Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis

Benjamin Bevans; André Ramalho; Ziyad Smoqi; Aniruddha Gaikwad; Telmo G. Santos; Prahalada Rao; J. P. Oliveira

doi:10.1115/msec2023-101622

Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis

Benjamin Bevans, André Ramalho, Ziyad Smoqi, Aniruddha Gaikwad, Telmo G. Santos, Prahalada Rao, J. P. Oliveira

UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

The goal of this work is to detect flaw formation in wire arc additive manufacturing (WAAM). This process uses an electric arc as the energy source in order to melt metallic wire and deposit the new material, similar to metal inert gas (MIG) welding. Industry has been slow to adopt WAAM due to the lack of process consistency and reliability. The WAAM process is susceptible to a multitude of stochastic disturbances that cause instability in the electric arc. These arc instabilities eventually lead to flaw formation such as porosity, spatter, and excessive deviations in the desired geometry. Therefore, the objective of this work is to detect flaw formation using in-situ acoustic (sound) data from a microphone installed near the electric arc. This data was processed using a novel wavelet integrated graph theory approach. This approach detected the onset of multiple types of flaw formations with a false alarm rate of less than 2%. Using this method, this work demonstrates the potential for in-situ monitoring and flaw detection of the WAAM process in a computationally tractable manner.

Original language	English
Title of host publication	Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering
Publisher	ASME - The American Society of Mechanical Engineers
ISBN (Electronic)	978-0-7918-8723-3
DOIs	https://doi.org/10.1115/msec2023-101622
Publication status	E-pub ahead of print - 28 Sept 2023
Event	ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023 - New Brunswick, United States Duration: 12 Jun 2023 → 16 Jun 2023

Publication series

Name	International Manufacturing Science and Engineering Conference
Publisher	American Society of Mechanical Engineers
Volume	1

Conference

Conference	ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023
Country/Territory	United States
City	New Brunswick
Period	12/06/23 → 16/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Automation
Control
Rapid Prototyping and Solid Freeform Fabrication
Welding and Joining

Access to Document

10.1115/msec2023-101622

Cite this

Bevans, B., Ramalho, A., Smoqi, Z., Gaikwad, A., Santos, T. G., Rao, P., & Oliveira, J. P. (2023). Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis. In Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering Article v001t01a005 (International Manufacturing Science and Engineering Conference; Vol. 1). ASME - The American Society of Mechanical Engineers. Advance online publication. https://doi.org/10.1115/msec2023-101622

Bevans, Benjamin ; Ramalho, André ; Smoqi, Ziyad et al. / Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis. Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering. ASME - The American Society of Mechanical Engineers, 2023. (International Manufacturing Science and Engineering Conference).

@inproceedings{657066de51da4d98b641dd608b9fd628,

title = "Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis",

abstract = "The goal of this work is to detect flaw formation in wire arc additive manufacturing (WAAM). This process uses an electric arc as the energy source in order to melt metallic wire and deposit the new material, similar to metal inert gas (MIG) welding. Industry has been slow to adopt WAAM due to the lack of process consistency and reliability. The WAAM process is susceptible to a multitude of stochastic disturbances that cause instability in the electric arc. These arc instabilities eventually lead to flaw formation such as porosity, spatter, and excessive deviations in the desired geometry. Therefore, the objective of this work is to detect flaw formation using in-situ acoustic (sound) data from a microphone installed near the electric arc. This data was processed using a novel wavelet integrated graph theory approach. This approach detected the onset of multiple types of flaw formations with a false alarm rate of less than 2\%. Using this method, this work demonstrates the potential for in-situ monitoring and flaw detection of the WAAM process in a computationally tractable manner.",

keywords = "Automation, Control, Rapid Prototyping and Solid Freeform Fabrication, Welding and Joining",

author = "Benjamin Bevans and Andr{\'e} Ramalho and Ziyad Smoqi and Aniruddha Gaikwad and Santos, \{Telmo G.\} and Prahalada Rao and Oliveira, \{J. P.\}",

note = "Funding Information: info:eu-repo/grantAgreement/FCT/OE/UI\%2FBD\%2F151018\%2F2021/PT\# Andr{\'e} Ramalho, Telmo G. Santos and J.P. Oliveira acknowledge Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia (FCT-MCTES) for its financial support via the project UID/00667/2020 (UNIDEMI). JPO acknowledges the funding of CENIMAT/i3N by national funds through the FCT-Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia, I.P., within the scope of Multiannual Financing of R\&D Units, reference UIDB/50025/2020-2023. This activity has received funding from the European Institute of Innovation and Technology (EIT) – Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing. Prahalada Rao acknowledges funding from the Department of Energy (DOE), Office of Science, under Grant number DESC0021136, and the National Science Foundation (NSF) [Grant numbers CMMI-1719388, CMMI-1920245, CMMI-1739696, CMMI-1752069, PFI-TT 2044710, ECCS 2020246] for funding his research program. This work espousing the concept of online process monitoring in WAAM was funded through the foregoing DOE Grant (Program Officer: Timothy Fitzsimmons), which supported the doctoral graduate work of Mr. Benjamin Bevans. The XCT work was performed at the Nebraska Nanoscale Facility: National Nanotechnology Coordinated Infrastructure under award no. ECCS: 2025298, and with support from the Nebraska Research Initiative through the Nebraska Center for Materials and Nanoscience and the Nanoengineering Research Core Facility at the University of Nebraska-Lincoln. Publisher Copyright: Copyright {\textcopyright} 2023 by ASME.; ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023 ; Conference date: 12-06-2023 Through 16-06-2023",

year = "2023",

month = sep,

day = "28",

doi = "10.1115/msec2023-101622",

language = "English",

series = "International Manufacturing Science and Engineering Conference",

publisher = "ASME - The American Society of Mechanical Engineers",

booktitle = "Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering",

address = "United States",

}

Bevans, B, Ramalho, A, Smoqi, Z, Gaikwad, A, Santos, TG, Rao, P & Oliveira, JP 2023, Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis. in Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering., v001t01a005, International Manufacturing Science and Engineering Conference, vol. 1, ASME - The American Society of Mechanical Engineers, ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023, New Brunswick, United States, 12/06/23. https://doi.org/10.1115/msec2023-101622

Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis. / Bevans, Benjamin; Ramalho, André; Smoqi, Ziyad et al.
Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering. ASME - The American Society of Mechanical Engineers, 2023. v001t01a005 (International Manufacturing Science and Engineering Conference; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis

AU - Bevans, Benjamin

AU - Ramalho, André

AU - Smoqi, Ziyad

AU - Gaikwad, Aniruddha

AU - Santos, Telmo G.

AU - Rao, Prahalada

AU - Oliveira, J. P.

N1 - Funding Information: info:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F151018%2F2021/PT# André Ramalho, Telmo G. Santos and J.P. Oliveira acknowledge Fundação para a Ciência e a Tecnologia (FCT-MCTES) for its financial support via the project UID/00667/2020 (UNIDEMI). JPO acknowledges the funding of CENIMAT/i3N by national funds through the FCT-Fundação para a Ciência e a Tecnologia, I.P., within the scope of Multiannual Financing of R&D Units, reference UIDB/50025/2020-2023. This activity has received funding from the European Institute of Innovation and Technology (EIT) – Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing. Prahalada Rao acknowledges funding from the Department of Energy (DOE), Office of Science, under Grant number DESC0021136, and the National Science Foundation (NSF) [Grant numbers CMMI-1719388, CMMI-1920245, CMMI-1739696, CMMI-1752069, PFI-TT 2044710, ECCS 2020246] for funding his research program. This work espousing the concept of online process monitoring in WAAM was funded through the foregoing DOE Grant (Program Officer: Timothy Fitzsimmons), which supported the doctoral graduate work of Mr. Benjamin Bevans. The XCT work was performed at the Nebraska Nanoscale Facility: National Nanotechnology Coordinated Infrastructure under award no. ECCS: 2025298, and with support from the Nebraska Research Initiative through the Nebraska Center for Materials and Nanoscience and the Nanoengineering Research Core Facility at the University of Nebraska-Lincoln. Publisher Copyright: Copyright © 2023 by ASME.

PY - 2023/9/28

Y1 - 2023/9/28

N2 - The goal of this work is to detect flaw formation in wire arc additive manufacturing (WAAM). This process uses an electric arc as the energy source in order to melt metallic wire and deposit the new material, similar to metal inert gas (MIG) welding. Industry has been slow to adopt WAAM due to the lack of process consistency and reliability. The WAAM process is susceptible to a multitude of stochastic disturbances that cause instability in the electric arc. These arc instabilities eventually lead to flaw formation such as porosity, spatter, and excessive deviations in the desired geometry. Therefore, the objective of this work is to detect flaw formation using in-situ acoustic (sound) data from a microphone installed near the electric arc. This data was processed using a novel wavelet integrated graph theory approach. This approach detected the onset of multiple types of flaw formations with a false alarm rate of less than 2%. Using this method, this work demonstrates the potential for in-situ monitoring and flaw detection of the WAAM process in a computationally tractable manner.

AB - The goal of this work is to detect flaw formation in wire arc additive manufacturing (WAAM). This process uses an electric arc as the energy source in order to melt metallic wire and deposit the new material, similar to metal inert gas (MIG) welding. Industry has been slow to adopt WAAM due to the lack of process consistency and reliability. The WAAM process is susceptible to a multitude of stochastic disturbances that cause instability in the electric arc. These arc instabilities eventually lead to flaw formation such as porosity, spatter, and excessive deviations in the desired geometry. Therefore, the objective of this work is to detect flaw formation using in-situ acoustic (sound) data from a microphone installed near the electric arc. This data was processed using a novel wavelet integrated graph theory approach. This approach detected the onset of multiple types of flaw formations with a false alarm rate of less than 2%. Using this method, this work demonstrates the potential for in-situ monitoring and flaw detection of the WAAM process in a computationally tractable manner.

KW - Automation

KW - Control

KW - Rapid Prototyping and Solid Freeform Fabrication

KW - Welding and Joining

UR - https://www.scopus.com/pages/publications/85176815312

U2 - 10.1115/msec2023-101622

DO - 10.1115/msec2023-101622

M3 - Conference contribution

AN - SCOPUS:85176815312

T3 - International Manufacturing Science and Engineering Conference

BT - Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering

PB - ASME - The American Society of Mechanical Engineers

T2 - ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023

Y2 - 12 June 2023 through 16 June 2023

ER -

Bevans B, Ramalho A, Smoqi Z, Gaikwad A, Santos TG, Rao P et al. Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis. In Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering. ASME - The American Society of Mechanical Engineers. 2023. v001t01a005. (International Manufacturing Science and Engineering Conference). Epub 2023 Sept 28. doi: 10.1115/msec2023-101622

Flaw Detection in Wire Arc Additive Manufacturing Using In-Situ Acoustic Sensing and Graph Signal Analysis

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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