Machine Learning Tools to Optimize Metal Additive Manufacturing Process Parameters to Enhance Fatigue Performance of Aircraft Components
Navy STTR 2020.A - Topic N20A-T002
NAVAIR - Ms. Donna Attick firstname.lastname@example.org
Opens: January 14, 2020 - Closes: February 12, 2020 (8:00 PM ET)
TECHNOLOGY AREA(S): Air
ACQUISITION PROGRAM: JSF
Joint Strike Fighter
OBJECTIVE: Develop an
advanced machine learning (ML) tool capable of optimizing process parameters
for metal laser powder-based additively manufactured components to achieve
enhanced fatigue performance for aircraft components.
powder bed and powder feed additive manufacturing (AM) technologies have proven
to produce complicated parts from high-performance alloys such as titanium,
Inconel, and tool steel [Ref 1]. Many processes are currently able to consistently
produce intricate geometries and meet standard geometric tolerances. However,
achieving predictable part performance, including static (e.g., strength) and
dynamic (e.g., high cycle and low cycle fatigue) behaviors remains a
significant challenge. In order to attain satisfactory part performance, pre-
and post-processing parameters are tuned using expensive trial and error
approaches. Perhaps the use of various sensors integrated with simulation and
modeling tools that leverage data analytics, data fusion, and machine learning
(ML) techniques may improve fatigue performance of AM parts, potentially
without any post-processing required.
PHASE I: Develop an
initial computational concept for a ML ICME-based toolset for a laser powder
metal AM process under the assumption of in-situ and/or ex-situ sensor data to
link AM process parameters and/or state variables to the fatigue performance of
the part. Ensure that the concept methodology demonstrates both its ability for
sensor fusion and its ability to learn from trial runs to predict the final
part geometry, associated material properties, and final part performance.
Demonstrate the feasibility of the methodology using actual AM coupons, testing
(e.g., ASTM E466, ASTM E606) [Refs 7, 8], and analyses for a single material.
The computational prototype of the proposed advanced ML ICME tool should have
the potential for development into a full-scale ML ICME system for integrating
with AM machines to enable designer to optimize fatigue life in Phase II. The
Phase I effort will include prototype plans to be developed under Phase II.
PHASE II: Fully develop,
verify, and validate a prototype ML system for a laser powder-based metal AM
process to perform geometry control and material property control during AM
processing. Demonstrate its ability to manufacture aircraft components with
complex geometry and tailored performance using additional metal alloys.
PHASE III DUAL USE
APPLICATIONS: Further develop and refine an advanced ML ICME system for various
powder-based AM processes to fabricate specific naval aircraft components for
integration into the Fleet. Conduct final component-level testing to
demonstrate the geometry and material property control of AM components meeting
the Navy’s specifications.
1. Frazier, W.E. “Metal
Additive Manufacturing: A Review”. Journal of Materials Engineering and
Performance, 23 (6), 2014, pp. 1917-1928. DOI: 10.1007/s11665-014-0958-z. https://link.springer.com/article/10.1007/s11665-014-0958-z
Computational Materials Engineering: A Transformational Discipline for Improved
Competitiveness and National Security." National Research Council, The
National Academies Press, Washington, DC, 2008. DOI: 10.17226/12199
3. Russell, S. and
Norvig, P. “Artificial Intelligence: A Modern Approach.” Prentice Hall: Upper
Saddle River, NJ, 2009. ISBN-10: 0136042597. https://www.cin.ufpe.br/~tfl2/artificial-intelligence-modern-approach.9780131038059.25368.pdf
4. Vandone, A., Baraldo,
S., and Valente, A. “Multisensor Data Fusion for Additive Manufacturing Process
Control.” IEEE Robotics and Automation Letters, 3 (4), 32018, pp. 279-3284.
5. Zhu, Z., Anwar, N.,
Huang, Q., and Mathieu, L. “Machine learning in tolerancing for additive
manufacturing.” CIRP Annuals, 67 (1), 2018, pp. 157-160. DOI:
6. Garanger, K., Feron,
E., Garoche, P., Rimoli, J. J., Berrigan, J. D., Grover, M., and Hobbs, K.
“Foundations of Intelligent Additive Manufacturing.” https://arxiv.org/pdf/1705.00960.pdf
7. ASTM E466 – 15
Standard Practice for Conducting Force Controlled Constant Amplitude Axial
Fatigue Tests of Metallic Materials
8. ASTM E606 / E606M –
12 Standard Test Method for Strain-Controlled Fatigue Testing https://www.astm.org/Standards/E606
Learning; ML; Sensor Fusion, Fatigue; Metal Additive Manufacturing; AM; Laser
Powder Bed Fusion; Powder Feed; ICME; Material Property Control