Automated Industrial Metallography
(AIM)
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Corporate
 4401
Dayton-Xenia
Rd.
Dayton,
Ohio 45432-1894
 Phone:
937-426-6900
 Fax: 937-429-5413
Use Office
or disclosure
of data
contained on
this sheet
is subject
to the restriction
on the title
page of this presentation.
Subjects to be Covered
• SBIR Phase 1 – Structural Repair
• SBIR Phase 1 & 2 – Additive Manufacturing (AM)
• RIF – Automated Industrial Metallography (AIM)
– RoboMet.3D (R3D)
• AIM Networking Concept
• UES POCs
• Summary
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
2
Structural Repair
• Dimensional Restoration of Corrosion
Damaged Aircraft Components
• SBIR Phase I
• Contract Number: FA8117-14-C-0001
• POP: 20 January - 19 October 2014
• UES proposes metal spray technologies for dimensional
restoration of Alloy 7075 (7050) aircraft components
damaged by corrosion
(continued)
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
3
Structural Repair
USAF TPOC:
•
•
•
•
•
David F. Ellicks, DAF, DR-II
Senior Materials Engineer
AF Corrosion Prevention and Control Office (AFRL/RXSSR)
325 Richard Ray Blvd (Bldg 165), Robins AFB, GA 31098-1639
David.ellicks@us.af.mil, DSN 468-7701 (478-926-7701)
UES PI:
• Dr. Rabi S. Bhattacharya
• Division Manager, Surface Engineering Laboratory
• rbhattacharya@ues.com, 937-426-6900 x114 (continued)
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
4
Structural Repair
• UES Team Members
• Curtiss-Wright Surface Technologies
•
•
•
•
Peter Ruggiero, CWST Regional Manager
Peter.ruggiero@cwst.com, 860-623.9901 x5160
Dr. Xinqing (Chin) Ma, 860.623.9901 x5121
Chin.ma@cwst.com,
• Flame-Spray Industries
•
•
Keith Kowalsky, Owner
kkowalsky@flame-spray.com, 516.459.7154
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
5
Additive Manufacturing (AM)
• Process Modeling for Additive Manufacturing
of Titanium Alloys by Electron Beam Melting
• SBIR Phase I (Notified Phase II Awardee)
• Contract Number: FA8650-13-M-5059
• POP: 5/31/2013 – 3/3/2014
• UES proposed to obtain data to anchor models for AM by
EBM. Predictive modeling of optimum machine parameters
for a given powder and part dimensions will increase 1st part
yield
(continued)
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
6
Additive Manufacturing (AM)
• USAF TPOC:
•
•
•
•
John Kleek
Materials Engineer
AFRL, Wright-Patterson AFB, Dayton, Ohio
John.kleek@us.af.mil,
• UES PI:
• Dr John Porter
• Material Scientist, Material Characterization Facility
• jporter@ues.com, 937.255.1310
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
7
AIM Project
Contract
FA8750-12C-0197
Award Date & Start Date
8/8/2012
Completion Date
5/10/2014 (2 Units = Hill/Robins AFBs)
Troy Graham, 76 PMXG/QPE
troy.graham@us.af.mil
AF TPOC
Chad Odell, 309 CMXG/ENS
chad.odell@us.af.mil
Mufit Yalcin, 802 MXSG/MXDTA
mufit.yalcin@us.af.mil
Contracting Officer
AFSC PM
Tiara Moore, AFSC/PZAB
tiara.moore@us.af.mil
Ed Kincaid, AFSC/ENS
edwin.kincaid@us.af.mil
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
AIM Project
Contract
FA8750-12C-0197 =UP
Award Date & Start Date
9/23/2013
Completion Date
Award + 1 year (1 Unit = Tinker AFB)
Jake Freemen, Project Engineer
jfreeman@ues.com,
UES TPOC
Umesh Adiga, PH.D., Software Development
uadiga@ues.com,
Tom Kielbaso, Technical Sales
tkielbaso@ues.com
Bob Bondaruk, UES Midwest City
bbondaruk@ues.com,
Contracting Officer
PM
Rick Weddle, Director of Contracts
rweddle@ues.com,
Nina Joshi, Ph.D., CEO
njoshi@ues.com,
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
RoboMet.3D
Automated system generating 2D
microstructural data for 3D characterization
Common Applications:
•
•
•
