Advanced Numerical and AI Strategies for Material Forming

Erscheinungsjahr 2026
ISBN: 978-3-0364-0986-3
Verlag: Trans Tech Publications

Buch, Englisch, 270 Seiten, Format (B × H): 170 mm x 240 mm, Gewicht: 600 g

This special edition presents research results related to the use of advanced and non-conventional methods for modelling and simulation of forming processes, including the application of machine learning and AI techniques. By that, developments in the field of numerical simulation were considered that could eventually be applied to the simulation of any forming process. The edition will be helpful for researchers in materials science and the development of materials processing technologies.

Autoren/Hrsg.

Klusemann, Benjamin

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde

Inhaltsverzeichnis

Preface
Numerical Investigation of Deposition Efficiency Influencing Factors in the Friction Surfacing Process
Prediction of Strip Width Deviation in Hot Strip Roughing Mills Based on Machine Learning Regression
Multi-Objective Bayesian Optimization of Dual-Phase Steel Microstructures for Minimal Damage Initiation
Algebraic Hierarchical Graph Neural Networks for Forming Simulation of Thermoplastic Composite Materials
Explainable Machine Learning and 3D Visualization for Rotary Tube Bending: Expert Evaluation of a Web-Based Tool
Investigation of Code Optimization Strategies for Enhancing the Performance of Static Recrystallization Cellular Automata Models
Parameter Optimization and Data-Driven Soft-Sensor Framework for Torque Prediction in Bobbin-Tool Friction Stir Welding of AA2024
Long Short-Term Memory to Predict the Quality Parameters in the Hot Rolling Process
AI-Based Predictions of Forming Effects for Enhanced Crash Simulation
Residual Learning-Based Synthetic Data for Hybrid Metamodeling in the Stamping of an Automotive Door Panel
Robust and Feature-Aware Arbritary Lagrangian-Eulerian Method for Material Forming Applications
On-Line Prediction of Dry Zones during Composite Process through Digital Shadow Approach
Graph Neural Network for Draw-in Prediction in Sheet Metal Stamping
A Bayesian Data Assimilation Framework for Characterizing Recrystallization in Steels
Predicting Lateral Flow in Hot Sheet Metal Rolling Using Symbolic Regression
Physics-Informed Recurrent Neural Networks with Kinematic Constraints for Large Deformation Metal Forming
An Ai-Based Approach to Developing a Microstructural Model for Multi-Stage Hot Deformation Processes
Accelerating Microstructural Evolution Simulations in DIGIMU® through a Front-Tracking Lagrangian Solver: Implementation and Validation in AISI 304L Stainless Steel
Study on the Apparent Friction Coefficient between a Deformable and a Rough Rigid Body under Large Sliding
Using Kolmogorov-Arnold Networks for Discrepancy Modeling of Lateral Flow in Hot Rolling of Steel Slabs
Modelling Deformation Mechanisms Decomposition, Separation and Compaction in Mechanical Joining Processes of Fiber Reinforced Thermoplastics on Meso Scale
Combined Experimental and Simulation-Based Approach for Optimizing Roll Forming of Pipes for Hydrogen Infrastructure
Integrated Artificial Neural Network-Based Approach for Predicting Surface Roughness Parameters in Laser Surface Texturing of Ti6Al4V