Reliability centered additive manufacturing computational design framework

Patrick Harris, Bernard Laskowski, Edward William Reutzel, James C. Earthman, Andrew J. Hess

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Analatom, Applied Research Laboratory Penn State University, and University of California Irvine propose to apply modern concepts from simulation, corrosion modeling, and control theory along with Prognostics and Health Management (PHM) in the development of an end-to-end, prognostics-based Additive Manufacturing (AM) materials assessment architecture. The project team will consider four potential levels of implementation that includes molecular dynamics simulation of material corrosion and cracking defects, AM advanced statistical process control and monitoring, enhancing end-use items with corrosion, strain sensors and operational environment assessment techniques. Selection of appropriate applied levels architecture will be dependent on the desired target application. Emphasis will be placed on developing a flexible computational framework that incrementally learns optimal parameters and discerns parameter sets that lead to degradation profiles. An approach for a Reliability-Centered AM Computational Design Framework (RCAM CDF) focused on selective laser melt (SLM) AM processes for metallic components has been developed. This design framework initially includes minimum part geometry for a load bearing coupon structure, with defect types and densities induced by design of the AM build fabrication process, followed by laser Computed Tomography (CT) inspection of the components, with subsequent in situ structural monitoring sensors (corrosion, strain) to record the accelerated lifetime and fatigue testing. Existing designs, associated design tools appropriate for design, materials, processes, standards and data management systems already exist. These designs already have associated failure modes, fault analysis tools and fault isolation manuals referenced to actual designs. Introducing new tools that attempt to unilaterally replace existing design tools will inhibit RCAM CDF adoption rate. An alternate approach that Analatom recommends uses associative memory linking data exported from design tools, materials, processes, standards, and data management systems across the entire US Navy enterprise creating a library of reference Additive Manufacturing designs. The designs would be based on original designs modified for Additive Manufacturing. Preliminary proof has been demonstrated that this approach can capture and find 'seeded' print defects correlating to scan images taken during build. The associative memory image based 'defect' scanner has been able similar 'defect regions' corresponding to small simulated corrosive pits.

Original languageEnglish (US)
Title of host publication2018 IEEE Aerospace Conference, AERO 2018
PublisherIEEE Computer Society
Pages1-10
Number of pages10
ISBN (Electronic)9781538620144
DOIs
StatePublished - Jun 25 2018
Event2018 IEEE Aerospace Conference, AERO 2018 - Big Sky, United States
Duration: Mar 3 2018Mar 10 2018

Publication series

NameIEEE Aerospace Conference Proceedings
Volume2018-March
ISSN (Print)1095-323X

Other

Other2018 IEEE Aerospace Conference, AERO 2018
CountryUnited States
CityBig Sky
Period3/3/183/10/18

Fingerprint

3D printers
manufacturing
defect
corrosion
Defects
Corrosion
defects
associative memory
data management
management systems
Information management
Bearings (structural)
laser
sensor
Data storage equipment
Statistical process control
navy
Fatigue testing
control theory
Lasers