Magnetic domains
Dendrites, voids
True shape of grains, grain
boundary engineering
AFRL SBIR Phase II,
• Topic AF083-77
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
10
AIM Project
• Automated
Industrial
Metallography
–
–
–
–
Indexing
Sectioning
Cold Mount
Interface
w/RoboMet.3D®
– Reporting
• Robo-Met.3D®
–
–
–
–
Grinding
Polishing
Cleaning
Sample
Preparation
– Imaging
(continued)
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
AIM Project
FEATURES
BENEFITS
•Precise metallography control
for HVOF thermal spray coatings
•Increased evaluation efficiency
– Sectioning, Polishing,
Cleaning, Imaging, Reporting
•Evaluation/Qualification Tool
•Rapid & Consistent
•Artificial Intelligence (AI)
•Real-time, repeatable data
• Reduced processing times;
estimated results ~ 90 minutes
• Reduced analysis subjectivity
•LAN/WAN Capability
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
12
AIM - Automated Reporting Parameters
Specimen Name
Specimen #
Coating Booth #
Technician #
RESULTS
Coat Thickness
Specimen Image:
Voids
Oxides
Date/Time
Coated
Porosity
Date/Time
Tested
Coating Type
Substrate Type
Pass / Fail
PARAMETER
Un-Melted
Particle
Transverse
Cracks
Grit
De-Iaminations
Comments
Interface
Separation
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
13
AIM - System Testing
SYSTEM TESTING @ UES
• Subroutines Functionality Testing
– 20 coupons
• Integrated Systems Functionality
Testing - 250 coupons
• Imaging System Reliability Testing
– 250 coupon
• Compare AI algorithms against
manual metallography results
DEPOT INSTALLATION
• Turnkey
– UES will ship, de-crate, set-up,
and ensure all subsystems and
integrated equipment are
operational prior to DD250
actions
• Train
–
4 Technicians per beddown site
• Determine AIM system accuracy
to AMS and ASTM standards
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
14
AIM - Timeline
Artificial Intelligence:
Q2 2014
• System &
software
design,
development
Hardware: Q1 2014
• Algorithm
development
Delivery: Q4 2014
• Hardware/Software integration
• AI integration
• Verification
Integration: Q3 2014
• Installation, Trng,
& Ver/Val, Hill,
Robins, & Tinker
AFBs
• Final Reports
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
15
AIM Networking Concept
The future open manufacturing concept for the AIM unit includes
networking AIM instruments across USAF (AFSC, ALCs, & AFRL). Once
networked with other COEs the diagnostic tool will enable collaborative
sharing of expertise and data.
The ultimate goal of transitioning this technology is to improve the
efficiency of the coating process by facilitating real time access to large
pools of data from spray coatings and application methods.
Commercialization represents a major success for both AFRL and the
ALC’s, as the concept has evolved from the original Robo-Met.3D®
microstructural characterization tool used in laboratories to a broadly
applicable high-throughput coating quality analysis tool usable on the
production floor.
(continued)
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
16
AIM Networking Concept
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
17
UES POCs
• Jake Freeman
– Project Engineer, Dayton, Ohio
– 937.426.6900 X172
– jfreeman@ues.com
• Bob Bondaruk
– Chief Sustainment Officer, Midwest City, Oklahoma
– 405.361.4266
– bbondaruk@ues.com
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.
Summary
Bottom Line
Adding an AIM unit to the DoD MetLab
evaluation and certification process replaces
manual steps with semi-automated functions
thus reducing time while increasing data
standardization in the decision process
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this presentation.