Developing a Digital Twin to Predict Sheet Metal Forming

This project focuses on the development of a digital twin for predicting sheet metal forming operation parameters. It advances towards integrating modeling and simulation, a MODSIM concept, for accurate predictions using real parameters. The developed digital twin was built using SolidWorks Simulation® platform. The plastic model of the material was implemented. The developed simulation model was evaluated using various materials and bending angles, demonstrating concordance between the simulation results and both experimental and empirical data. This validates the digital twin's capability to predict sheet metal bending manufacturing parameters, such as springback angle and press loading capacity, thereby improving manufacturing processes, optimizing designs, and addressing economic and sustainability considerations.

Authors

Institutions

Publication Details

Published
2025-04-07
DOI
https://doi.org/10.46254/af6.20250133
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Developing a Digital Twin to Predict Sheet Metal Forming

Hesham Elkanani
Manufacturing Process and Optimization
article

Developing a Digital Twin to Predict Sheet Metal Forming

Hesham Elkanani
article en

Abstract

This project focuses on the development of a digital twin for predicting sheet metal forming operation parameters. It advances towards integrating modeling and simulation, a MODSIM concept, for accurate predictions using real parameters. The developed digital twin was built using SolidWorks Simulation® platform. The plastic model of the material was implemented. The developed simulation model was evaluated using various materials and bending angles, demonstrating concordance between the simulation results and both experimental and empirical data. This validates the digital twin's capability to predict sheet metal bending manufacturing parameters, such as springback angle and press loading capacity, thereby improving manufacturing processes, optimizing designs, and addressing economic and sustainability considerations.

Al Yamamah University (SA)
Openalex Percentile: Top 77%
Manufacturing Process and Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.