Skip to main content
SpringerLink
Log in

Penetration recognition based on machine learning in arc welding: a review

  • Critical Review
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Penetration recognition is the critical technology to improve manufacturing quality and automation level in arc welding. In this paper, recent research advances concerning penetration recognition based on machine learning are comprehensively reviewed, focused on signals feature extraction and application of machine learning in this field. Three types of signals originating from arc welding, namely, weld pool image, arc sound, and arc voltage, are used to analyze the correlation with the penetration state and extract effective features. Next, the following contents briefly introduce some machine learning methods including conventional machine learning and deep learning, and emphatically summarize their application and performance in penetration recognition. Notably, the deep learning method possesses higher classification accuracy and better generalization ability in penetration recognition due to its deeper architecture and effective learning capacity. In the end, some challenges and tendencies, involving multi-sensor information fusion, data-driven recognition, model development incorporating attention mechanism, lightweight model deployment, and real-time control, are presented for further research. This paper is an attempt to provide a reference source and some guidance for researchers who use machine learning methods for penetration monitoring.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Ogbemhe J, Mpofu K, Tlale N, Ramatsetse B (2019) Application of robotics in rail car manufacturing learning factory: a case of welding complex joints. Procedia Manuf 31:316–322

    Article  Google Scholar 

  2. Maiolino P, Woolley R, Branson D, Benardos P, Popov A, Ratchev S (2017) Flexible robot sealant dispensing cell using RGB-D sensor and off-line programming. Robot Comput Integr Manuf 48:188–195

    Article  Google Scholar 

  3. Liu YK, Zhang WJ, Zhang YM (2014) A tutorial on learning human welder’s behavior: sensing, modeling, and control. J Manuf Process 16:123–136. https://doi.org/10.1016/j.jmapro.2013.09.004

    Article  Google Scholar 

  4. Zhang Y-M (2008) 1 - An analysis of welding process monitoring and control. In: Zhang YBT-R-TWPM (ed) Woodhead publishing series in welding and other joining technologies. Woodhead Publishing, pp 1–11

  5. Zhang G, Wu CS, Liu X (2015) Single vision system for simultaneous observation of keyhole and weld pool in plasma arc welding. J Mater Process Technol 215:71–78

    Article  Google Scholar 

  6. Leonardo BQ, Steffens CR, Filho SCDS, Mor JL, Botelho SSDC (2016) Vision-based system for welding groove measurements for robotic welding applications. In: Proceedings - IEEE International Conference on Robotics and Automation, pp 5650–5655. https://doi.org/10.1109/ICRA.2016.7487785

  7. Lv N, Zhong J, Chen H, Lin T, Chen S (2014) Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length. Int J Adv Manuf Technol 74:235–249

    Article  Google Scholar 

  8. Peng Y, Xu G, Gu X, Zhou G, Tian Y (2017) A low-cost infrared sensing system for monitoring the MIG welding process. Int J Adv Manuf Technol 92:4031–4038

    Article  Google Scholar 

  9. Rumelhart D, Hinton GE, Williams RJ (1986) Learning representations by back propagating errors. Nature 323:533–536

    Article  MATH  Google Scholar 

  10. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  11. Cortes C, Vapnik V (1995) Support-Vector Networks. Mach Learn 20:273–297

    Article  MATH  Google Scholar 

  12. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527

    Article  MathSciNet  MATH  Google Scholar 

  13. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  14. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv Prepr arXiv14123555

  15. Lü X, Gu D, Wang Y, Qu Y, Qin C, Huang F (2018) Feature extraction of welding seam image based on laser vision. IEEE Sens J 18:4715–4724

    Article  Google Scholar 

  16. Kovacevic R, Zhang YM (1996) Sensing free surface of arc weld pool using specular reflection: principle and analysis. Proc Inst Mech Eng Part B J Eng Manuf 210:553–564

    Article  Google Scholar 

  17. Pietrzak KA, Packer SM (1994) Vision-based weld pool width control. J Manuf Sci Eng Trans ASME 116:86–92. https://doi.org/10.1115/1.2901813

    Article  Google Scholar 

  18. Wu CS, Gao JQ (2006) Vision-based neuro-fuzzy control of weld penetration in gas tungsten arc welding of thin sheets. Int J Model Identif Control 1:126–132

