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Wolf Robotics Laser Wire AM System that resides in the Manufacturing Demonstration Facility at Oak Ridge National Labs (left). Schematic showing the elements and interfaces of the framework being developed (right).
This project seeks to develop AM predictive models of material behavior, processing windows, and material characterization validated with experimental data; develop closed-loop control scheme to optimize deposition; and achieve part quality and consistency.
Problem
The Navy plans to use additive manufacturing (AM) to supply out-of-production and long lead-time metallic components. Solutions need to ensure the manufacturability of metallic parts from a variety of AM machines and geographic locations. Certification costs of metal AM parts are a barrier to more extensive use. Advances in fundamental understanding and control of AM fabrication benefit the Naval supply chain by reducing the cost and time to modify, develop, and certify new/replacement parts. Recent advances in modeling and simulation tools based on an Integrated Computational Materials Engineering (ICME) approach provide an opportunity to relate process parameters to microstructure to properties to performance. Advanced sensor technology, distributed systems, and machine learning for use in process monitoring and control systems ensure the quality of AM fabricated parts.
Objective
The program objective is to define a framework that couples modeling tools, in-situ process measurements, real-time closed-loop control, and machine learning to meet performance requirements for AM parts. Predictive modeling tools are being implemented for the specific AM process variables and resulting microstructure predictions. Additional models for predicting mechanical properties based on microstructure characteristics are being verified with metallurgical testing and evaluations of Ti-6Al-4V AM specimens. Sensors are to be selected and tested for in-situ process monitoring with the goal of implementing these in-situ data into a closed-loop control system. The end goal is a framework that can provide real-time monitoring of the build, identify material variability and potential anomalies, and capture a build process history.
Technical Approach
This project engages explicit expertise from multiple sources. Lockheed Martin is the prime contractor responsible for integrating the entire system. Iowa State University is providing the model to predict microstructure. Carnegie Mellon is expanding the model to predict the mechanical properties from the microstructure. Colorado School of Mines is responsible for the metallurgical analysis, data analytics, model integration, and the sensor definition. GKN is developing the process map definition for the specific AM implementation and the design of experiments for test specimens, as well as supporting the AM specimen builds. Wolf Robotics is providing the AM system resident at the Manufacturing Demonstration Facility at Oak Ridge National Laboratories. America Makes is providing the project data management and dissemination. Each of the teammates has a breadth of knowledge of AM and brings a specific expertise to the project.
Project Participants
Project Principal
Other Project Participants
- Iowa State University
- Carnegie Mellon University
- Oak Ridge National Laboratory
- GKN Hoeganaes
- Wolf Robotics
- Colorado School of Mines
Public Participants
- U.S. Department of Defense
