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Rapid, Low Cost, High-quality Component Qualification Using Multi-scale, Multi-physics Analytical Toolset for the Optimization of Metal Additive Manufacturing Process Parameters



TECHNOLOGY AREA(S): Air Platform, Materials/Processes

ACQUISITION PROGRAM: 4.0T - CTO, Chief Technology Office

OBJECTIVE: Develop an innovative multi-scale, multi-physics, analytical software toolset capable of optimizing critical metal laser powder bed additive manufacturing (AM) process parameters to enable rapid, low cost, high-quality component qualification.

DESCRIPTION: As metal additive manufacturing (AM) continues to progress, several Navy programs are looking to take advantage of the design freedom and as-needed production capability the technology has to offer. However, AM part quality is negatively impacted by process variability between AM methods, machines, materials, and build environments. These differences, combined with the cyclic nature of the AM process itself (heating and cooling / expanding and contracting,) often result in parts that do not meet design specifications. The primary method of addressing these issues has been to adjust process parameters through trial and error. However, this costly and time consuming approach may still not provide the best parameter combination. Simulations have been developed to try to predict the effects these influences will have on part quality, but they are limited in their abilities. In most cases these simulations consider the effects of only one of these influences and do not take into account the interactions of the others. These simulations also tend to focus on either a part’s micro or macro structure, which prevents them from being able to fully optimize process parameters for both.

In order to quickly and cost-effectively produce and qualify high-quality AM parts, an innovative prediction and optimization software toolset is sought. The software toolset will need to consider the thermal and mechanical aspects of the AM process and the variables introduced by the selected AM machine, material and build environment for both the micro and macro structure levels. Ideally, this toolset will take user defined and/or previously loaded input parameters for the selected AM machine (e.g. energy, scan speed, scan spacing, and layer height ranges as well as possible support strategies, scan patterns, and build environment conditions), fabrication material (e.g. particle size and shape, packing density, and conduction), and desired part qualities. From these inputs, the toolset will be able to provide the user with a list of machine settings necessary to achieve the desired part qualities such as: surface finish; dimensional tolerances; specified microstructure; necessary performance characteristics (e.g. strength and fatigue); and minimized distortion and porosity.

PHASE I: Demonstrate feasibility of an integrated analytical software toolset capable of predicting key part qualities and providing optimized machine process parameters to ensure a quality part (i.e. a part that has the desired surface finish and dimensional tolerances; minimum distortion, residual stress, and porosity; and the necessary microstructure to achieve the required mechanical and fatigue characteristics) by comparing predictions and a limited set of specimens using a single laser powder bed machine and single material (e.g. Ti64 or 17-4PH.)

PHASE II: Develop a prototype of the software toolset using the framework developed in Phase I to optimize process parameters to achieve desired part qualities, as well as provide a prediction of these features for a part produced using build parameters that have been optimized for production (i.e. minimal support structure, powder use, necessary post processing, etc.). Demonstrate and validate the prototype by comparing the optimized builds and predictions to baseline builds (i.e. using default process settings) and traditional build characteristics (part geometry, strength and fatigue properties) of desired Navy components from a number of Navy-selected laser powder bed machines and materials.

PHASE III DUAL USE APPLICATIONS: Fully develop the prototype toolset into a release version of the software to enable integration into Navy and Commercial AM software applications. Private Sector Commercial Potential: The design freedom and potential time and cost savings of additive manufacturing (AM) make it applicable to almost any industry. However, in most cases, industries do not have a good understanding of the AM build process. This leads to millions of dollars being wasted on inefficient attempts to address build problems and wasted material on unusable parts. The proposed prediction/optimization toolset would provide industry with an effective means of minimizing residual stress and distortion before the build process is even started and would reduce the need for highly trained operators.


  • Neugebauer F., Keller N., Ploshikhin V.; Feuerhahn F., & Köhler H., (2014). Multi Scale FEM Simulation for Distortion Calculation in Additive Manufacturing of Hardening Stainless Steel. International Workshop on Thermal Forming and Welding Distortion, Bremen, 09-10 April, 2014
  • Sun, J., Yang, Y., & Wang, D. (2013). Parametric optimization of selective laser melting for forming Ti6Al4Vsamples by Taguchi method. Optics & Laser Technology 49: 118-124. 23 January 2013.
  • Keller, N., Neugebauer, F., Xu, H., & Ploshikhin, V. Thermo-mechanical Simulation of Additive Layer Manufacturing of Titanium Aerospace structures.

KEYWORDS: Cost Reduction; multi-scale; Metal Additive Manufacturing; Process Optimization; Multi-physics; Part Quality

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