    Article  Google Scholar 

  19. Zhang Y, Zhao Z, Zhang Y, Bai L, Wang K, Han J (2019) Online weld pool contour extraction and seam width prediction based on mixing spectral vision. Opt Rev 26:65–76

    Article  Google Scholar 

  20. Fan C, Lv F, Chen S (2009) Visual sensing and penetration control in aluminum alloy pulsed GTA welding. Int J Adv Manuf Technol 42:126–137

    Article  Google Scholar 

  21. Richardson RW, Gutow DA, Anderson RA, Farson DF (1984) Coaxial arc weld pool viewing for process monitoring and control. Weld J 63:43–50

    Google Scholar 

  22. Jiang C, Zhang F, Wang Z (2017) Image processing of aluminum alloy weld pool for robotic VPPAW based on visual sensing. IEEE Access 5:21567–21573. https://doi.org/10.1109/ACCESS.2017.2761986

    Article  Google Scholar 

  23. Chen C, Lv N, Chen S (2021) Welding penetration monitoring for pulsed GTAW using visual sensor based on AAM and random forests. J Manuf Process 63:152–162. https://doi.org/10.1016/j.jmapro.2020.04.005

    Article  Google Scholar 

  24. Huang J, Xue L, Huang J, Zou Y, Ma K (2019) Penetration estimation of GMA backing welding based on weld pool geometry parameters. Chinese J Mech Eng 32:1–11. https://doi.org/10.1186/s10033-019-0366-2

    Article  Google Scholar 

  25. Wu CS, Gao JQ, Zhang M (2004) Sensing weld pool geometrical appearance in gas—metal arc welding. Proc Inst Mech Eng Part B J Eng Manuf 218:813–818

    Article  Google Scholar 

  26. Zhang W, Liu Y, Wang X, Zhang YM (2012) Characterization of three-dimensional weld pool surface in GTAW. Weld J 91:195s–203s

    Google Scholar 

  27. Saeed G, Zhang YM (2007) Weld pool surface depth measurement using a calibrated camera and structured light. Meas Sci Technol 18:2570–2578. https://doi.org/10.1088/0957-0233/18/8/033

    Article  Google Scholar 

  28. Zhang YM, Kovacevic R, Wu L (1992) Sensitivity of front-face weld geometry in representing the full penetration. Proc Inst Mech Eng Part B J Eng Manuf 206:191–197

    Article  Google Scholar 

  29. Yang L, Li E, Long T, Fan J, Mao Y, Fang Z, Liang Z (2018) A welding quality detection method for arc welding robot based on 3D reconstruction with SFS algorithm. Int J Adv Manuf Technol 94:1209–1220

    Article  Google Scholar 

  30. Li L, Yang X, Zhang F, Lin T (2011) Research on surface recover of aluminum alloy PGTAW pool based on SFS. In: Robotic welding, intelligence and automation. Springer, pp 307–314

    Chapter  Google Scholar 

  31. Mnich C, Al-Bayat F, Debrunner C, Steele J, Vincent T (2004) In situ weld pool measurement using stereovision. In: Proceedings of 2004 Japan–USA Symposium on flexible automation ASME, pp 1–2

  32. Bastos TF, Calderón L, Martin JM, Ceres R (1996) Ultrasonic sensors and arc welding - a noisy mix. Sens Rev 16:26–32. https://doi.org/10.1108/02602289610123530

    Article  Google Scholar 

  33. Arata Y, Inoue K, Futamata M, Toh T (1979) Investigation on welding arc sound (Report I): effect of welding method and welding condition of welding arc sound (welding physics, processes & instruments). Trans JWRI 8:25–31

    Google Scholar 

  34. Tarn J, Huissoon J (2005) Developing psycho-acoustic experiments in gas metal arc welding. In: IEEE International Conference Mechatronics and Automation, 2005. IEEE, pp 1112–1117

    Chapter  Google Scholar 

  35. Kaskinen P, Mueller G (1986) Acoustic arc length control. In: Proceedings of the international conference “Advances in welding science and technology”. Gatlinburg, pp 763–765

  36. Wang JF, Chen B, Chen HB, Chen SB (2009) Analysis of arc sound characteristics for gas tungsten argon welding. Sens Rev 29:240–249. https://doi.org/10.1108/02602280910967657

    Article  Google Scholar 

  37. Lv N, Xu YL, Fang G, Yu XW, Chen SB (2016) Research on welding penetration state recognition based on BP-Adaboost model for pulse GTAW welding dynamic process. In: 2016 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO). IEEE, pp 100–105

