5567.003 Sensor-Based Approaches to LPBF Machine Pre-Qualification

The University of Dayton Research Institute (UDRI) has partnered with ZEISS, Northrop Grumman (NGC), and The Ohio State University (OSU) to utilize gas flow characterization, CFD modeling, ZEISS’ rapid parameter characterization service, and in-situ sensor correlation for demonstrating consistent quantification of gas flow effects on end part quality.


There is a lack of normative standards for machine pre-qualification across machine types and part manufacturers. Most organizations typically qualify their machines through internal processes or by following the original equipment manufacturer’s (OEM) specifications. Since there is a high degree of variation in machine designs, qualification practices can vary significantly. Most data element gaps noted for machine pre-qualification such as gas flow, spatter, flaw state, surface quality, and surface defects can be directly influenced by gas flow variation. Gas flow designs are extremely inconsistent across machine types, making gas flow characterization arguably the most important metric for pre-qualification purposes.


This project seeks to utilize sensor-based data sets, correlated to gas flow characterization, to pre-qualify machines and manufacturers to enable quality parts across the additive manufacturing (AM) supply chain. This program will deliver measurable metrics for machine and manufacturer pre-qualification that can be adopted at any stage of the metal AM supply chain.

Technical Approach

Gas flow characterization and modeling of multiple machine types in varying flow conditions will be performed across multiple locations via an anemometer. A uniform spacing of measurements will be taken across the build chamber to establish flow conditions throughout the entire build area. Gas flow metrics will be collected in the baseline gas flow conditions for the AM machines. After these initial measurements, the gas flow conditions within the AM machine will be modified. Gas flow modifications include real-world situations such as modification of the inlet diffuser, inlet/outlet geometry, obstructions of the inlet/outlet, and powder infiltration of the inlet/outlet. Following modification of the gas flow, the conditions will be recharacterized via an anemometer. Task two will include multi-machine printing using in-situ sensing of the ZEISS rapid AM parameter matrix in varying gas flow conditions with sensor data collected via the same sensor package across all prints. Task three will be completed using ZEISS rapid AM parameter characterization of printed parts via X-ray CT. Finally, Task four will produce pre-qualification metrics via data fusion of sensor data, gas flow metrics, model outputs, and NDE machine quality results.

Project Participants

Project Principal

Other Project Participants

  • Northrop Grumman
  • The Ohio State University
  • Zeiss

Public Participants

  • U.S. Department of Defense

Project Summary

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