Optimization of austenitic and ferritic steels for deep drawing. Part 2: FEM analyses with damage development.

Deep drawing of sheet metal is a crucial industrial process due to its high productivity and low cost per unit produced. Stainless steels are ideal for this process, given their high deformability compared to other steels. Despite its apparent simplicity, understanding how the material deforms during deep drawing is essential to predict the final result. From this point of view, simulation via finite element modelling (FEM) represents a rapid and cost-effective alternative to experimental testing. When properly calibrated, FEM models allow for analysing stress and strain distribution, identifying areas at risk of failure, calculating final wall thickness, and optimizing die geometry. This research led to the development of a FEM model capable of simulating deep drawing under different operating conditions, steel types (AISI 304 and AISI 430) and lubrication. The model was calibrated and validated by comparing the numerical results with those obtained from a series of Erichsen tests. To ensure the accuracy of the true stress-strain curves, the steels were thoroughly characterized through tensile tests, Erichsen tests, and metallographic analyses. A specific method was also developed to represent the true stress-strain curve beyond necking, up to physical failure of the steel. The experiment was conducted according to the principles of DoE (Design of Experiments), combined with statistical analysis using the ANOVA technique.

Publication Details

Journal
Frattura ed Integrità Strutturale
Published
2025-10-01
Primary Topic
Diverse Scientific and Economic Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of austenitic and ferritic steels for deep drawing. Part 2: FEM analyses with damage development.

Frattura ed Integrità Strutturale
Diverse Scientific and Economic Studies
article

Optimization of austenitic and ferritic steels for deep drawing. Part 2: FEM analyses with damage development.

article en

Abstract

Deep drawing of sheet metal is a crucial industrial process due to its high productivity and low cost per unit produced. Stainless steels are ideal for this process, given their high deformability compared to other steels. Despite its apparent simplicity, understanding how the material deforms during deep drawing is essential to predict the final result. From this point of view, simulation via finite element modelling (FEM) represents a rapid and cost-effective alternative to experimental testing. When properly calibrated, FEM models allow for analysing stress and strain distribution, identifying areas at risk of failure, calculating final wall thickness, and optimizing die geometry. This research led to the development of a FEM model capable of simulating deep drawing under different operating conditions, steel types (AISI 304 and AISI 430) and lubrication. The model was calibrated and validated by comparing the numerical results with those obtained from a series of Erichsen tests. To ensure the accuracy of the true stress-strain curves, the steels were thoroughly characterized through tensile tests, Erichsen tests, and metallographic analyses. A specific method was also developed to represent the true stress-strain curve beyond necking, up to physical failure of the steel. The experiment was conducted according to the principles of DoE (Design of Experiments), combined with statistical analysis using the ANOVA technique.

Frattura ed Integrità Strutturale
Openalex Percentile: Top 31%
Diverse Scientific and Economic Studies
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.