Short answer

Incorporate advanced damage models and anisotropic material properties into forming simulations to improve the prediction of fracture and ensure dimensional accuracy of components made from challenging alloys like alloy 718.

Field: Final Production
Source: International Journal of Material Forming (2019)
Method: Numerical simulation and experimental validation
Evidence: Strong effect

Coupling advanced damage models with anisotropic yield criteria in finite element simulations accurately predicts fracture in alloy 718 during sheet metal forming. This final production research insight is drawn from a 2019 study published in International Journal of Material Forming. Using Numerical simulation and experimental validation, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Incorporate advanced damage models and anisotropic material properties into forming simulations to improve the prediction of fracture and ensure dimensional accuracy of components made from challenging alloys like alloy 718.

Study

Final ProductionHigh ImpactStrong effect

Advanced damage modelling enhances alloy 718 sheet metal formability prediction

Coupling advanced damage models with anisotropic yield criteria in finite element simulations accurately predicts fracture in alloy 718 during sheet metal forming.

International Journal of Material Forming · 2019

Key Findings

01Numerical simulations accurately predicted fracture locations in drawbead regions, aligning with experimental observations.
02The combination of GISSMO and an anisotropic material model (Barlat Yld2000-2D) showed potential for accurate forming simulations of alloy 718.
03DIC measurements are effective for calibrating GISSMO parameters.

Application

Design takeaway

How to apply

When designing forming processes for high-strength alloys, utilize finite element analysis software that supports advanced damage models and anisotropic material characterization. Calibrate these models using experimental data, such as DIC, to validate simulation results against real-world forming behaviour.

Project actions

01When simulating metal forming, consider using more advanced material models that account for damage and anisotropy.
02If possible, use experimental data like strain measurements from Digital Image Correlation to calibrate your simulation models.

Method & Evidence

AimTo investigate the effectiveness of coupling the GISSMO damage model with anisotropic yield criteria (Barlat Yld2000-2D) for predicting fracture and shape deviations in alloy 718 sheet metal forming simulations.

MethodNumerical simulation and experimental validation

ProcedureFinite element simulations using LS-DYNA were performed, integrating the GISSMO damage model with von Mises and Barlat Yld2000-2D yield criteria. Material failure parameters for GISSMO were calibrated using Digital Image Correlation (DIC) measurements from various specimen geometries. Damage distribution was experimentally validated using a non-linear acoustic technique.

ContextSheet metal forming of nickel-based superalloys for aerospace applications.

Variables

IVDamage model (GISSMO), Yield criteria (von Mises, Barlat Yld2000-2D)

DVPredicted effective plastic strains, Predicted shape deviations, Fracture location

CVMaterial (alloy 718), Forming temperature (room temperature), Simulation software (LS-DYNA), Specimen geometries used for calibration

Strengths & Limitations

Strengths

+Direct experimental validation of simulation results.
+Use of advanced techniques like DIC for model calibration.

Limitations

The accuracy of simulations depends heavily on the quality of material data and the chosen model. Experimental validation is time-consuming and can be costly.

Reliability & validity

Reliability is supported by the use of established simulation software and validated experimental techniques. Validity is strengthened by direct comparison of simulation predictions with experimental observations of fracture location and damage distribution.

Think critically

How might the choice of yield criterion (e.g., von Mises vs. Barlat Yld2000-2D) impact the accuracy of damage prediction in different forming scenarios?

Design Principles

"Accurate material failure prediction in forming simulations is essential for achieving desired component geometry and preventing production defects."

Accurate prediction of material failure during forming processes is crucial for manufacturing high-performance components, especially with advanced alloys like alloy 718. This research demonstrates a method to improve simulation accuracy, reducing costly trial-and-error in production and ensuring final product tolerances.

What This Means for Your Design

This study shows that using better computer models for how materials break during metal shaping can help predict exactly where parts will fail, making manufacturing more reliable.

How to use in your project

1.Reference this study when discussing the limitations of traditional forming limit diagrams and the benefits of advanced simulation techniques for predicting material failure in your design project.

Add to My Project

Quick Cite

Paragraph starter

This research highlights the critical need for advanced material modelling in sheet metal forming simulations. By coupling the GISSMO damage model with anisotropic yield criteria, the study successfully predicted fracture in alloy 718, outperforming traditional methods. This underscores the importance of using sophisticated simulation tools to accurately anticipate material failure and ensure the dimensional integrity of manufactured components, particularly for high-performance applications.

Source

International Journal of Material Forming

Damage and fracture during sheet-metal forming of alloy 718

journal · 2019

View source

Questions About This Research

What does the research say about advanced damage modelling enhances alloy 718 sheet metal formability prediction?: Incorporate advanced damage models and anisotropic material properties into forming simulations to improve the prediction of fracture and ensure dimensional accuracy of components made from challenging alloys like alloy 718. Evidence: International Journal of Material Forming (2019).
Why does "Advanced damage modelling enhances alloy 718 sheet metal formability prediction" matter for design?: Accurate prediction of material failure during forming processes is crucial for manufacturing high-performance components, especially with advanced alloys like alloy 718. This research demonstrates a method to improve simulation accuracy, reducing costly trial-and-error in production and ensuring final product tolerances.
How can designers apply this research?: Incorporate advanced damage models and anisotropic material properties into forming simulations to improve the prediction of fracture and ensure dimensional accuracy of components made from challenging alloys like alloy 718.
What were the main findings?: Numerical simulations accurately predicted fracture locations in drawbead regions, aligning with experimental observations.. The combination of GISSMO and an anisotropic material model (Barlat Yld2000-2D) showed potential for accurate forming simulations of alloy 718.. DIC measurements are effective for calibrating GISSMO parameters.
What research method was used?: Numerical simulation and experimental validation.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2019 journal from International Journal of Material Forming.
What should I do differently in my next project?: When designing forming processes for high-strength alloys, utilize finite element analysis software that supports advanced damage models and anisotropic material characterization. Calibrate these models using experimental data, such as DIC, to validate simulation results against real-world forming behaviour.
What are the limitations?: The study focused on room temperature forming; behaviour at elevated temperatures may differ. Calibration was specific to alloy 718, and other superalloys might require different parameters.