    Chapter  Google Scholar 

  38. Pal K, Pal SK (2011) Monitoring of weld penetration using arc acoustics. Mater Manuf Process 26:684–693. https://doi.org/10.1080/10426910903496813

    Article  Google Scholar 

  39. Liu L, Lan H, Zheng H, Yu Z (2010) Relationship between arc sound signal and penetration status in MIG welding. Jixie Gongcheng Xuebao(Chinese). J Mech Eng 46:79–84

    Article  Google Scholar 

  40. Gao Y, Zhao J, Wang Q, Xiao J, Zhang H (2020) Weld bead penetration identification based on human-welder subjective assessment on welding arc sound. Measurement 154:107475

    Article  Google Scholar 

  41. Liu L, Lan H, Zheng H, Jian X (2012) Feature extraction and dimensionality reduction of arc sound under typical penetration status in metal inert gas welding. Chinese J Mech Eng 25:293–298

    Article  Google Scholar 

  42. Wang JF, Yu HD, Qian YZ, Yang RZ, Chen SB (2011) Feature extraction in welding penetration monitoring with arc sound signals. Proc Inst Mech Eng Part B J Eng Manuf 225:1683–1691

    Article  Google Scholar 

  43. Zhang HH, Lv N, Ben CS (2015) Study on the relationship between the energy in most effective frequency range of arc sound signal and the change of arc height in pulsed Al alloy GTAW process. Adv Intell Syst Comput 363:385–399. https://doi.org/10.1007/978-3-319-18997-0_33

    Article  Google Scholar 

  44. Zhang Z, Chen S (2017) Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals. J Intell Manuf 28:207–218

    Article  Google Scholar 

  45. Ren W, Wen G, Liu S, Yang Z, Xu B, Zhang Z (2018) Seam penetration recognition for GTAW using convolutional neural network based on time-frequency image of arc sound. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, pp 853–860

    Chapter  Google Scholar 

  46. Ren W, Wen G, Xu B, Zhang Z (2020) A novel convolutional neural network based on time–frequency spectrogram of arc sound and its application on GTAW penetration classification. IEEE Trans Ind Informatics 17:809–819

    Article  Google Scholar 

  47. Lv N, Xu Y, Fang G, Zhao H, Chen S (2014) Mechanism analysis and feature extraction of arc sound channel for pulse GTAW welding dynamic process. In: International Conference on Robotic Welding, Intelligence and Automation. Springer, pp 249–261

    Google Scholar 

  48. Yujin Y, Peihua Z, Qun Z (2010) Research of speaker recognition based on combination of LPCC and MFCC. In: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems. IEEE, pp 765–767

    Chapter  Google Scholar 

  49. Wang J, Zuo Y, Huang Y, Yang B, Pan S (2011) Arc sound recogniting penetration state using LPCC features. In: Robotic Welding, Intelligence and Automation. Springer, pp 229–233

    Chapter  Google Scholar 

  50. Cui Y, Shi Y, Zhu T, Cui S (2020) Welding penetration recognition based on arc sound and electrical signals in K-TIG welding. Measurement 163:107966

    Article  Google Scholar 

  51. Wang Q, Gao Y, Huang L, Gong Y, Xiao J (2019) Weld bead penetration state recognition in GMAW process based on a central auditory perception model. Measurement 147:106901

    Article  Google Scholar 

  52. Kanti KM, Rao PS (2008) Prediction of bead geometry in pulsed GMA welding using back propagation neural network. J Mater Process Technol 200:300–305

    Article  Google Scholar 

  53. Li XR, Shao Z, Zhang YM, Kvidahl L (2013) Monitoring and control of penetration in GTAW and pipe welding. Weld J 92:190s–196s

    Google Scholar 

  54. Bicknell A, Smith JS, Lucas J (1994) Arc voltage sensor for monitoring of penetration in TIG welds. IEE Proceedings-Science, Meas Technol 141:513–520

    Article  Google Scholar 

  55. Cheng Y, Xiao J, Chen S, Zhang Y (2018) Intelligent penetration welding of thin-plate gtaw process based on arc voltage feedback. Hanjie Xuebao/Trans China Weld Inst 39:1–4

    Google Scholar 

  56. Wang QL, Yang CL, Geng Z (1993) Separately excited resonance phenomenon of the weld pool and its application. Weld J 72:455-s

