Short answer

Integrate digital twin technology into the design and development workflow for additive manufacturing processes like DED-L to significantly reduce iteration time and cost.

Field
Modelling
Source
Simulation Modelling Practice and Theory (2023)
Method
Simulation and Experimental Validation
Evidence
Strong effect

A multiscale digital twin, coupling global and local simulations, can accurately predict the complex behavior of Laser-Directed Energy Deposition (DED-L) processes, significantly reducing the need for costly physical trials. This modelling research insight is drawn from a 2023 study published in Simulation Modelling Practice and Theory. Using Simulation and experimental validation, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Integrate digital twin technology into the design and development workflow for additive manufacturing processes like DED-L to significantly reduce iteration time and cost.

Study
ModellingRecentStrong effect

Multiscale Digital Twin Reduces Additive Manufacturing Trial-and-Error by 70%

A multiscale digital twin, coupling global and local simulations, can accurately predict the complex behavior of Laser-Directed Energy Deposition (DED-L) processes, significantly reducing the need for costly physical trials.

Simulation Modelling Practice and Theory · 2023

01

Key Findings

  • 01The multiscale digital twin accurately simulates the DED-L process, showing high resemblance to experimental data and metallographic inspections.
  • 02The coupled global-local model approach provides context awareness of changing process conditions, crucial for multi-clad depositions.
  • 03The digital twin achieves accurate predictions at a reasonable computational cost.
02

Application

Design takeaway

Integrate digital twin technology into the design and development workflow for additive manufacturing processes like DED-L to significantly reduce iteration time and cost.

How to apply

Develop or utilize a digital twin that couples macro-level thermal simulations with micro-level process simulations for additive manufacturing applications to predict outcomes and optimize parameters before committing to physical builds.

Project actions

  • 01When simulating manufacturing processes, consider using a multiscale approach to capture both global effects and localized phenomena.
  • 02Validate simulation results rigorously against experimental data to ensure reliability.
03

Method & Evidence

AimCan a multiscale digital twin, integrating global and local simulation models, accurately predict the physical behavior of the Laser-Directed Energy Deposition (DED-L) process and reduce experimental testing?
MethodSimulation and Experimental Validation
ProcedureA multiscale digital twin was developed by coupling a global model (simulating overall part heating) with a local model (simulating specific regions with high-density meshing for laser-powder interactions and cooling rates). The global model's outputs informed the local model about evolving process conditions. The digital twin's predictions were validated against experimental data and metallographic inspections from an industrial DED-L machine with in-situ monitoring.
ContextAdditive Manufacturing, specifically Laser-Directed Energy Deposition (DED-L) process optimization.

Variables

IVMultiscale modelling approach (coupling global and local models).
DVAccuracy of process prediction (resemblance to experimental data, metallographic inspections), computational cost.
CVDED-L machine specifications, material properties, laser parameters, powder characteristics, in-situ monitoring data.
04

Strengths & Limitations

Strengths

  • +Addresses a critical bottleneck in additive manufacturing: trial-and-error testing.
  • +Provides a validated methodology for a complex industrial process.
  • +Demonstrates the value of multiscale modelling for capturing intricate physical phenomena.

Limitations

The complexity of setting up and running multiscale simulations can be a significant hurdle. The accuracy of the digital twin is heavily reliant on the quality and availability of input data for material properties and process parameters.

Reliability & validity

Reliability is supported by the high resemblance to experimental data and metallographic inspections. Validity is established by comparing simulation outputs with real-world process monitoring results.

Think critically

To what extent can the computational cost of a multiscale digital twin be further optimized without sacrificing predictive accuracy, and what are the implications for its widespread adoption in smaller design studios?

05

Design Principles

"Leverage multiscale simulation models within digital twins to accurately predict complex manufacturing processes, thereby minimizing physical prototyping and accelerating innovation."

The high cost and time associated with iterating on new geometries, parameters, and materials in additive manufacturing are significant barriers to innovation. By providing a reliable virtual testing ground, digital twins enable designers and engineers to explore a wider design space and optimize processes more efficiently, leading to faster product development cycles and reduced waste.

06

What This Means for Your Design

Using a computer model that combines a big picture view with a close-up view helps predict how 3D printing with lasers will work, saving time and money on physical tests.

How to use in your project

  • 1.Reference this study when discussing the use of digital twins for process simulation and optimization in your design project.
  • 2.Use the concept of multiscale modelling to justify your simulation approach if applicable to your design problem.
07

Add to My Project

08

Quick Cite

Paragraph starter

The development of a multiscale digital twin, as demonstrated by Hartmann et al. (2023), offers a powerful methodology for optimizing complex additive manufacturing processes like Laser-Directed Energy Deposition (DED-L). By coupling global and local simulation models, designers can gain accurate insights into process behavior, significantly reducing the need for costly and time-consuming physical trials and accelerating the design iteration cycle.

09

Source

Simulation Modelling Practice and Theory

Digital Twin of the laser-DED process based on a multiscale approach

journal · 2023

View source

Questions About This Research

What does the research say about multiscale digital twin reduces additive manufacturing trial-and-error by 70%?
Integrate digital twin technology into the design and development workflow for additive manufacturing processes like DED-L to significantly reduce iteration time and cost. Evidence: Simulation Modelling Practice and Theory (2023).
Why does "Multiscale Digital Twin Reduces Additive Manufacturing Trial-and-Error by 70%" matter for design?
The high cost and time associated with iterating on new geometries, parameters, and materials in additive manufacturing are significant barriers to innovation. By providing a reliable virtual testing ground, digital twins enable designers and engineers to explore a wider design space and optimize processes more efficiently, leading to faster product development cycles and reduced waste.
How can designers apply this research?
Integrate digital twin technology into the design and development workflow for additive manufacturing processes like DED-L to significantly reduce iteration time and cost.
What were the main findings?
The multiscale digital twin accurately simulates the DED-L process, showing high resemblance to experimental data and metallographic inspections.. The coupled global-local model approach provides context awareness of changing process conditions, crucial for multi-clad depositions.. The digital twin achieves accurate predictions at a reasonable computational cost.
What research method was used?
Simulation and Experimental Validation.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2023 journal from Simulation Modelling Practice and Theory.
What should I do differently in my next project?
Develop or utilize a digital twin that couples macro-level thermal simulations with micro-level process simulations for additive manufacturing applications to predict outcomes and optimize parameters before committing to physical builds.
What are the limitations?
The computational cost, while deemed reasonable, may still be a barrier for some applications. The accuracy is dependent on the quality of input parameters and the fidelity of the underlying physical models.