General Lab Information

Energy Systems Division

Additive Manufacturing

The Additive Manufacturing (AM) Strategy of Brookhaven National Laboratory is set to provide multi-length scale information on 3D printed components and statistically relevant ensembles that can flow into data analytics and AI to (i) correlate process parameter and structural defects with failure probability and (ii) develop new structures and materials. For this purpose, we rely on a network of experts and facilities: primarily our in-house Users’ Facilities, the National Synchrotron Light Source II (NSLS-II) and the Center for Functional Nanomaterials (CFN).

illustration of additive manufacturing opportunities

Brookhaven Lab’s AM works along two main thrusts:

THRUST 1: Structural Components
Understand material defects that can lead to catastrophic failures.

THRUST 2: Functional Components
Address materials, design and performance for high-precision applications.

Our goal is to provide a platform to support both industry and research to obtain the material information they need to advance knowledge, processes and products. Contact us to understand more about opportunities and modalities for collaboration.

Fundamental part of our mission is also to collaborate with Small Businesses under the DOE SBIR/STTR Program. We are committed to help Small Businesses to improve their processes and product portfolio for diverse applications. See DOE SBIR/STTR Program opportunities.

Capabilities

  • Inter-departmental and inter-laboratory collaborations that allows AM to reach out to a network of relevant expertise.
  • Provide a connection between reliability issues of real-world systems and basic material sciences.
  • Strong on-campus multi-variate material characterization expertise in collaboration with CFN and NSLS II.
  • Data analytics in collaboration with the Brookhaven Lab’s Computational Science Initiative (CSI).
diagram of additive manufaturing work relationships

Research Areas

The AM landscape of processes diversified by fundamental variables and outcomes supports a range of applications that could, by principal characteristics, be categorized as:

Customizable mass products with innovative design and produced in large quantities:

  • Generally, these products require long supplier guarantee.
  • Replacement of defective structures must be available on the shelf for some years.

Components fabricated with special materials, for which low wastes are necessary:

  • Expensive, rare, toxic, and hazardous materials.
  • Achieve innovative designs for lower cost, lower weight and better functionality.

Hybrid/composite materials with specific functionality:

  • Integrated objects, such as sensors and conductors, to reduce weight and costs.
  • Electronics and sensorics printed on different substrates.

Materials difficult to machine, design or having specific insertions:

  • Components for high-efficiency, such as fuel nozzles for better combustion.

To allow each application to become marketable, specific activities are needed along the product life cycle to guarantee the quality of powders, proof performance and reliability of components with diverse materials combination, proof functionality and surface roughness, and investigate interface physics of adhesion between different layers.

The workshop on “Industrial Additive Manufacturing on Metals and Ceramics” held at BNL on April 25, 2019 highlighted the following areas for need of more research:

  1. Repeatability of the printing processes, machine to machine and run to run.
  2. Understand microstructure and properties of the object after printing and after postprocessing for development of predictive tools and models.
  3. Use high throughput and statistical approach to create large datasets to be combined with AI for better understanding of material issues such as corrosion, segregation, phase development in alloys and impact of recycled versus new feedstock material.
  4. Combine scientific data at various stages of the investigation with data analytics and AI to guide investigation, define correlations and allow faster predictions.
  5. Develop and provide proper tools for in-situ investigation of material dynamics, such as the area surrounding the melting pool to understand how pressure waves, vaporization, and different cooling rates impact the resulting material properties.

We believe that addressing and solving these issues is a team business and it requires a network of experts working together. We also believe that the information flowing into the evaluation process should come from different stages of the AM life cycle and should consider multiple variables and dynamics.