Laser welding monitoring techniques based on optical diagnosis and artificial intelligence: a review

doi:10.1007/s40436-024-00493-1

Abstract

Abstract: Laser welding is an efficient and precise joining method widely used in various industries. Real-time monitoring of the welding process is important for improving the quality of the weld products. This study provides an overview of the optical diagnostics of the laser welding process. The common welding defects and their formation mechanisms are described, starting with an introduction to the principles of laser welding. Optical signal sources are divided into radiated and external active lights, and different monitoring systems are summarized and classified. Also, the applications of artificial intelligence techniques in data processing, weld defect prediction and classification, and adaptive welding control are summarized. Finally, future research and challenges in real-time laser welding monitoring technology based on optical diagnostics are discussed. This study demonstrated that optical diagnostic techniques could acquire substantial information about the laser welding process and help identify welding defects.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00493-1

Key words: Laser welding, Real-time monitoring, Welding defect, Optical diagnosis, Artificial intelligence

Yi-Wei Huang, Xiang-Dong Gao, Perry P. Gao, Bo Ma, Yan-Xi Zhang. Laser welding monitoring techniques based on optical diagnosis and artificial intelligence: a review[J]. Advances in Manufacturing, 2025, 13(2): 337-361.

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References

[1] Aminzadeh A, Sattarpanah KS, Meiabadi MS et al (2022) A survey of process monitoring using computer-aided inspection in laser-welded blanks of light metals based on the digital twins concept. Quantum Beam Sci 6:19. https://doi.org/10.3390/qubs6020019
[2] Chen L, Yang T, Zhuang Y et al (2021) The multi-objective optimization modelling for properties of 301 stainless steel welding joints in ultra-narrow gap laser welding. Weld World 65:1333-1345
[3] Yang F (2021) Research progress of laser welding under subatmospheric pressure. Int J Adv Manuf Technol 116:803-820
[4] American WS, Kumar N, Kumar N et al (2021) A state-of-the-art review of laser welding of polymers—part I: welding parameters. Weld J 100:221-228
[5] Spöttl M, Mohrbacher H (2014) Laser-based manufacturing concepts for efficient production of tailor welded sheet metals. Adv Manuf 2:193-202
[6] Mohrbacher H, Spoettl M, Paegle J (2015) Innovative manufacturing technology enabling light weighting with steel in commercial vehicles. Adv Manuf 3:3-18
[7] Fan X, Gao X, Liu G et al (2021) Research and prospect of welding monitoring technology based on machine vision. Int J Adv Manuf Technol 115:3365-3391
[8] He B, Bai KJ (2021) Digital twin-based sustainable intelligent manufacturing: a review. Adv Manuf 9:1-21
[9] Fabbro R (2010) Melt pool and keyhole behaviour analysis for deep penetration laser welding. J Phys Appl Phys 43:445501. https://doi.org/10.1088/0022-3727/43/44/445501
[10] Zeng H, Zhou Z, Chen Y et al (2001) Wavelet analysis of acoustic emission signals and quality control in laser welding. J Laser Appl 13:167-173
[11] You DY, Gao XD, Katayama S (2014) Review of laser welding monitoring. Sci Technol Weld Join 19:181-201
[12] Cai W, Wang J, Zhou Q et al (2019) Equipment and machine learning in welding monitoring: a short review. In: Proceedings of the 5th international conference on mechatronics and robotics engineering- ICMRE’19. ACM Press, Rome
[13] Lee J, Kang M (2021) A review on the characteristics of laser welding with filler wire according to process parameters controlling the heat input and wire feeding. J Weld Join 39:167-173
[14] Ion JC (2005) Chapter 16—keyhole welding. In Ion JC (ed) laser processing of engineering materials, Butterworth-Heinemann, Oxford, pp 395-495
