AMMO Monthly Meeting

AMMO WG Teleconference Minutes – 3 November 2021

On 3 November 2021, the Additive Manufacturing Maintenance Operations (AMMO) working group conducted a teleconference with approximately 65 participants.  A summary follows:

Introduction:  Debbie Lilu (NCMS) welcomed the group, discussed administrative protocol, and provided an overview of the agenda.

Machine learning utilized to accelerate AM within DoD programs – Zach Simkin, Senvol, discussed several DoD programs including an SBIR with ONR, NAVAIR, NAVSEA, and AFRL to develop machine learning (ML) software capabilities, an Air Force program to develop a process optimization and characterization plan for multi-laser AM, an America Makes program using an ML approach enabling rapid development of material allowables, and an ARL program to develop a qualification plan that leverages ML and used an ML approach to enable rapid development of material allowables.

Additive Manufacturing Scaling Ability – Christina Vasil, GE Additive, discussed three AM scaling capabilities that are TRL 2 through 6. The first was Large-Format Printing which uses multi-laser machines with scan stitching and enables new design, consolidating parts, and light-weighting while also maintaining speed and quality of production. The second was more transparent and accountable qualification and certification process to ensure high quality builds with greater yield rates through the use of simulations and sensors, driving faster adoption of additive. The third was developing a secure digital trust supply chain requiring risk mitigation as the AM network grows.


America Makes Mx and Sustainment Advisory Group (AMMSAG) Update – Marilyn Gaska (LMCO) provided an update that included the following:

  1. The AMMSAG meeting featured presentation was from ADDIGURU on in-situ monitoring technology for the Additive Manufacturing (AM) processes.
  2. Todd Spurgeon stated that draft AM Guidebook comments are due NLT the end of November.
  3. America Makes upcoming events:
    1. Moved the annual meeting to a virtual format on Dec 1-2. Registration is open and no cost. Use the following link:
    2. Gaps Progress Report Available: America Makes & ANSI Standardization Roadmap for Additive Manufacturing
    3. The winners of the RAPID Innovation call were announced. AMMSAG sponsored an idea on parts families product qualification and certification that was awarded as a project.
    4. DMC is still on; however, the parts and materiel management group has decided to move to March.


MxD Update – Federico Sciammarello was unable to join. There will be an MXD announcement next month.


JAMWG Update – Brett Connor, OSD(R&E), provided a review from the October JAMWG meeting conducted virtually on 27 and 29 Oct.

  1. Reviewed FY21 progress and updated priorities for FY22
  2. The 1st call for AM metrics went out. It is largely focused from an organic standpoint.
  3. This Summer DoD Instruction 5000.93 “Use of Additive Manufacturing in the DoD” was published.

The next JAMWG meeting is November 17th.


Next Meeting: – The next AMMO WG call is scheduled for 10:30-12:00 am (Eastern Time) on Wednesday, 1 December 2021.


POC for this action is Ray Langlais, LMI,, (571) 633-8019


Forum Q&A

Machine learning utilized to accelerate AM within DoD programs – Zach Simkin, Senvol,)



Q1. Re: Slide 10 on input modules of AM data, where does data about the feedstock come into play (i.e. particle size, PSD, morphology, virgin or reused, etc.)?

A1. Typically put as module #1, but can make as additional module.


Q2. What Materials data bases does Senvol have access to?

A2. We have access to quite a wealth of material databases. You may use our database, but most clients use their own data.


Q3. How much faster is the Senvol ML process versus traditional sequential process?

A3. It depends. It is not uncommon to have savings of 50% or more.


Q4. When you are looking at optimizing parameter sets (AFRL study), did Senvol ML look at only bulk parameters? Or did you also look at contour parameters, upskin/downskin? What about parameters for thin features versus those for thick features?

A4. We looked at various parameters. Machine learning tool incase 24 or 17 is very helpful in obtaining quick answers.



Additive Manufacturing Scaling Ability – Christina Vasil, GE Additive



Q1. Is GE Additive considering ML to speed up the qual and cert time for new AM builds?

A1. Justin – Absolutely, in a variety of ways, It is a growing area of use for materiel development, also leverage for sensor applications,. Any time we are using large data bases, we consider using machine learning.


Q2. Does GE Digital’s trusted network leverage smart contracts to support pay as you print business models?

A2. I haven’t been personally involved in the smart contract. At this time, no, that is not how GE contracting is set-up. However, it is an interesting idea, and may be viable in the future.

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Date(s) - 11/03/2021
10:30 AM - 12:00 PM