Uncovering acoustic signatures of pore formation in laser powder bed fusion

被引：0

作者：

Tempelman, Joshua R. ^{[1
,2
]}

Mudunuru, Maruti K. ^{[3
]}

Karra, Satish ^{[4
]}

Wachtor, Adam J. ^{[1
]}

Ahmmed, Bulbul ^{[5
]}

Flynn, Eric B. ^{[6
]}

Forien, Jean-Baptiste ^{[7
]}

Guss, Gabe M. ^{[8
]}

Calta, Nicholas P. ^{[7
]}

DePond, Phillip J. ^{[7
]}

Matthews, Manyalibo J. ^{[7
]}

机构：

[1] Engineering Institute, Los Alamos National Laboratory, Los Alamos,NM,87545, United States

[2] Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana,IL,61801, United States

[3] Watershed & Ecosystem Science, Pacific Northwest National Laboratory, Richland,WA,99352, United States

[4] Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland,WA,99352, United States

[5] Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos,NM,87545, United States

[6] Space Remote Sensing and Data Science, Los Alamos National Laboratory, Los Alamos,NM,87545, United States

[7] Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore,CA,94550, United States

[8] Engineering Directorate, Lawrence Livermore National Laboratory, Livermore,CA,94550, United States

来源：

International Journal of Advanced Manufacturing Technology | 2024年 / 130卷 / 5-6期

关键词：

We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments; and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom k -means clustering (NMF k) to learn the underlying spectral patterns associated with pore formation. NMF k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover; the NMF k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%; respectively. The intrinsic data compression of NMFk; the relatively light computational cost of the machine learning workflow; and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF. © 2023; This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Journal article (JA)

引用

页码：3103 / 3114

共 50 条

[1] Uncovering acoustic signatures of pore formation in laser powder bed fusion
Tempelman, Joshua R.
Mudunuru, Maruti K.
Karra, Satish
Wachtor, Adam J.
Ahmmed, Bulbul
Flynn, Eric B.
Forien, Jean-Baptiste
Guss, Gabe M.
Calta, Nicholas P.
Depond, Phillip J.
Matthews, Manyalibo J.
[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2023, 130 (5-6): : 3103 - 3114
[2] Uncovering acoustic signatures of pore formation in laser powder bed fusion
Joshua R. Tempelman
Maruti K. Mudunuru
Satish Karra
Adam J. Wachtor
Bulbul Ahmmed
Eric B. Flynn
Jean-Baptiste Forien
Gabe M. Guss
Nicholas P. Calta
Phillip J. DePond
Manyalibo J. Matthews
[J]. The International Journal of Advanced Manufacturing Technology, 2024, 130 : 3103 - 3114
[3] Dynamics of pore formation during laser powder bed fusion additive manufacturing
Martin, Aiden A.
Calta, Nicholas P.
Khairallah, Saad A.
Wang, Jenny
Depond, Phillip J.
Fong, Anthony Y.
Thampy, Vivek
Guss, Gabe M.
Kiss, Andrew M.
Stone, Kevin H.
Tassone, Christopher J.
Weker, Johanna Nelson
Toney, Michael F.
van Buuren, Tony
Matthews, Manyalibo J.
[J]. NATURE COMMUNICATIONS, 2019, 10 (1)
[4] Dynamics of pore formation during laser powder bed fusion additive manufacturing
Aiden A. Martin
Nicholas P. Calta
Saad A. Khairallah
Jenny Wang
Phillip J. Depond
Anthony Y. Fong
Vivek Thampy
Gabe M. Guss
Andrew M. Kiss
Kevin H. Stone
Christopher J. Tassone
Johanna Nelson Weker
Michael F. Toney
Tony van Buuren
Manyalibo J. Matthews
[J]. Nature Communications, 10
[5] Sensor fusion of pyrometry and acoustic measurements for localized keyhole pore identification in laser powder bed fusion
Tempelman, Joshua R.
Wachtor, Adam J.
Flynn, Eric B.
Depond, Phillip J.
Forien, Jean -Baptiste
Guss, Gabe M.
Calta, Nicholas P.
Matthews, Manyalibo J.
[J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2022, 308
[6] Pore defects in Laser Powder Bed Fusion: Formation mechanism, control method, and perspectives
Du, Chuanbin
Zhao, Yanhua
Jiang, Jingchao
Wang, Qian
Wang, Haijin
Li, Nan
Sun, Jie
[J]. JOURNAL OF ALLOYS AND COMPOUNDS, 2023, 944
[7] Acoustic Diagnostic of Laser Powder Bed Fusion Processes
Zhirnov, Ivan
Kouprianoff, Dean
[J]. SPS 2022, 2022, 21 : 542 - 552
[8] Identifying the keyhole stability and pore formation mechanisms in laser powder bed fusion additive manufacturing
Guo, Liping
Liu, Hanjie
Wang, Hongze
Wei, Qianglong
Xiao, Yakai
Tang, Zijue
Wu, Yi
Wang, Haowei
[J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2023, 321
[9] Keyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing
Huang, Yuze
Fleming, Tristan G.
Clark, Samuel J.
Marussi, Sebastian
Fezzaa, Kamel
Thiyagalingam, Jeyan
Leung, Chu Lun Alex
Lee, Peter D.
[J]. NATURE COMMUNICATIONS, 2022, 13 (01)
[10] Mitigating keyhole pore formation by nanoparticles during laser powder bed fusion additive manufacturing
Qu, Minglei
Guo, Qilin
Escano, Luis I.
Clark, Samuel J.
Fezzaa, Kamel
Chen, Lianyi
[J]. ADDITIVE MANUFACTURING LETTERS, 2022, 3

← 1 2 3 4 5 →