[15] Sibillano T, Ancona A, Berardi V et al (2007) Optical detection of conduction/keyhole mode transition in laser welding. J Mater Process Technol 191:364-367
[16] Bahador A, Hamzah E, Kondoh K et al (2018) Heat-conduction-type and keyhole-type laser welding of Ti-Ni shape-memory alloys processed by spark-plasma sintering. Mater Trans 59:835-842
[17] Coviello D, D’Angola A, Sorgente D (2022) Numerical study on the influence of the plasma properties on the keyhole geometry in laser beam welding. Front Phys 9:754672. https://doi.org/10.3389/fphy.2021.754672
[18] Svenungsson J, Choquet I, Kaplan AFH (2015) Laser welding process—a review of keyhole welding modelling. Phys Procedia 78:182-191
[19] Sibillano T, Ancona A, Berardi V et al (2006) Correlation spectroscopy as a tool for detecting losses of ligand elements in laser welding of aluminium alloys. Opt Lasers Eng 44:1324-1335
[20] Chen X (2003) Three-dimensional modelling of the laser-induced plasma plume characteristics in laser welding. J Phys Appl Phys 36:628. https://doi.org/10.1088/0022-3727/36/6/304
[21] Gong J, Peng G, Li L et al (2021) Effect of plasma plume produced by vacuum laser welding on energy transmission. Opt Laser Technol 136:106744. https://doi.org/10.1016/j.optlastec.2020.106744
[22] Brock C, Hohenstein R, Schmidt M (2011) Towards fast tracking of the keyhole geometry. Phys Procedia 12:697-703
[23] Feng Y, Gao X, Zhang Y et al (2021) Simulation and experiment for dynamics of laser welding keyhole and molten pool at different penetration status. Int J Adv Manuf Technol 112:2301-2312
[24] Gong S, Pang S, Wang H et al (2021) Laser welding basics. In: Weld pool dynamics in deep penetration laser welding. Springer, Singapore. https://doi.org/10.1007/978-981-16-0788-2_1
[25] Woizeschke P, Radel T, Nicolay P et al (2017) Laser deep penetration welding of an aluminum alloy with simultaneously applied vibrations. Lasers Manuf Mater Process 4:1-12
[26] Olsson R, Eriksson I, Powell J et al (2011) Challenges to the interpretation of the electromagnetic feedback from laser welding. Opt Lasers Eng 49:188-194
[27] Wang T, Gao X, Seiji K et al (2012) Study of dynamic features of surface plasma in high-power disk laser welding. Plasma Sci Technol 14:245-251
[28] Okabe T, Yasuda K, Nakata K (2016) Dynamic observations of welding phenomena and finite element analysis in high-frequency electric resistance welding. Weld Int 30:835-845
[29] Auwal ST, Ramesh S, Yusof F et al (2018) A review on laser beam welding of copper alloys. Int J Adv Manuf Technol 96:475-490
[30] Allen TR, Huang W, Tanner JR et al (2020) Energy-coupling mechanisms revealed through simultaneous keyhole depth and absorptance measurements during laser-metal processing. Phys Rev Appl 13:064070. https://doi.org/10.1103/PhysRevApplied.13.064070
[31] Parab ND, Zhao C, Cunningham R et al (2019) High-speed synchrotron X-ray imaging of laser powder bed fusion process. Synchrotron Radiat News 32:4-8
[32] Zhao H, Debroy T (2001) Pore formation during laser beam welding of die-cast magnesium alloy AM60B—mechanism and remedy. Weld J Miami Fla 80:204-210
[33] Katayama S (2020) Fundamentals and details of laser welding. Springer, Singapore
[34] Sheikhi M, Malek GF, Assadi H (2015) Prediction of solidification cracking in pulsed laser welding of 2024 aluminum alloy. Acta Mater 82:491-502
[35] Lippold JC (1994) Solidification behavior and cracking susceptibility of pulsed-laser welds in austenitic stainless steels. Weld J 73(6):129-139
[36] Kim HT, Nam SW (1996) Solidification cracking susceptibility of high strength aluminum alloy weldment. Scr Mater 34:1139-1145
[37] Zhang M, Zhang Z, Tang K et al (2018) Analysis of mechanisms of underfill in full penetration laser welding of thick stainless steel with a 10 kW fiber laser. Opt Laser Technol 98:97-105
[38] Fang X, Liu H, Zhang J (2015) Reducing the under fill rate of pulsed laser welding of titanium alloy through the application of a transversal pre-extrusion load. J Mater Process Technol 220:124-134