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

Cite this

Harris, P., Laskowski, B., Reutzel, E. W., Earthman, J. C., & Hess, A. J. (2018). Reliability centered additive manufacturing computational design framework. In 2018 IEEE Aerospace Conference, AERO 2018 (pp. 1-10). (IEEE Aerospace Conference Proceedings; Vol. 2018-March). IEEE Computer Society. https://doi.org/10.1109/AERO.2018.8396824
Harris, Patrick ; Laskowski, Bernard ; Reutzel, Edward William ; Earthman, James C. ; Hess, Andrew J. / Reliability centered additive manufacturing computational design framework. 2018 IEEE Aerospace Conference, AERO 2018. IEEE Computer Society, 2018. pp. 1-10 (IEEE Aerospace Conference Proceedings).
@inproceedings{67b34f6b05db485088008b569fd63382,
title = "Reliability centered additive manufacturing computational design framework",
abstract = "Analatom, Applied Research Laboratory Penn State University, and University of California Irvine propose to apply modern concepts from simulation, corrosion modeling, and control theory along with Prognostics and Health Management (PHM) in the development of an end-to-end, prognostics-based Additive Manufacturing (AM) materials assessment architecture. The project team will consider four potential levels of implementation that includes molecular dynamics simulation of material corrosion and cracking defects, AM advanced statistical process control and monitoring, enhancing end-use items with corrosion, strain sensors and operational environment assessment techniques. Selection of appropriate applied levels architecture will be dependent on the desired target application. Emphasis will be placed on developing a flexible computational framework that incrementally learns optimal parameters and discerns parameter sets that lead to degradation profiles. An approach for a Reliability-Centered AM Computational Design Framework (RCAM CDF) focused on selective laser melt (SLM) AM processes for metallic components has been developed. This design framework initially includes minimum part geometry for a load bearing coupon structure, with defect types and densities induced by design of the AM build fabrication process, followed by laser Computed Tomography (CT) inspection of the components, with subsequent in situ structural monitoring sensors (corrosion, strain) to record the accelerated lifetime and fatigue testing. Existing designs, associated design tools appropriate for design, materials, processes, standards and data management systems already exist. These designs already have associated failure modes, fault analysis tools and fault isolation manuals referenced to actual designs. Introducing new tools that attempt to unilaterally replace existing design tools will inhibit RCAM CDF adoption rate. An alternate approach that Analatom recommends uses associative memory linking data exported from design tools, materials, processes, standards, and data management systems across the entire US Navy enterprise creating a library of reference Additive Manufacturing designs. The designs would be based on original designs modified for Additive Manufacturing. Preliminary proof has been demonstrated that this approach can capture and find 'seeded' print defects correlating to scan images taken during build. The associative memory image based 'defect' scanner has been able similar 'defect regions' corresponding to small simulated corrosive pits.",
author = "Patrick Harris and Bernard Laskowski and Reutzel, {Edward William} and Earthman, {James C.} and Hess, {Andrew J.}",
year = "2018",
month = "6",
day = "25",
doi = "10.1109/AERO.2018.8396824",
language = "English (US)",
series = "IEEE Aerospace Conference Proceedings",
publisher = "IEEE Computer Society",
pages = "1--10",
booktitle = "2018 IEEE Aerospace Conference, AERO 2018",
address = "United States",

}

Harris, P, Laskowski, B, Reutzel, EW, Earthman, JC & Hess, AJ 2018, Reliability centered additive manufacturing computational design framework. in 2018 IEEE Aerospace Conference, AERO 2018. IEEE Aerospace Conference Proceedings, vol. 2018-March, IEEE Computer Society, pp. 1-10, 2018 IEEE Aerospace Conference, AERO 2018, Big Sky, United States, 3/3/18. https://doi.org/10.1109/AERO.2018.8396824

Reliability centered additive manufacturing computational design framework. / Harris, Patrick; Laskowski, Bernard; Reutzel, Edward William; Earthman, James C.; Hess, Andrew J.

2018 IEEE Aerospace Conference, AERO 2018. IEEE Computer Society, 2018. p. 1-10 (IEEE Aerospace Conference Proceedings; Vol. 2018-March).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Reliability centered additive manufacturing computational design framework

AU - Harris, Patrick

AU - Laskowski, Bernard

AU - Reutzel, Edward William

AU - Earthman, James C.

AU - Hess, Andrew J.

