5514.001 Data Driven Qualification for Additive Manufacturing

Foundation set to reduce data burden and qualification timeline for new processes, materials

Baseline qualification framework including feedstock characterization, process in-situ monitoring, defect formation, and effect-of-defects.


A major limiting factor for the adoption of additively manufactured (AM) parts into structural applications is the challenge of qualification. The range of equipment suppliers that use their own proprietary feedstock formulations makes each AM system unique and thus subject to its own qualification protocol—even the same system model in a different location. This highlights the need for a qualification process that is dependent on material science rather than on individual manufacturing parameters.


This project began the process of developing a data-driven qualification process that allows relationships across instruments, platforms, suppliers, and even alloy systems to be inferred using intelligent machine-learning algorithms informed by underlying physics-based modeling. This allows predictions to be made across AM systems which can reduce the amount of data needed to qualify a new machine and certify a new part, greatly speeding the adoption of AM parts into military vehicles without sacrificing the quality and reliability that drive the need for qualification. The goal is to enable qualification of AM parts with significantly reduced testing across various tools, vendors, and feedstocks by directly incorporating awareness of manufacturing issues such as melt pool scaling, powder reuse, and local thermal environment during a build.

Technical Approach

Working closely with collaborators at U.S. Army Combat Capabilities Development Command (CCDC) Ground Vehicle Systems Center (GVSC), the Alliance for the Development of Additive Processing Technologies (ADAPT) Data-Driven Qualification for Additive Manufacturing (DDQ-AM) team at Colorado School of Mines established baseline process-property data for select demonstration parts using laser powder bed fusion and laid the groundwork for a multi-year vision to develop a physics-based, data-driven approach to enable qualification of AM parts across multiple manufacturing platforms and feedstocks. Combining expansive process monitoring with extensive microstructural analysis and measured properties, the team built an intelligent machine learning framework informed by underlying physical metallurgical principles to enable accurate prediction of printed part performance in conjunction with process monitoring data based on minimal input information.


Three candidate parts were identified on the U.S. Army M113 and MRAP vehicles. Topology optimization of these parts was conducted based on an assumed objective (e.g., minimum weight) without consideration for the thermodynamic conditions in the build environment. In parallel, basic thermo-mechanical simulations were undertaken to demonstrate the utility of simulation. Suitability of AM for these three candidate parts was demonstrated through modeling and fabrication efforts.

Parts and characterization specimens were fabricated on different metal AM machines using default manufacturer-suggested parameters. The anisotropic microstructure and tensile behavior were captured with regard to the qualification framework. Additionally, defect formation and feedstock reuse were studied individually but will be incorporated into the qualification framework in future work. The effects of defects were considered as part of the qualification framework.

This project also developed a method for simultaneously performing mass serialization/verification and surface binding enhancement by utilizing microindentations made by a portable hardness tester.

Project Participants

Project Principal

Other Project Participants

  • Army Ground Vehicle Service Center
  • Colorado State University
  • University of Pittsburgh

Public Participants

  • U.S. Department of Defense

Success Story

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