[39] Zhu B, Zhang G, Zou J et al (2021) Melt flow regularity and hump formation process during laser deep penetration welding. Opt Laser Technol 139:106950. https://doi.org/10.1016/j.optlastec.2021.106950
[40] Xiao X, Liu X, Cheng M et al (2020) Towards monitoring laser welding process via a coaxial pyrometer. J Mater Process Technol 277:116409. https://doi.org/10.1016/j.jmatprotec.2019.116409
[41] Doong JL, Wu CS, Hwang JR (1991) Infrared temperature sensing of laser welding. Int J Mach Tools Manuf 31:607-616
[42] Nair AM, Muvvala G, Sarkar S et al (2020) Real-time detection of cooling rate using pyrometers in tandem in laser material processing and directed energy deposition. Mater Lett 277:128330. https://doi.org/10.1016/j.matlet.2020.128330
[43] Stehr T, Hermsdorf J, Henning T et al (2010) Closed loop control for laser micro spot welding using fast pyrometer systems. Phys Procedia 5:465-471
[44] Gade R, Moeslund TB (2014) Thermal cameras and applications: a survey. Mach Vis Appl 25:245-262
[45] Al-Karawi J, Schmidt J (2004) Application of infrared thermography to the analysis of welding processes. In: Proceedings of the 2004 international conference on quantitative infrared thermography, QIRT Council, Belgium, July 5-8, H.9.1-H.9.6
[46] Mathieu A, Matteï S, Deschamps A et al (2006) Temperature control in laser brazing of a steel/aluminium assembly using thermographic measurements. NDT E Int 39:272-276
[47] Chen Z, Gao X (2014) Detection of weld pool width using infrared imaging during high-power fiber laser welding of type 304 austenitic stainless steel. Int J Adv Manuf Technol 74:1247-1254
[48] Zhou J, Xia G, Zhou Z et al (2023) Comparison study of 6082 Al alloy laser welding using 455 nm blue laser and 1080 nm near-infrared laser. Optik 272:170224. https://doi.org/10.1016/j.ijleo.2022.170224
[49] Yu R, Guo S, Huang Y et al (2023) Prediction of variable-groove weld penetration using texture features of infrared thermal images and machine learning methods. J Mater Res Technol 23:1039-1051
[50] Vakili-Farahani F, Lungershausen J, Wasmer K (2017) Wavelet analysis of light emission signals in laser beam welding. J Laser Appl 29:022424. https://doi.org/10.2351/1.4983507
[51] Sanders PG, Leong KH, Keske JS et al (1998) Real-time monitoring of laser beam welding using infrared weld emissions. J Laser Appl 10:205-211
[52] Geiger M, Kägeler C, Schmidt M (2008) High-power laser welding of contaminated steel sheets. Prod Eng 2:235-240
[53] Zhang Y, Gao X, You D et al (2019) A low-cost welding status monitoring framework for high-power disk laser welding. IEEE Access 7:17365-17376
[54] Connolly JO, Beirne GJ, O’Connor GM et al (2000) Optical monitoring of laser generated plasma during laser welding. In: Haglund RF, Wood RF (eds) Laser plasma generation and diagnostics, SPIE-The International Society for Optical Engineering, Bellingham
[55] Bruncko J, Uherek F (2003) Michalka M (2003) Monitoring of laser welding processes by optical emission spectroscopy. In: Hrabovsky M, Senderakova D, Tomanek P (eds) Photonics, devices, and systems I, SPIE-The International Society for Optical Engineering, Bellingham
[56] Sebestova H, Chmelickova H, Nozka L et al (2012) Non-destructive real time monitoring of the laser welding process. J Mater Eng Perform 21:764-769
[57] Sibillano T, Rizzi D, Mezzapesa FP et al (2012) Closed loop control of penetration depth during CO₂ laser lap welding processes. Sensors 12:11077-11090
[58] Saeed G, Zhang YM (2007) Weld pool surface depth measurement using a calibrated camera and structured light. Meas Sci Technol 18:2570. https://doi.org/10.1088/0957-0233/18/8/033
[59] Wang L, Mohammadpour M, Gao X et al (2021) Adjustable ring mode (ARM) laser welding of stainless steels. Opt Lasers Eng 137:106360. https://doi.org/10.1016/j.optlaseng.2020.106360
