Short answer

Integrate analytical modelling techniques into the design workflow to predict and manage residual stresses in additively manufactured metal parts, thereby improving component reliability and reducing development time.

Field: Modelling
Source: Preprints.org (2020)
Method: Analytical modelling and computational simulation.
Evidence: Strong effect

A novel physics-based thermomechanical analytical model can rapidly and accurately predict residual stress in metal additive manufacturing, crucial for mission-critical aerospace and automotive applications. This modelling research insight is drawn from a 2020 study published in Preprints.org. Using Analytical modelling and computational simulation., researchers explored how this design variable affects real-world outcomes. The key design takeaway: Integrate analytical modelling techniques into the design workflow to predict and manage residual stresses in additively manufactured metal parts, thereby improving component reliability and reducing development time.

Study

ModellingHigh ImpactStrong effect

Physics-Based Analytical Model Accurately Predicts Residual Stress in Metal Additive Manufacturing

Preprints.org · 2020

Key Findings

01A physics-based analytical model can accurately predict residual stress distributions (scan-direction and build-direction) in LPBF parts.
02The model accounts for critical process parameters like scan strategies and material properties.
03The developed model offers a faster alternative to traditional experimental and numerical methods for residual stress prediction.

Application

Design takeaway

How to apply

Utilize the principles of this analytical approach to develop or adapt models for specific materials and additive manufacturing processes to predict residual stresses, guiding process parameter selection and design modifications.

Project actions

01When researching additive manufacturing, consider how internal stresses can affect part performance.
02Explore simplified analytical models as a starting point for predicting material behavior under complex manufacturing conditions.

Method & Evidence

AimTo develop and validate a rapid, physics-based analytical model for predicting residual stress in laser powder bed fusion (LPBF) of metals.

MethodAnalytical modelling and computational simulation.

ProcedureA moving point heat source approach was used to model the temperature field, considering scan strategies, heat loss, and phase transformations. Thermal stresses were calculated using Green's functions for point body loads. The Johnson-Cook flow stress model was employed to predict yield surfaces under cyclic heating and cooling. Incremental plasticity and kinematic hardening were used to predict residual stress build-up, coupled with equilibrium and compatibility conditions. The model was validated against experimental data for maraging steel 350.

ContextAdditive Manufacturing (Laser Powder Bed Fusion) for aerospace and automotive components.

Variables

IV["Scan strategies","Heat loss parameters","Energy for phase transformation","Process parameters (e.g., laser power, scan speed)"]

DV["Temperature field","Thermal stress","Yield surface","Residual stress distribution"]

CV["Material properties (e.g., thermal conductivity, specific heat, Young's modulus, Johnson-Cook parameters)","Geometry of the part","Heat source model (moving point heat source)"]

Strengths & Limitations

Strengths

+Provides a rapid prediction method compared to FEA.
+Based on fundamental physics principles.
+Validated against experimental data.

Limitations

The complexity of real-world manufacturing processes means that analytical models are simplifications. Factors like powder quality, ambient conditions, and machine variability are often not fully captured.

Reliability & validity

The study's reliability is supported by its use of established physical models (moving heat source, Johnson-Cook model) and its validity is demonstrated through comparison with experimental measurements of residual stress. However, the specific implementation and assumptions within the analytical framework will influence its generalizability.

Think critically

How might the accuracy of this analytical model be affected by variations in material properties or inconsistencies in the additive manufacturing process itself?

Design Principles

"Predictive modelling of internal stresses is essential for ensuring the structural integrity of components manufactured through additive processes."

Residual stresses in additively manufactured metal parts can compromise their structural integrity and performance. Developing efficient predictive models allows designers and engineers to anticipate and mitigate these stresses early in the design process, leading to more reliable and higher-quality components without the extensive time and cost associated with purely experimental or complex finite element simulations.

What This Means for Your Design

This study shows how to create a computer model that can quickly guess how much stress will build up inside metal parts made with 3D printing, which is important for making sure these parts don't break.

How to use in your project

1.Reference this study when discussing the importance of residual stress analysis in your design project.
2.Use the findings to justify the need for predictive modelling in your chosen manufacturing process.

Add to My Project

Quick Cite

Paragraph starter

The research by Mirkoohi et al. (2020) highlights the critical need for accurate and rapid prediction of residual stresses in additive manufacturing. Their development of a physics-based analytical model for laser powder bed fusion demonstrates that it is possible to foresee these internal stresses, which can significantly impact component integrity, particularly in high-stakes industries like aerospace and automotive. This work provides a valuable framework for designers to anticipate and mitigate potential material failures by integrating predictive modelling into their design and manufacturing strategies.

Source

Preprints.org

Analytical Modeling of Residual Stress in Laser Powder Bed Fusion

journal · 2020

View source

Questions About This Research

What does the research say about physics-based analytical model accurately predicts residual stress in metal additive manufacturing?: Integrate analytical modelling techniques into the design workflow to predict and manage residual stresses in additively manufactured metal parts, thereby improving component reliability and reducing development time. Evidence: Preprints.org (2020).
Why does "Physics-Based Analytical Model Accurately Predicts Residual Stress in Metal Additive Manufacturing" matter for design?: Residual stresses in additively manufactured metal parts can compromise their structural integrity and performance. Developing efficient predictive models allows designers and engineers to anticipate and mitigate these stresses early in the design process, leading to more reliable and higher-quality components without the extensive time and cost associated with purely experimental or complex finite element simulations.
How can designers apply this research?: Integrate analytical modelling techniques into the design workflow to predict and manage residual stresses in additively manufactured metal parts, thereby improving component reliability and reducing development time.
What were the main findings?: A physics-based analytical model can accurately predict residual stress distributions (scan-direction and build-direction) in LPBF parts.. The model accounts for critical process parameters like scan strategies and material properties.. The developed model offers a faster alternative to traditional experimental and numerical methods for residual stress prediction.
What research method was used?: Analytical modelling and computational simulation..
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2020 journal from Preprints.org.
What should I do differently in my next project?: Utilize the principles of this analytical approach to develop or adapt models for specific materials and additive manufacturing processes to predict residual stresses, guiding process parameter selection and design modifications.
What are the limitations?: The model's accuracy is dependent on the precise material properties and the fidelity of the heat source model. Validation was performed on a specific material (maraging steel 350) and may require adaptation for other alloys.