    Google Scholar 

  57. Wang Z, Zhang YM, Wu L (2010) Measurement and estimation of weld pool surface depth and weld penetration in pulsed gas metal arc welding. Weld J 89:117s–126s

    Google Scholar 

  58. Zou S, Wang Z, Hu S, Wang W, Cao Y (2020) Control of weld penetration depth using relative fluctuation coefficient as feedback. J Intell Manuf 31:1203–1213

    Article  Google Scholar 

  59. Chen Z, Chen J, Feng Z (2018) Welding penetration prediction with passive vision system. J Manuf Process 36:224–230. https://doi.org/10.1016/j.jmapro.2018.10.009

    Article  Google Scholar 

  60. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  61. Zhang B, Shi Y, Cui Y, Wang Z, Hong X (2021) Prediction of keyhole TIG weld penetration based on high-dynamic range imaging. J Manuf Process 63:179–190. https://doi.org/10.1016/j.jmapro.2020.03.053

    Article  Google Scholar 

  62. Thekkuden DT, Mourad A-HI (2019) Investigation of feed-forward back propagation ANN using voltage signals for the early prediction of the welding defect. SN Appl Sci 1:1–17

    Article  Google Scholar 

  63. Chen B, Wang J, Chen S (2009) Modeling of pulsed GTAW based on multi-sensor fusion. Sens Rev 29:223–232. https://doi.org/10.1108/02602280910967639

    Article  Google Scholar 

  64. Lv N, Xu Y, Li S, Yu X, Chen S (2017) Automated control of welding penetration based on audio sensing technology. J Mater Process Technol 250:81–98. https://doi.org/10.1016/j.jmatprotec.2017.07.005

    Article  Google Scholar 

  65. Chang B, Huang J (2020) Discrimination of molten pool penetration based on genetic algorithm optimization of BP neural network. In: Journal of Physics: Conference Series. IOP Publishing, p 12110

    Google Scholar 

  66. Gao X, Lin J, Xiao Z, Chen X (2016) Recognition model of arc welding penetration using ICA-BP neural network. Trans China Weld Inst 37(5):33–36

    Google Scholar 

  67. Wang XW, Li RR (2012) GBAMPSO-BPNN based penetration prediction model for GTAW. Shanghai Jiaotong Daxue Xuebao/Journal Shanghai Jiaotong Univ 46:76–79+86 (in Chinese)

  68. Chang Y, Yue J, Guo R, Liu W, Li L (2020) Penetration quality prediction of asymmetrical fillet root welding based on optimized BP neural network. J Manuf Process 50:247–254. https://doi.org/10.1016/j.jmapro.2019.12.022

    Article  Google Scholar 

  69. Wu D, Chen H, He Y, Song S, Lin T, Chen S (2016) A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine. Sens Rev 36:257–266. https://doi.org/10.1108/SR-01-2016-0009

    Article  Google Scholar 

  70. Wu D, Chen H, Huang Y, Chen S (2018) Online monitoring and model-free adaptive control of weld penetration in VPPAW based on extreme learning machine. IEEE Trans Ind Informatics 15:2732–2740. https://doi.org/10.1109/TII.2018.2870933

    Article  Google Scholar 

  71. Wu D, Chen J, Liu H, Zhang P, Yu Z, Chen H, Chen S (2019) Weld penetration in situ prediction from keyhole dynamic behavior under time-varying VPPAW pools via the OS-ELM model. Int J Adv Manuf Technol 104:3929–3941

    Article  Google Scholar 

  72. Liang N-Y, Huang G-B, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans neural networks 17:1411–1423

    Article  Google Scholar 

  73. Feng B, Qin K, Jiang Z (2018) ELM with L1/L2 regularization constraints. Hanjie Xuebao/Transactions China Weld Inst 39:31–35 (in Chinese)

  74. Allwein EL, Schapire RE, Singer Y (2000) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1:113–141

    MathSciNet  MATH  Google Scholar 

  75. Liang R, Yu R, Luo Y, Zhang Y (2019) Machine learning of weld joint penetration from weld pool surface using support vector regression. J Manuf Process 41:23–28

    Article  Google Scholar 

  76. Liu L, Chen H, Chen S (2019) Online monitoring of variable polarity tig welding penetration state based on fusion of welding characteristic parameters and SVM. In: Transactions on Intelligent Welding Manufacturing. Springer, pp 87–104

