Project Info

Quality Made: The Next Generation Titanium 3D Printer

Aaron Stebner |

This project is a large, collaborative effort between Mines, Lockheed Martin, Wolf Robotics, Carnegie Mellon, Oak Ridge, and other collaborators. The overall task is to deliver a 6-axis robotic laser welding additive manufacturing machine to the Navy that can qualify parts as they are being built, instead of taking years to qualify a part after it’s built.

Critical technologies need to be developed for in situ process monitoring – the ability to detect defects during additive manufacture of metals. In fact, this challenge relates more broadly to engineering and manufacturing – the ability to sense problems at the relevant time and length scales of materials manufacturing phenomena. In this particular case, the build velocities are on the orders of meters per second and cooling rates approaching 20,000 K/s, so combined it creates a sensing challenge that meets or exceeds 500,000 events per second. Sensing technology development strategies at this time scale include optical and/or thermal imaging, acoustic monitoring, X-ray imaging, and others.

The successful student applicant will have the opportunity to either work under a more senior graduate student or post doc, or to lead their own sensing technology development effort, depending on skills, motivation, and available time to dedicate to the program. They also have the ability to propose their own sensing strategy, though the team of experts has definitely targeted a few paths that need to be taken if the student prefers a more structured, directed research project.

More Information

Grand Engineering Challenge: Engineer the tools of scientific discovery

Student Preparation


The student should have moderate to excellent programming skills and a general knowledge of sensors and data collection devices. It is not expected that students will be familiar with all of the aforementioned technologies. We will match the student in an area they are most likely to succeed and most interested in.

Time Commitment

20 minimum per month, more is better

Skills/Techniques Gained

titanium alloys and their microstructures and properties
continuous laser welding additive manufacturing technologies
mechatronics/robotics controls
machine learning
data compression & sensing
software development

Mentoring Plan

The student will be paired with a senior PhD student or a Post Doc that can work with the student as much as needed, based on the student’s availability. Additionally, the student will meet at least every other week with myself and Professor Kappes to review their individual progress and plan their next steps.

We also have weekly ADAPT subgroup meetings on campus, bi-weekly “machine learning in materials manufacturing” journal club where all interested students on campus come to review literature in the field together, and we will likely have monthly project team meetings the student will be welcome to attend, as available, for additional interactions.