[60] Wang L, Mohammadpour M, Yang B et al (2020) Monitoring of keyhole entrance and molten pool with quality analysis during adjustable ring mode laser welding. Appl Opt 59:1576-1584
[61] Zhang Y, Gao X (2014) Analysis of characteristics of molten pool using cast shadow during high-power disk laser welding. Int J Adv Manuf Technol 70:1979-1988
[62] Wang J, Wang C, Meng X et al (2012) Study on the periodic oscillation of plasma/vapour induced during high power fibre laser penetration welding. Opt Laser Technol 44:67-70
[63] Sun D, Cai Y, Wang Y et al (2014) Effect of He-Ar ratio of side assisting gas on plasma 3D formation during CO₂ laser welding. Opt Lasers Eng 56:41-49
[64] Gao X, Wen Q, Katayama S (2013) Analysis of high-power disk laser welding stability based on classification of plume and spatter characteristics. Trans Nonferrous Met Soc China 23:3748-3757
[65] Tenner F, Brock C, Klämpfl F et al (2015) Analysis of the correlation between plasma plume and keyhole behavior in laser metal welding for the modeling of the keyhole geometry. Opt Lasers Eng 64:32-41
[66] Zhang X, Tang Z, Wu Y et al (2022) Progress in in situ X-ray imaging of welding process. Rev Sci Instrum 93:071501. https://doi.org/10.1063/5.0074042
[67] Boley M, Abt F, Weber R et al (2013) X-ray and optical videography for 3D measurement of capillary and melt pool geometry in laser welding. Phys Procedia 41:488-495
[68] Kaplan AFH, Mizutani M, Katayama S et al (2003) On the mechanism of pore formation during keyhole laser spot welding. In: Miyamoto I, Kobayashi KF, Sugioka K et al (eds) First international symposium on high-power laser macroprocessing, SPIE-the International Society for Optics and Photonics, Bellingham
[69] Mai TA, Spowage AC (2004) Characterisation of dissimilar joints in laser welding of steel-kovar, copper-steel and copper-aluminium. Mater Sci Eng A 374:224-233
[70] Yamada T, Shobu T, Nishimura A et al (2012) In-situ X-ray observation of molten pool depth during laser micro welding. J Laser Micro Nanoeng 7:244-248
[71] Heider A, Sollinger J, Abt F et al (2013) High-speed X-ray analysis of spatter formation in laser welding of copper. Phys Procedia 41:112-118
[72] Zhan X, Bu X, Qin T et al (2017) X-ray online detection for laser welding T-joint of Al-Li alloy. Mod Phys Lett B 31:1750154. https://doi.org/10.1142/S0217984917501548
[73] Cunningham R, Zhao C, Parab N et al (2019) Keyhole threshold and morphology in laser melting revealed by ultrahigh-speed X-ray imaging. Science 363(6429):849-852
[74] Dupriez ND, Denkl A (2017) Advances of OCT technology for laser beam processing: precision and quality during laser welding. Laser Tech J 14:34-38
[75] Boley M, Fetzer F, Weber R et al (2019) Statistical evaluation method to determine the laser welding depth by optical coherence tomography. Opt Lasers Eng 119:56-64
[76] Lohaus L, Bautze T, Diepold K (2010) Evaluation of optical sensors for laser welding in a technical cognitive environment. In: International congress on applications of lasers & electro-optics. Laser Institute of America, Anaheim, California, USA. https://doi.org/10.2351/1.5062015
[77] Schmitt R, Mallmann G, Devrient M et al (2014) 3D polymer weld seam characterization based on optical coherence tomography for laser transmission welding applications. Phys Procedia 56:1305-1314
[78] Dorsch F, Harrer T, Haug P et al (2016) Process control using capillary depth measurement. In: International congress on applications of lasers & electro-optics. Laser Institute of America, San Diego, California, USA. https://doi.org/10.2351/1.5118550
[79] Dorsch F, Dubitzky W, Effing L et al (2017) Capillary depth measurement for process control. In: Kaierle S, Heinemann SW(eds) High-power laser materials processing: applications, diagnostics, and systems vol 10097, SPIE, San Francisco, California, United States, p 1009708
