve a given final cross-section is designed by experts based on previous
experience, and iteratively refined in a costly trial-and-error process. Finite
Element Method simulations are increasingly adopted to make this process more
efficient and to test potential rolling sequences, achieving good accuracy at
the cost of long simulation times, limiting the practical use of the approach.
We propose a supervised learning approach to predict the deformation of a given
workpiece by a set of rolls with a given geometry; the model is trained on a
large dataset of procedurally-generated FEM simulations, which we publish as
supplementary material. The resulting predictor is four orders of magnitude
faster than simulations, and yields an average Jaccard Similarity Index of
0.972 (against ground truth from simulations) and 0.925 (against real-world
measured deformations); we additionally report preliminary results on using the
predictor for automatic planning of rolling sequences.
Related papers
- von Mises Quasi-Processes for Bayesian Circular Regression [57.88921637944379]
We explore a family of expressive and interpretable distributions over circle-valued random functions.
The resulting probability model has connections with continuous spin models in statistical physics.
For posterior inference, we introduce a new Stratonovich-like augmentation that lends itself to fast Markov Chain Monte Carlo sampling.
arXiv Detail & Related papers (2024-06-19T01:57:21Z) - Fast, accurate training and sampling of Restricted Boltzmann Machines [4.785158987724452]
We present an innovative method in which the principal directions of the dataset are integrated into a low-rank RBM.
This approach enables efficient sampling of the equilibrium measure via a static Monte Carlo process.
Our results show that this strategy successfully trains RBMs to capture the full diversity of data in datasets where previous methods fail.
arXiv Detail & Related papers (2024-05-24T09:23:43Z) - Likelihood-based inference and forecasting for trawl processes: a
stochastic optimization approach [0.0]
We develop the first likelihood-based methodology for the inference of real-valued trawl processes.
We introduce novel deterministic and probabilistic forecasting methods.
We release a Python library which can be used to fit a large class of trawl processes.
arXiv Detail & Related papers (2023-08-30T15:37:48Z) - Investigation of reinforcement learning for shape optimization of
profile extrusion dies [1.5293427903448022]
Reinforcement Learning (RL) is a learning-based optimization algorithm.
RL is based on trial-and-error interactions of an agent with an environment.
We investigate this approach by applying it to two 2D test cases.
arXiv Detail & Related papers (2022-12-23T08:53:09Z) - 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) - Probabilistic Registration for Gaussian Process 3D shape modelling in
the presence of extensive missing data [63.8376359764052]
We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data.
Experiments are conducted both for a 2D small dataset with diverse transformations and a 3D dataset of ears.
arXiv Detail & Related papers (2022-03-26T16:48:27Z) - Surrogate Modelling for Injection Molding Processes using Machine
Learning [0.23090185577016442]
Injection molding is one of the most popular manufacturing methods for the modeling of complex plastic objects.
We propose a baseline for a data processing pipeline that includes the extraction of data from Moldflow simulation projects.
We evaluate machine learning models for fill time and deflection distribution prediction and provide baseline values of MSE and RMSE metrics.
arXiv Detail & Related papers (2021-07-30T12:13:52Z) - Scalable nonparametric Bayesian learning for heterogeneous and dynamic
velocity fields [8.744017403796406]
We develop a model for learning heterogeneous and dynamic patterns of velocity field data.
We show the effectiveness of our techniques to the NGSIM dataset of complex multi-vehicle interactions.
arXiv Detail & Related papers (2021-02-15T17:45:46Z) - Machine learning for rapid discovery of laminar flow channel wall
modifications that enhance heat transfer [56.34005280792013]
We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number.
We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations.
arXiv Detail & Related papers (2021-01-19T16:14:02Z) - Real-Time Regression with Dividing Local Gaussian Processes [62.01822866877782]
Local Gaussian processes are a novel, computationally efficient modeling approach based on Gaussian process regression.
Due to an iterative, data-driven division of the input space, they achieve a sublinear computational complexity in the total number of training points in practice.
A numerical evaluation on real-world data sets shows their advantages over other state-of-the-art methods in terms of accuracy as well as prediction and update speed.
arXiv Detail & Related papers (2020-06-16T18:43:31Z)
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.