Pedro Prates

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Professor Auxiliar

Universidade de Aveiro

Portugal

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Pedro Prates is an Auxiliar Professor at Universidade de Aveiro, Portugal. His research areas focus on machine learning applications in sheet metal forming, numerical studies on advanced materials, and the analysis of fatigue crack growth in aluminum alloys. Recent publications include studies on predictive modeling and the evaluation of constitutive parameters in sheet metal forming processes.

Recent Grants

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KYKLOS 4.0 - An Advanced Circular and Agile Manufacturing Ecosystem based on rapid reconfigurable manufacturing process and individualized consumer preferences

Open Date: 2020-01-01

Close Date: 2023-12-01

Grant: Close

TherMechChar-XSteels - Toward virtual forming and design: Thermomechanical characterization of high strength steels through full-field measurements and a single designed test

Open Date: 2019-05-01

Close Date: 2021-06-01

Grant: Close

EZ-SHEET - Easy-to-Use Strategy for the Accurate Material Description in Sheet Metal Forming

Open Date: 2018-07-01

Close Date: 2021-07-01

Grant: Close

RDFORMING - Robust Design of Sheet Metal Forming Processes to Reduce Productivity Losses

Open Date: 2018-06-01

Close Date: 2021-05-01

Grant: Close

ifDAMAGElse - Modelling and numerical simulation of damage in metallic sheets: anisotropic behaviour and tension-compression asymmetry coupled approach for formability prediction

Open Date: 2018-05-01

Close Date: 2021-06-01

Pedro Prates

Research Interests

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About

Recent Grants

Articles (18)

Predicting edge cracking in sheet metal forming: evaluating machine learning models and data transformations

Reducing Mesh Dependency in Dataset Generation for Machine Learning Prediction of Constitutive Parameters in Sheet Metal Forming

Numerical Study on Continuous-Bending-Under-Tension of 3rd Generation Steel

SecFL – Secure Federated Learning Framework for predicting defects in sheet metal forming under variability

Sensitivity Analysis of the Square Cup Forming Process Using PAWN and Sobol Indices

Machine learning applications in sheet metal constitutive Modelling: A review

Identification of Sheet Metal Constitutive Parameters Using Metamodeling of the Biaxial Tensile Test on a Cruciform Specimen

Effect of L‐T and S‐T orientations on fatigue crack growth in an aluminum 7050‐T7451 plate

Influence of specimen orientation on fatigue crack growth in 7050-T7451 and 2050-T8 aluminium alloys

Machine Learning for Predicting Fracture Strain in Sheet Metal Forming

Recent Advances and Applications of Machine Learning in Metal Forming Processes

Load sequence effects and cyclic deformation behaviour of 7075-T651 aluminium alloy

Fatigue crack propagation analysis in 2024-T351 aluminium alloy using nonlinear parameters

Notch fatigue analysis and crack initiation life estimation of maraging steel fabricated by laser beam powder bed fusion under multiaxial loading

Effect of input variables uncertainty in free tube hydroforming process

Numerical study of the square cup stamping process: a stochastic analysis

Mechanical design of ring tensile specimen via surrogate modelling for inverse material parameter identification

Federated Learning as a Privacy-Providing Machine Learning for Defect Predictions in Smart Manufacturing

Collaborators (13)

Ricardo Branco

Ali KHALFALLAH

Joel Alexandre da Silva de Jesus

Marta Oliveira

Nataliya A. Sakharova

André Filipe Gomes Pereira

Diogo Neto

Carlos Leitao

José Valdemar Fernandes

JOSE COSTA

Pablo López Crespo

Bernardete Ribeiro

Pedro Antunes

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