    Chapter  Google Scholar 

  77. Cheng C, Li H-F, Bao C-H (2016) Hybrid artificial fish algorithm to solve TSP problem. In: Proceedings of the 6th international Asia conference on industrial engineering and management innovation. Springer, pp 275–285

    Chapter  Google Scholar 

  78. Zhu T, Shi Y, Cui S, Cui Y (2019) Recognition of weld penetration during K-TIG welding based on acoustic and visual sensing. Sens Imaging 20:1–21. https://doi.org/10.1007/s11220-018-0224-9

    Article  Google Scholar 

  79. Chen X-W, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525

    Article  Google Scholar 

  80. Wu D (2018) Research on predicting and intelligent control for weld formation during VPPAW process using multi-information fusion. Shanghai Jiao Tong University

    Google Scholar 

  81. Huang HB, Li RX, Yang ML, Lim TC, Ding WP (2017) Evaluation of vehicle interior sound quality using a continuous restricted Boltzmann machine-based DBN. Mech Syst Signal Process 84:245–267

    Article  Google Scholar 

  82. Wu D, Huang Y, Chen H, He Y, Chen S (2017) VPPAW penetration monitoring based on fusion of visual and acoustic signals using t-SNE and DBN model. Mater Des 123:1–14

    Article  Google Scholar 

  83. Zhang Y, You D, Gao X, Katayama S (2019) Online monitoring of welding status based on a DBN model during laser welding. Engineering 5:671–678

    Article  Google Scholar 

  84. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  85. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  86. Nomura K, Fukushima K, Matsumura T, Asai S (2021) Burn-through prediction and weld depth estimation by deep learning model monitoring the molten pool in gas metal arc welding with gap fluctuation. J Manuf Process 61:590–600. https://doi.org/10.1016/j.jmapro.2020.10.019

    Article  Google Scholar 

  87. Wu D, Hu M, Huang Y, Zhang P, Yu Z (2021) In situ monitoring and penetration prediction of plasma arc welding based on welder intelligence-enhanced deep random forest fusion. J Manuf Process 66:153–165. https://doi.org/10.1016/j.jmapro.2021.04.007

    Article  Google Scholar 

  88. Wang Q, Jiao W, Zhang YM (2020) Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control. J Manuf Syst 57:429–439. https://doi.org/10.1016/j.jmsy.2020.10.002

    Article  Google Scholar 

  89. Cheng Y, Chen S, Xiao J, Zhang YM (2021) Dynamic estimation of joint penetration by deep learning from weld pool image. Sci Technol Weld Join 26:279–285. https://doi.org/10.1080/13621718.2021.1896141

    Article  Google Scholar 

  90. Xia C, Pan Z, Fei Z, Zhang S, Li H (2020) Vision based defects detection for Keyhole TIG welding using deep learning with visual explanation. J Manuf Process 56:845–855. https://doi.org/10.1016/j.jmapro.2020.05.033

    Article  Google Scholar 

  91. Wang Z, Li L, Chen H, Lin S, Wu J, Ding T, Tian J, Xu M (2022) Recognition of GTAW weld penetration based on the lightweight model and transfer learning. Weld World. https://doi.org/10.1007/s40194-022-01396-0

  92. Feng Y, Chen Z, Wang D, Chen J, Feng Z (2020) DeepWelding: a deep learning enhanced approach to GTAW using multisource sensing images. IEEE Trans Ind Informatics 16:465–474. https://doi.org/10.1109/TII.2019.2937563

    Article  Google Scholar 

  93. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings

  94. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 770–778

    Google Scholar 

  95. Li H, Liu J, Xie J, Wang X (2019) GTAW penetration prediction model based on convolution neural network algorithm. J Mech Eng 55:22–28

    Article  Google Scholar 

  96. Li H, Ren H, Liu Z, Huang F, Xia G, Long Y (2022) In-situ monitoring system for weld geometry of laser welding based on multi-task convolutional neural network model. Meas J Int Meas Confed 204:112138. https://doi.org/10.1016/j.measurement.2022.112138

    Article  Google Scholar 

  97. Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22:1345–1359. https://doi.org/10.1109/TKDE.2009.191

    Article  Google Scholar 

  98. Jiao W, Wang Q, Cheng Y, Zhang YM (2021) End-to-end prediction of weld penetration: a deep learning and transfer learning based method. J Manuf Process 63:191–197. https://doi.org/10.1016/j.jmapro.2020.01.044