PY - 2018/6/25

Y1 - 2018/6/25

N2 - Analatom, Applied Research Laboratory Penn State University, and University of California Irvine propose to apply modern concepts from simulation, corrosion modeling, and control theory along with Prognostics and Health Management (PHM) in the development of an end-to-end, prognostics-based Additive Manufacturing (AM) materials assessment architecture. The project team will consider four potential levels of implementation that includes molecular dynamics simulation of material corrosion and cracking defects, AM advanced statistical process control and monitoring, enhancing end-use items with corrosion, strain sensors and operational environment assessment techniques. Selection of appropriate applied levels architecture will be dependent on the desired target application. Emphasis will be placed on developing a flexible computational framework that incrementally learns optimal parameters and discerns parameter sets that lead to degradation profiles. An approach for a Reliability-Centered AM Computational Design Framework (RCAM CDF) focused on selective laser melt (SLM) AM processes for metallic components has been developed. This design framework initially includes minimum part geometry for a load bearing coupon structure, with defect types and densities induced by design of the AM build fabrication process, followed by laser Computed Tomography (CT) inspection of the components, with subsequent in situ structural monitoring sensors (corrosion, strain) to record the accelerated lifetime and fatigue testing. Existing designs, associated design tools appropriate for design, materials, processes, standards and data management systems already exist. These designs already have associated failure modes, fault analysis tools and fault isolation manuals referenced to actual designs. Introducing new tools that attempt to unilaterally replace existing design tools will inhibit RCAM CDF adoption rate. An alternate approach that Analatom recommends uses associative memory linking data exported from design tools, materials, processes, standards, and data management systems across the entire US Navy enterprise creating a library of reference Additive Manufacturing designs. The designs would be based on original designs modified for Additive Manufacturing. Preliminary proof has been demonstrated that this approach can capture and find 'seeded' print defects correlating to scan images taken during build. The associative memory image based 'defect' scanner has been able similar 'defect regions' corresponding to small simulated corrosive pits.

AB - Analatom, Applied Research Laboratory Penn State University, and University of California Irvine propose to apply modern concepts from simulation, corrosion modeling, and control theory along with Prognostics and Health Management (PHM) in the development of an end-to-end, prognostics-based Additive Manufacturing (AM) materials assessment architecture. The project team will consider four potential levels of implementation that includes molecular dynamics simulation of material corrosion and cracking defects, AM advanced statistical process control and monitoring, enhancing end-use items with corrosion, strain sensors and operational environment assessment techniques. Selection of appropriate applied levels architecture will be dependent on the desired target application. Emphasis will be placed on developing a flexible computational framework that incrementally learns optimal parameters and discerns parameter sets that lead to degradation profiles. An approach for a Reliability-Centered AM Computational Design Framework (RCAM CDF) focused on selective laser melt (SLM) AM processes for metallic components has been developed. This design framework initially includes minimum part geometry for a load bearing coupon structure, with defect types and densities induced by design of the AM build fabrication process, followed by laser Computed Tomography (CT) inspection of the components, with subsequent in situ structural monitoring sensors (corrosion, strain) to record the accelerated lifetime and fatigue testing. Existing designs, associated design tools appropriate for design, materials, processes, standards and data management systems already exist. These designs already have associated failure modes, fault analysis tools and fault isolation manuals referenced to actual designs. Introducing new tools that attempt to unilaterally replace existing design tools will inhibit RCAM CDF adoption rate. An alternate approach that Analatom recommends uses associative memory linking data exported from design tools, materials, processes, standards, and data management systems across the entire US Navy enterprise creating a library of reference Additive Manufacturing designs. The designs would be based on original designs modified for Additive Manufacturing. Preliminary proof has been demonstrated that this approach can capture and find 'seeded' print defects correlating to scan images taken during build. The associative memory image based 'defect' scanner has been able similar 'defect regions' corresponding to small simulated corrosive pits.

UR - http://www.scopus.com/inward/record.url?scp=85049838785&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049838785&partnerID=8YFLogxK

U2 - 10.1109/AERO.2018.8396824

DO - 10.1109/AERO.2018.8396824

M3 - Conference contribution

T3 - IEEE Aerospace Conference Proceedings

SP - 1

EP - 10

BT - 2018 IEEE Aerospace Conference, AERO 2018

PB - IEEE Computer Society

ER -

Harris P, Laskowski B, Reutzel EW, Earthman JC, Hess AJ. Reliability centered additive manufacturing computational design framework. In 2018 IEEE Aerospace Conference, AERO 2018. IEEE Computer Society. 2018. p. 1-10. (IEEE Aerospace Conference Proceedings). https://doi.org/10.1109/AERO.2018.8396824