[80] Schmoeller M, Stadter C, Liebl S et al (2019) Inline weld depth measurement for high brilliance laser beam sources using optical coherence tomography. J Laser Appl 31:022409. https://doi.org/10.2351/1.5096104
[81] Mittelstädt C, Mattulat T, Seefeld T et al (2019) Novel approach for weld depth determination using optical coherence tomography measurement in laser deep penetration welding of aluminum and steel. J Laser Appl 31:022007. https://doi.org/10.2351/1.5082263
[82] Authier N, Touzet E, Lücking F et al (2020) Coupled membrane free optical microphone and optical coherence tomography keyhole measurements to setup welding laser parameters. In: Kaierle S, Heinemann SW (eds) High-power laser materials processing: applications, diagnostics, and systems, vol 9, SPIE, San Francisco
[83] Sokolov M, Franciosa P, Sun T et al (2021) Applying optical coherence tomography for weld depth monitoring in remote laser welding of automotive battery tab connectors. J Laser Appl 33:012028. https://doi.org/10.2351/7.0000336
[84] Ma D, Jiang P, Shu L et al (2023) DBN-based online identification of porosity regions during laser welding of aluminum alloys using coherent optical diagnosis. Opt Laser Technol 165:109597. https://doi.org/10.1016/j.optlastec.2023.109597
[85] Gao X, Chen Y (2014) Detection of micro gap weld using magneto-optical imaging during laser welding. Int J Adv Manuf Technol 73:23-33
[86] Gao X, Huang G, You D et al (2017) Magneto-optical imaging deviation model of micro-gap weld joint. J Manuf Syst 42:82-92
[87] Gao X, Mo L, You D et al (2017) Tight butt joint weld detection based on optical flow and particle filtering of magneto-optical imaging. Mech Syst Signal Process 96:16-30
[88] Li Y, Gao X, Chen Y et al (2021) Modeling for tracking micro gap weld based on magneto-optical sensing and kalman filtering. IEEE Sens J 21:11598-11614
[89] You D, Gao X, Katayama S (2016) Data-driven based analyzing and modeling of MIMO laser welding process by integration of six advanced sensors. Int J Adv Manuf Technol 82:1127-1139
[90] Norman P, Engstroem H, Kaplan AFH (2008) Theoretical analysis of photodiode monitoring of laser welding defects by imaging combined with modelling. J Phys Appl Phys 41:195502. https://doi.org/10.1088/0022-3727/41/19/195502
[91] Liu W, Liu S, Ma J et al (2014) Real-time monitoring of the laser hot-wire welding process. Opt Laser Technol 57:66-76
[92] Li S, Chen G, Katayama S et al (2014) Relationship between spatter formation and dynamic molten pool during high-power deep-penetration laser welding. Appl Surf Sci 303:481-488
[93] You D, Gao X, Katayama S (2015) A Novel stability quantification for disk laser welding by using frequency correlation coefficient between multiple-optics signals. IEEE/ASME Trans Mechatron 20:327-337
[94] Volpp J (2017) Keyhole stability during laser welding—part II: process pores and spatters. Prod Eng 11:9-18
[95] Liu G, Gao X, You D et al (2019) Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors. J Intell Manuf 30:821-832
[96] von Witzendorff P, Kaierle S, Suttmann O et al (2015) Using pulse shaping to control temporal strain development and solidification cracking in pulsed laser welding of 6082 aluminum alloys. J Mater Process Technol 225:162-169
[97] Gao X, You D, Katayama S (2012) Infrared image recognition for seam tracking monitoring during fiber laser welding. Mechatronics 22:370-380
[98] You D, Gao X, Katayama S (2014) Monitoring of high-power laser welding using high-speed photographing and image processing. Mech Syst Signal Process 49:39-52
[99] Olsson R, Eriksson I, Powell J et al (2011) Advances in pulsed laser weld monitoring by the statistical analysis of reflected light. Opt Lasers Eng 49:1352-1359
[100] Wu D, Huang Y, Zhang P et al (2020) Visual-acoustic penetration recognition in variable polarity plasma arc welding process using hybrid deep learning approach. IEEE Access 8:120417-120428
[101] Park YW, Park H, Rhee S et al (2002) Real time estimation of CO₂ laser weld quality for automotive industry. Opt Laser Technol 34:135-142
