Short answer

Utilize advanced simulation and real-time monitoring tools to optimize additive manufacturing processes for bulk metallic glasses, ensuring material integrity and desired performance characteristics.

Field: Modelling
Source: Journal of Manufacturing and Materials Processing (2026)
Method: Literature Review and Synthesis
Evidence: Strong effect

Computational modelling and real-time process optimization are crucial for enhancing dimensional accuracy, amorphous structure retention, and functional performance in additive manufacturing of bulk metallic glasses. This modelling research insight is drawn from a 2026 study published in Journal of Manufacturing and Materials Processing. Using Literature review and synthesis, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Utilize advanced simulation and real-time monitoring tools to optimize additive manufacturing processes for bulk metallic glasses, ensuring material integrity and desired performance characteristics.

Study

ModellingNew This WeekStrong effect

Optimizing Bulk Metallic Glass Additive Manufacturing through Process Simulation

Journal of Manufacturing and Materials Processing · 2026

Key Findings

01Selective laser melting, electron beam melting, and directed energy deposition are key AM techniques for BMGs.
02Computational modelling and real-time optimization enhance dimensional accuracy and amorphous structure.
03Challenges include crystallization, stress buildup, oxidation, and contamination.

Application

Design takeaway

Utilize advanced simulation and real-time monitoring tools to optimize additive manufacturing processes for bulk metallic glasses, ensuring material integrity and desired performance characteristics.

How to apply

Before committing to physical prototypes, use finite element analysis (FEA) or other simulation software to model the thermal profiles and stress development during the AM process for BMGs. Adjust printing parameters based on simulation results.

Project actions

01When designing for additive manufacturing of advanced materials, consider using simulation software to predict potential issues.
02Document the parameters used in simulations and how they relate to physical test results.

Method & Evidence

AimHow can computational and real-time process optimization strategies be leveraged to improve the dimensional accuracy, amorphous retention, and functional performance of bulk metallic glass parts produced via additive manufacturing?

MethodLiterature Review and Synthesis

ProcedureThe research critically evaluates recent developments in additive manufacturing techniques for bulk metallic glasses, focusing on advancements in process optimization strategies, including computational modelling and real-time adjustments. It analyzes how these methods address challenges such as crystallization, stress accumulation, and dimensional accuracy.

ContextAdditive Manufacturing of Advanced Materials

Variables

IV["Additive manufacturing process parameters (e.g., laser power, scan speed, layer thickness)","Computational modelling strategies"]

DV["Dimensional accuracy","Amorphous structure retention","Microstructural properties","Functional performance","Stress buildup"]

CV["Material composition of the bulk metallic glass","Specific additive manufacturing technique used","Environmental conditions (e.g., atmosphere)"]

Strengths & Limitations

Strengths

+Comprehensive review of current AM techniques for BMGs.
+Emphasis on the role of process optimization and modelling.

Limitations

Access to sophisticated simulation software and accurate material property data can be a barrier.

Reliability & validity

The reliability and validity of the findings depend on the quality and breadth of the reviewed literature. The review synthesizes findings from multiple studies, which can enhance generalizability, but direct experimental validation of all modelled outcomes is not performed within the review itself.

Think critically

To what extent can current computational models accurately predict the complex microstructural evolution and defect formation in bulk metallic glasses during additive manufacturing, and what are the implications for design iteration speed?

Design Principles

"Predictive modelling and adaptive control are essential for complex material additive manufacturing."

The unique properties of bulk metallic glasses (BMGs) make them valuable for advanced applications. However, achieving these properties through additive manufacturing (AM) is challenging due to issues like crystallization and stress buildup. Modelling allows designers and engineers to predict and mitigate these issues before physical prototyping, leading to more efficient development and higher quality parts.

What This Means for Your Design

Using computer simulations before 3D printing with special metals (bulk metallic glasses) helps predict and fix problems like cracking or changing the material's structure, leading to better final products.

How to use in your project

1.Reference the use of simulation software to predict material behaviour or process outcomes in your design project's development section.
2.Discuss how modelling helped refine design choices or manufacturing strategies.

Add to My Project

Quick Cite

Paragraph starter

The additive manufacturing of bulk metallic glasses presents unique challenges, necessitating the use of advanced modelling techniques. Computational simulations, such as finite element analysis, can predict thermal gradients and stress accumulation during the printing process, allowing for proactive adjustments to parameters like laser power, scan speed, and layer thickness. This predictive capability is crucial for maintaining the amorphous structure, preventing crystallization, and ensuring dimensional accuracy, ultimately leading to higher quality and more reliable components for demanding applications.

Source

Journal of Manufacturing and Materials Processing

Progresses and Challenges in Additive Manufacturing of Bulk Metallic Glasses

journal · 2026

View source

Questions About This Research

What does the research say about optimizing bulk metallic glass additive manufacturing through process simulation?: Utilize advanced simulation and real-time monitoring tools to optimize additive manufacturing processes for bulk metallic glasses, ensuring material integrity and desired performance characteristics. Evidence: Journal of Manufacturing and Materials Processing (2026).
Why does "Optimizing Bulk Metallic Glass Additive Manufacturing through Process Simulation" matter for design?: The unique properties of bulk metallic glasses (BMGs) make them valuable for advanced applications. However, achieving these properties through additive manufacturing (AM) is challenging due to issues like crystallization and stress buildup. Modelling allows designers and engineers to predict and mitigate these issues before physical prototyping, leading to more efficient development and higher quality parts.
How can designers apply this research?: Utilize advanced simulation and real-time monitoring tools to optimize additive manufacturing processes for bulk metallic glasses, ensuring material integrity and desired performance characteristics.
What were the main findings?: Selective laser melting, electron beam melting, and directed energy deposition are key AM techniques for BMGs.. Computational modelling and real-time optimization enhance dimensional accuracy and amorphous structure.. Challenges include crystallization, stress buildup, oxidation, and contamination.
What research method was used?: Literature Review and Synthesis.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2026 journal from Journal of Manufacturing and Materials Processing.
What should I do differently in my next project?: Before committing to physical prototypes, use finite element analysis (FEA) or other simulation software to model the thermal profiles and stress development during the AM process for BMGs. Adjust printing parameters based on simulation results.
What are the limitations?: The effectiveness of modelling is dependent on the accuracy of material property data and the complexity of the simulation.