SBIR-STTR Award

Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.
Award last edited on: 8/7/2019

Sponsored Program
STTR
Awarding Agency
DOD : DLA
Total Award Amount
$1,099,962
Award Phase
2
Solicitation Topic Code
DLA18A-001
Principal Investigator
John Middendorf

Company Information

ARCTOS Technology Solutions LLC (AKA: Universal Technology Corporation~UTC)

1270 North Fairfield Road
Dayton, OH 45432
   (937) 426-2808
   tharruff@utcdayton.com
   www.utcdayton.com

Research Institution

University of Dayton

Phase I

Contract Number: SP4701-18-P-0115
Start Date: 00/00/00    Completed: 00/00/00
Phase I year
2018
Phase I Amount
$99,989
Universal Technology Corporation (UTC) has teamed with the University of Dayton Research Institute (UDRI), Stratonics, and Macy Consulting to demonstrate not only the transitionability into commercial systems, but also to develop the data analytics and monitoring and control requirements to extract the full value fromseveral sensors, including the Stratonics ThermaViz, acoustic and profilometry sensors acquired by UDRI, and a low-cost sensor fusion system, AMSENSE, developed and sold by UTC.This project will judge the efficacy of each sensor for in-process monitoring and control of Laser Powder Bed Fusion (LPBF), and then decide which sensors should be combined into a new sensor fusion system that will finally enable qualified AM processes to exist en masse within the DoD supply chain.These decisions will be based on correlations between sensor data and final build results measured on physical test specimens.At the end of this project the research team will identify what future work is needed to implement the new sensor fusion system on DoD LPBF systems for real-time process control.

Phase II

Contract Number: SP4701-19-C-0041
Start Date: 00/00/00    Completed: 00/00/00
Phase II year
2019
Phase II Amount
$999,973
This Phase II project aims to assemble the key set of sensor modalities that are needed to reliably view the key process anomalies and properties of laser powder bed fusion. The research team will down-select from the Phase I sensors investigated and integrate the sensors into a sensor fusion software package that facilitates data collection and synchronization, and eventually feedback control of the AM process. The research team will then prove the sensors see all key process variations, and perform robust effect of defect studies that will supply the data needed for developing accurate process windows, and hence closed-loop feedback control algorithms. Next the team will determine what corrective actions are needed to fix the specific process variations that occur and implement those actions in the software system. The algorithms and sensing techniques will be demonstrated against baseline builds on multiple AM systems to prove their validity and capability, and a plan for transitioning the technology into the DoD supply chain will be developed.