[102] Bardin F, Cobo A, Lopez-Higuera JM et al (2005) Optical techniques for real-time penetration monitoring for laser welding. Appl Opt 44:3869-3876
[103] Mrňa L, Šarbort M, Řeřucha Š et al (2013) Correlation between the keyhole depth and the frequency characteristics of light emissions in laser welding. Phys Procedia 41:469-477
[104] Wu D, Zhang P, Yu Z et al (2022) Progress and perspectives of in-situ optical monitoring in laser beam welding: sensing, characterization and modeling. J Manuf Process 75:767-791
[105] Fang J, Li L, Chen Y et al (2005) Wavelet analysis of plasma optical signals at pool penetration in laser welding. In: Mu G, Yu FTS, Jutamulia S (eds) Proceedings of information optics and photonics technology, vol 5642, SPIE, Beijing, 2005
[106] Sibillano T, Ancona A, Rizzi D et al (2010) Plasma plume oscillations monitoring during laser welding of stainless steel by discrete wavelet transform application. Sensors 10:3549-3561
[107] Zhang Y, You D, Gao X et al (2019) Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of thick plates. J Manuf Syst 51:87-94
[108] Park H, Rhee S, Kim D (2001) A fuzzy pattern recognition based system for monitoring laser weld quality. Meas Sci Technol 12:1318. https://doi.org/10.1088/0957-0233/12/8/345
[109] You DY, Gao XD, Katayama S (2014) Multisensor fusion system for monitoring high-power disk laser welding using support vector machine. IEEE Trans Ind Inform 10:1285-1295
[110] You D, Gao X, Katayama S (2015) WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM. IEEE Trans Ind Electron 62:628-636
[111] Cai W, Jiang P, Shu L et al (2022) Real-time identification of molten pool and keyhole using a deep learning-based semantic segmentation approach in penetration status monitoring. J Manuf Process 76:695-707
[112] Cao L, Li J, Zhang L et al (2023) Cross-attention-based multi-sensing signals fusion for penetration state monitoring during laser welding of aluminum alloy. Knowl Based Syst 261:110212. https://doi.org/10.1016/j.knosys.2022.110212
[113] Kawahito Y, Katayama S (2004) In-process monitoring and feedback control during laser microspot lap welding of copper sheets. J Laser Appl 16:121-127
[114] Kawahito Y, Ohnishi T, Katayama S (2009) In-process monitoring and feedback control for stable production of full-penetration weld in continuous wave fibre laser welding. J Phys Appl Phys 42:085501. https://doi.org/10.1088/0022-3727/42/8/085501
[115] de Graaf MW, Benneker JO, Aarts RGKM et al (2005) Robust process-controller for Nd:YAG welding. In: Proceedings of 24th international congress on applications of lasers & electro-optics 2005 (ICALEO05), AIP Publishing, Miami
[116] Jauregui JM, Aalderink BJ, Aarts RGKM et al (2008) Design, implementation and testing of a fuzzy control scheme for laser welding. J Laser Appl 20:146-153
[117] Konuk AR, Aarts R, Veld BH et al (2011) Closed loop control of laser welding using an optical spectroscopic sensor for Nd:YAG and CO₂ lasers. In: Proc. Conf. ICALEO 2011, Orlando, USA, 2011, Laser Institute of America, pp 85-94
[118] Gao X, Sun Y, Katayama S (2014) Neural network of plume and spatter for monitoring high-power disk laser welding. Int J Precis Eng Manuf-Green Technol 1:293-298
[119] Zhang K, Li D, Gui H et al (2018) Adaptive control for laser welding with filler wire of marine high strength steel with tight butt joints for large structures. J Manuf Process 36:434-441
[120] Kos M, Arko E, Kosler H et al (2019) Remote-laser welding system with in-line adaptive 3D seam tracking and power control. Procedia CIRP 81:1189-1194
[121] Baraniuk RG (2011) More is less: signal processing and the data deluge. Science 331(6018):717-719
[122] Kouraytem N, Li X, Tan W et al (2021) Modeling process-structure-property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches. J Phys Mater 4:032002. https://doi.org/10.1088/2515-7639/abca7b