Lionel Fourment on: Optimization, AI and Inverse Analysis in Forming

Erscheinungsjahr 2026
ISBN: 978-3-0364-0989-4
Verlag: Trans Tech Publications

Buch, Englisch, 162 Seiten, Format (B × H): 170 mm x 240 mm, Gewicht: 420 g

The processing of test results, modelling, and optimisation of technological parameters for complex material forming processes require the development of numerical methods that ensure reliability and computational efficiency. Articles collected in this special edition are devoted to innovation in this field, especially the use of artificial intelligence technologies, which is of utmost importance today.

Autoren/Hrsg.

Andrade-Campos, António Gil

Fachgebiete

• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Preface
Advanced Control of Stretch-Reducing Mills Using Artificial Intelligence
Proxy-Physics-Informed and Transfer Learning Networks for Radial-Axial Ring Rolling (RARR) under Varying Data Availability
Material Parameter Identification Using Bayesian Data Assimilation and Biaxial Tensile Test
Innovation of the “Cut-Clamp-Play” Concept for Robust and Efficient Sheet Metal Material Characterization
Method of an Automated Tool Design of Punch-Bending Processes Using the Asset Administration Shell
Uncertainty Detection in Sheet Metal Bending Processes with Machine Learning
AI-Driven Design and Optimization of Bending Processes
Hybrid Numerical and Data-Driven Modelling for Defect Prediction in Screw Press Hot Bulk Forging of the En AW-6060 Part
Machine Learning Algorithms Applied to the Identification of CPB06 Yield Criterion Parameters
Geometry-Based Concept for Automatic Cut Planning and Flattening of Sheet Metal Components
Towards Single-Test Anisotropy Calibration: Sensitivity, Identifiability and Validation of YLD2000-2d
Towards DIC-Subset-Independent Machine Learning Models for Constitutive Parameter Identification in Sheet Metal Forming
Application of Reference-Free Strain Measurement to Assess the Deformation Level of Pre-Deformed Components
Improving FEMU Calibration from a Single Heterogeneous Test through a Data-Driven Approach: An Exploratory Study
Robust Performance Optimization Using Bayesian Optimization and Extreme Value Theory