Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Manufacturing Processes. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Manufacturing Processes, 75, 2022 DOI: 10.1016/j.jmapro.2022.01.044
Accepted author manuscript, 2.42 MB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Progress and perspectives of in-situ optical monitoring in laser beam welding
T2 - Sensing, characterization and modeling
AU - Wu, D.
AU - Zhang, P.
AU - Yu, Z.
AU - Gao, Y.
AU - Zhang, H.
AU - Chen, H.
AU - Chen, S.
AU - Tian, Y.
N1 - This is the author’s version of a work that was accepted for publication in Journal of Manufacturing Processes. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Manufacturing Processes, 75, 2022 DOI: 10.1016/j.jmapro.2022.01.044
PY - 2022/3/31
Y1 - 2022/3/31
N2 - Laser beam welding manufacturing (LBW), being a promising joining technology with superior capabilities of high-precision, good-flexibility and deep penetration, has attracted considerable attention over the academic and industry circles. To date, the lack of repeatability and stability are still regarded as the critical technological barrier that hinders its broader applications especially for high-value products with demanding requirements. One significant approach to overcome this formidable challenge is in-situ monitoring combined with artificial intelligence (AI) techniques, which has been explored by great research efforts. The main goal of monitoring is to gather essential information on the process and to improve the understanding of the occurring complicated weld phenomena. This review firstly describes ongoing work on the in-situ optical sensing, behavior characterization and process modeling during dynamic LBW process. Then, much emphasis has been placed on the optical radiation techniques, such as multi-spectral photodiode, spectrometer, pyrometer and high-speed camera for observing the laser physical phenomenon including melt pool, keyhole and vapor plume. In particular, the advanced image/signal processing techniques and machine-learning models are addressed, in order to identify the correlations between process parameters, process signatures and product qualities. Finally, the major challenges and potential solutions are discussed to provide an insight on what still needs to be achieved in the field of process monitoring for metal-based LBW processes. This comprehensive review is intended to provide a reference of the state-of-the-art for those seeking to introduce intelligent welding capabilities as they improve and control the welding quality.
AB - Laser beam welding manufacturing (LBW), being a promising joining technology with superior capabilities of high-precision, good-flexibility and deep penetration, has attracted considerable attention over the academic and industry circles. To date, the lack of repeatability and stability are still regarded as the critical technological barrier that hinders its broader applications especially for high-value products with demanding requirements. One significant approach to overcome this formidable challenge is in-situ monitoring combined with artificial intelligence (AI) techniques, which has been explored by great research efforts. The main goal of monitoring is to gather essential information on the process and to improve the understanding of the occurring complicated weld phenomena. This review firstly describes ongoing work on the in-situ optical sensing, behavior characterization and process modeling during dynamic LBW process. Then, much emphasis has been placed on the optical radiation techniques, such as multi-spectral photodiode, spectrometer, pyrometer and high-speed camera for observing the laser physical phenomenon including melt pool, keyhole and vapor plume. In particular, the advanced image/signal processing techniques and machine-learning models are addressed, in order to identify the correlations between process parameters, process signatures and product qualities. Finally, the major challenges and potential solutions are discussed to provide an insight on what still needs to be achieved in the field of process monitoring for metal-based LBW processes. This comprehensive review is intended to provide a reference of the state-of-the-art for those seeking to introduce intelligent welding capabilities as they improve and control the welding quality.
KW - Behavior characterization
KW - Laser beam welding
KW - Machine learning
KW - Optical monitoring
KW - Process model
KW - Weld quality
KW - Deep penetration
KW - Electric welding
KW - High speed cameras
KW - Joining
KW - Laser beams
KW - Laser materials processing
KW - Process monitoring
KW - Welds
KW - Behaviour characterization
KW - High-precision
KW - In-situ optical monitoring
KW - Joining technology
KW - Manufacturing process
KW - Process-models
KW - Technological barriers
U2 - 10.1016/j.jmapro.2022.01.044
DO - 10.1016/j.jmapro.2022.01.044
M3 - Journal article
VL - 75
SP - 767
EP - 791
JO - Journal of Manufacturing Processes
JF - Journal of Manufacturing Processes
SN - 1526-6125
ER -