    Article  Google Scholar 

  99. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv Prepr arXiv14061078

  100. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  101. Chen C, Xiao R, Chen H, Lv N, Chen S (2020) Arc sound model for pulsed GTAW and recognition of different penetration states. Int J Adv Manuf Technol 108:3175–3191

    Article  Google Scholar 

  102. Chen C, Xiao R, Chen H, Lv N, Chen S (2021) Prediction of welding quality characteristics during pulsed GTAW process of aluminum alloy by multisensory fusion and hybrid network model. J Manuf Process 68:209–224. https://doi.org/10.1016/j.jmapro.2020.08.028

    Article  Google Scholar 

  103. Bacioiu D, Melton G, Papaelias M, Shaw R (2019) Automated defect classification of aluminium 5083 TIG welding using HDR camera and neural networks. J Manuf Process 45:603–613. https://doi.org/10.1016/j.jmapro.2019.07.020

    Article  Google Scholar 

  104. Bacioiu D, Melton G, Papaelias M, Shaw R (2019) Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning. NDT E Int 107:102139. https://doi.org/10.1016/j.ndteint.2019.102139

    Article  Google Scholar 

  105. Jiao W, Wang Q, Cheng Y, Yu R, Zhang Y (2020) Prediction of weld penetration using dynamic weld pool arc images. Weld J 99:295s–302s. https://doi.org/10.29391/2020.99.027

    Article  Google Scholar 

  106. Cheng Y, Wang Q, Jiao W, Yu R, Chen S, Zhang YM, Xiao J (2020) Detecting dynamic development of weld pool using machine learning from innovative composite images for adaptive welding. J Manuf Process 56:908–915. https://doi.org/10.1016/j.jmapro.2020.04.059

    Article  Google Scholar 

  107. Wu D, Huang Y, Zhang P, Yu Z, Chen H, Chen S (2020) Visual-acoustic penetration recognition in variable polarity plasma arc welding process using hybrid deep learning approach. IEEE Access 8:120417–120428

    Article  Google Scholar 

  108. Li Y, Liu C, Wu Z, Li L (2020) GMAW molten pool micrograph image recognition based on convolution neural network and transfer learning. Acta Microsc

    Google Scholar 

  109. Selvaraju RR, Das A, Vedantam R, Cogswell M, Parikh D, Batra D (2016) Grad-cam: why did you say that? arXiv Prepr arXiv161107450

  110. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626

    Google Scholar 

  111. Wang Z, Chen H, Zhong Q, Lin S, Wu J, Xu M, Zhang Q (2022) Recognition of penetration state in GTAW based on vision transformer using weld pool image. Int J Adv Manuf Technol 119:5439–5452

    Article  Google Scholar 

  112. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv Prepr arXiv170404861

  113. Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848–6856

    Google Scholar 

Download references

Code availability

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers U2141216, 51875212), the Program of Marine Economy Development Special Fund (Six Marine Industries) under the Department of Natural Resources of Guangdong Province (grant number GDNRC[2021]46), Science and Technology Planning Project of Guangdong Province (grant numbers 2021B1515420006, 2021B1515120026), and Shenzhen Science & Technology Program (grant numbers JSGG20201201100401005, JSGG20201201100400001).

Author information

Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, People’s Republic of China

    Zhenmin Wang, Liuyi Li, Haoyu Chen, Xiangmiao Wu, Ying Dong & Jiyu Tian

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, People’s Republic of China

    Qin Zhang

Authors
  1. Zhenmin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liuyi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haoyu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiangmiao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ying Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jiyu Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Zhenmin Wang: conceptualization; writing–original draft; writing–review and editing; supervision. Liuyi Li: formal analysis; investigation; resources; writing–original draft. Haoyu Chen: methodology; investigation, resources, writing–original draft. Xiangmiao Wu: conceptualization; supervision. Ying Dong: investigation; visualization. Jiyu Tian: funding acquisition; conceptualization; writing–review and editing. Qin Zhang: funding acquisition; conceptualization; writing–review and editing.

Corresponding authors

Correspondence to Jiyu Tian or Qin Zhang.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Written informed consent for publication was obtained from all participants.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Li, L., Chen, H. et al. Penetration recognition based on machine learning in arc welding: a review. Int J Adv Manuf Technol 125, 3899–3923 (2023). https://doi.org/10.1007/s00170-023-11035-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-11035-7

Keywords

Search

Navigation