15 documents found, page 1 of 2
Reducing mesh dependency in dataset generation for machine learning prediction ...
Mitreiro, Dário; Prates, Pedro A.; Andrade-Campos, AntónioGiven the extensive use of sheet metal-forming processes in the industry and the constant emergence of new materials, the accurate prediction of material constitutive models and their parameters is extremely important to enhance and optimise these processes. Machine learning techniques have proven to be highly promising for predicting these parameters using data obtained either experimentally or through numeric...
Identification of sheet metal constitutive parameters using metamodeling of the...
Parreira, Tomás G.; Marques, Armando E.; Sakharova, Nataliya A.; Prates, Pedro A.; Pereira, André F. G.An identification strategy based on a machine learning approach is proposed to identify the constitutive parameters of metal sheets. The main novelty lies in the use of Gaussian Process Regression with the objective of identifying the constitutive parameters of metal sheets from the biaxial tensile test results on a cruciform specimen. The metamodel is intended to identify the constitutive parameters of the wor...
Sensitivity analysis of the square cup forming process using PAWN and Sobol ind...
Parreira, Tomás G.; Rodrigues, Diogo C.; Oliveira, Marta C.; Sakharova, Nataliya A.; Prates, Pedro A.; Pereira, André F. G.This study investigates the sensitivity of the square cup forming process. It analyses how the uncertainties in the material properties, friction and process conditions affect the results of the square cup, such as equivalent plastic strain, geometry change, thickness reduction, punch force and springback. The cup flange and the die curvature region are identified as highly affected areas, while the cup bottom ...
Machine learning applications in sheet metal constitutive Modelling: a review
Marques, Armando E.; Parreira, Tomás G.; Pereira, André F. G.; Ribeiro, Bernardete M.; Prates, Pedro A.The numerical simulation of sheet metal forming processes depends on the accuracy of the constitutive model used to represent the mechanical behaviour of the materials. The formulation of these constitutive models, as well as their calibration process, has been an ongoing subject of research. In recent years, there has been a special focus on the application of data-driven techniques, namely Machine Learning, t...
SecFL – Secure Federated Learning Framework for predicting defects in sheet met...
Dib, Mario Alberto da Silveira; Prates, Pedro A.; Ribeiro, Bernardete M.With the ongoing digitization of the manufacturing industry and the ability to bring together data from specific manufacturing processes, there is enormous potential to use machine learning (ML) techniques to improve such processes. In this context, the competitive automotive industry can take advantage of the ML power by predicting defects before they occur, aiming to reduce the scrap rate and increase the rob...
Analysis of ESAFORM 2021 cup drawing benchmark of an Al alloy, critical factors...
Habraken, Anne Marie; Aksen, Toros Arda; Alves, José L.; Amaral, Rui L.; Betaieb, Ehssen; Chandola, Nitin; Corallo, Luca; Cruz, Daniel J.This article details the ESAFORM Benchmark 2021. The deep drawing cup of a 1 mm thick, AA 6016-T4 sheet with a strong cube texture was simulated by 11 teams relying on phenomenological or crystal plasticity approaches, using commercial or self-developed Finite Element (FE) codes, with solid, continuum or classical shell elements and different contact models. The material characterization (tensile tests, biaxial...
Analysis of ESAFORM 2021 cup drawing benchmark of an Al alloy, critical factors...
Habraken, Anne Marie; Aksen, Toros Arda; Alves, J. L.; Amaral, Rui L.; Betaieb, Ehssen; Chandola, Nitin; Corallo, Luca; Cruz, Daniel J.This article details the ESAFORM Benchmark 2021. The deep drawing cup of a 1 mm thick, AA 6016-T4 sheet with a strong cube texture was simulated by 11 teams relying on phenomenological or crystal plasticity approaches, using commercial or self-developed Finite Element (FE) codes, with solid, continuum or classical shell elements and different contact models. The material characterization (tensile tests, biaxial...
On the development of dataset supported strategies for the constitutive paramet...
Prates, Pedro A.; Pereira, André F. G.; Fernandes, José V.; Sakharova, NataliyaThis work presents an exploratory study for the development of a dataset-supported strategy to identify the plastic behaviour of metal sheets. Datasets were generated from numerical simulation results obtained from the biaxial tensile test on a cruciform-shaped sample, for 4000 hypothetical materials. These datasets were used to simultaneously estimate the yield criterion and hardening law parameters of referen...
On the identification of material constitutive model parameters using machine l...
Marques, Armando; Pereira, André; Ribeiro, Bernardete; Prates, Pedro A.This work aims to evaluate the predictive performance of various Machine Learning algorithms when applied to the prediction of material constitutive parameters, particularly the parameters of the Swift hardening law. For this, datasets were generated from the results of the numerical simulations of uniaxial tensile tests. The Machine Learning algorithms considered for this study are: Gaussian Process, Multi-lay...
Variance-Based Sensitivity Analysis of the Biaxial Test on a Cruciform Specimen
Pereira, André F.G.; Oliveira, Marta C.; Fernandes, José V.; Prates, Pedro A.Parameter identification is a key aspect in the modeling of the material’s mechanical behavior. The identification quality depends on the sensitivity of the test results to the values of the constitutive parameters. In this context, a variance-based sensitivity analysis is performed, in order to quantify the influence of the material parameters on the results of the biaxial test on a cruciform specimen; in part...