20240017326. RAPID MATERIAL DEVELOPMENT PROCESS FOR ADDITIVE MANUFACTURED MATERIALS simplified abstract (THE JOHNS HOPKINS UNIVERSITY)

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RAPID MATERIAL DEVELOPMENT PROCESS FOR ADDITIVE MANUFACTURED MATERIALS

Organization Name

THE JOHNS HOPKINS UNIVERSITY

Inventor(s)

Steven M. Storck of Catonsville MD (US)

Joseph J. Sopcisak of Derwood MD (US)

Christopher M. Peitsch of Perry Hall MD (US)

Salahudin M. Nimer of Fulton MD (US)

Zachary R Ulbig of Essex MD (US)

RAPID MATERIAL DEVELOPMENT PROCESS FOR ADDITIVE MANUFACTURED MATERIALS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240017326 titled 'RAPID MATERIAL DEVELOPMENT PROCESS FOR ADDITIVE MANUFACTURED MATERIALS

Simplified Explanation

The abstract of the patent application describes a rapid material development process for a powder bed fusion additive manufacturing (PBF AM) process. It involves using computational fluid dynamics (CFD) simulation to select a simulated parameter set, which is then used in a design of experiments (DOE) to create an orthogonal parameter space for predicting an ideal parameter set. This parameter space is used to generate reduced volume build samples with varying laser or electron beam parameters and/or feedstock chemistries. The samples are mechanically characterized using high throughput techniques and analyzed to determine an optimal parameter set for a 3D article or a validation sample. Machine learning techniques can also be used to optimize future parameter selection by modeling the relationship between input processing parameters and material characterization outputs.

  • The patent application describes a rapid material development process for PBF AM.
  • Computational fluid dynamics (CFD) simulation is used to select a simulated parameter set.
  • Design of experiments (DOE) is used to generate an orthogonal parameter space.
  • Reduced volume build samples are created with varying laser or electron beam parameters and/or feedstock chemistries.
  • High throughput techniques are used to mechanically characterize the samples.
  • The samples are analyzed to determine an optimal parameter set for a 3D article or a validation sample.
  • Machine learning techniques can be used to optimize future parameter selection.

Potential applications of this technology:

  • Rapid material development for powder bed fusion additive manufacturing processes.
  • Optimization of laser or electron beam parameters and feedstock chemistries for additive manufacturing.
  • Improved understanding of the effects of processing parameters on defects and microstructure.

Problems solved by this technology:

  • Time-consuming and costly trial-and-error approach to material development in additive manufacturing.
  • Lack of understanding of the effects of processing parameters on defects and microstructure.

Benefits of this technology:

  • Faster and more cost-effective material development process for additive manufacturing.
  • Improved control over processing parameters to minimize defects and optimize microstructure.
  • Increased understanding of the relationship between processing parameters and material characteristics.


Original Abstract Submitted

a rapid material development process for a powder bed fusion additive manufacturing (pbf am) process generally utilizes a computational fluid dynamics (cfd) simulation to facilitate selection of a simulated parameter set, which can then be used in a design of experiments (doe) to generate an orthogonal parameter space to predict an ideal parameter set. the orthogonal parameter space defined by the doe can then be used to generate a multitude of reduced volume build samples using pbf am with varying laser or electron beam parameters and/or feedstock chemistries. the reduced volume build samples are mechanically characterized using high throughput techniques and analyzed to provide an optimal parameter set for a 3d article or a validation sample, which provides an increased understanding of the parameters and their independent and confounding effects on defects and microstructure. additionally, machine learning techniques can be used to optimize for future parameter selection by modeling the relationship between input processing parameters and outputs of material characterization.

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