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Machine learning for intelligent welding and manufacturing systems: research progress and perspective review

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Abstract

In the modern era, welding systems have been made smart with the inclusion of contemporary information technologies such as intelligent manufacturing and machine learning (ML). The ML has been integrated with a wide application area of metal joining to achieve the status of intelligent welding systems (IWS). The IWS, using ML, has drawn massive consideration from researchers and industrialists to obtain high product quality and cost-effective solutions. Intelligent welding uses modern computers for sensing, learning, decision-making, monitoring, and control, thus replacing/minimizing human interference. ML-integrated welding is primarily for modeling, identification, optimization, prediction, and controlling multiple variables. Citing the necessity and importance of ML models in weld quality and process optimization, the current study is aimed on describing basics of ML techniques, their types, models, and adaptability scenarios in numerous industrially sought IWS.

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References

  1. Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science(80-) 349:255–260. https://doi.org/10.1126/SCIENCE.AAA8415

    Article  MathSciNet  MATH  Google Scholar 

  2. Zhang X (2020) Machine learning. In: A matrix algebra approach to artificial intelligence. Springer, Singapore

    MATH  Google Scholar 

  3. Jin Z, Zhang Z, Demir K, Gu GX (2020) Machine learning for advanced additive manufacturing. Matter 3:1541–1556. https://doi.org/10.1016/j.matt.2020.08.023

    Article  Google Scholar 

  4. Wang C, Tan XP, Tor SB, Lim CS (2020) Machine learning in additive manufacturing: state-of-the-art and perspectives. Addit Manuf 36:101538

    Google Scholar 

  5. Cemernek D, Cemernek S, Gursch H, et al (2021) Machine learning in continuous casting of steel: a state-of-the-art survey. J Intell Manuf.https://doi.org/10.1007/s10845-021-01754-7

  6. Shahane S, Aluru N, Ferreira P, et al (2020) Optimization of solidification in die casting using numerical simulations and machine learning. J Manuf Process 51.https://doi.org/10.1016/j.jmapro.2020.01.016

  7. Alavijeh MS, Scott R, Seviaryn F, Maev RG (2021) Using machine learning to automate ultrasound-based classification of butt-fused joints in medium-density polyethylene gas pipesa). J Acoust Soc Am 150:561. https://doi.org/10.1121/10.0005656

    Article  Google Scholar 

  8. Siljama O, Koskinen T, Jessen-Juhler O (2021) Virkkunen I (2021) Automated flaw detection in multi-channel phased array ultrasonic data using machine learning. J Nondestruct Eval 403(40):1–13. https://doi.org/10.1007/S10921-021-00796-4

    Article  Google Scholar 

  9. Sun M, Yang M, Wang B et al (2021) Applications of molten pool visual sensing and machine learning in welding quality monitoring. J Phys Conf Ser 2002:012016. https://doi.org/10.1088/1742-6596/2002/1/012016

    Article  Google Scholar 

  10. Wu M, Phoha VV, Moon YB, Belman AK (2016) Detecting malicious defects in 3D printing process using machine learning and image classification. In: ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), vol 50688, p V014T07A004. https://doi.org/10.1115/IMECE2016-67641

  11. Joshi MS, Flood A, Sparks T, Liou FW (2019) Applications of supervised machine learning algorithms in additive manufacturing: a review. In: Solid Freeform Fabrication 2019: Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium University of Texas at Austin. https://doi.org/10.26153/tsw/17252

  12. Kumar S, Gopi T, Harikeerthana N, et al (2022) Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control. J IntellManuf 1–35.https://doi.org/10.1007/s10845-022-02029-5

  13. Santhanam P (2020) Quality management of machine learning systems. Commun Comput Inf Sci 1272:1–13. https://doi.org/10.1007/978-3-030-62144-5_1

    Article  Google Scholar 

  14. Tsironis L, Bilalis N, Moustakis V (2005) Using machine learning to support quality management: Framework and experimental investigation. TQM Mag 17:237–248. https://doi.org/10.1108/09544780510594207

    Article  Google Scholar 

  15. Srdoč A, Bratko I, Sluga A (2007) Machine learning applied to quality management—a study in ship repair domain. Comput Ind 58:464–473. https://doi.org/10.1016/J.COMPIND.2006.09.013

    Article  Google Scholar 

  16. Jia CB, Liu XF, Zhang GK et al (2021) Penetration/keyhole status prediction and model visualization based on deep learning algorithm in plasma arc welding. Int J Adv Manuf Technol 117:3577–3597. https://doi.org/10.1007/s00170-021-07903-9

    Article  Google Scholar 

  17. Sudhagar S, Sakthivel M, Ganeshkumar P (2019) Monitoring of friction stir welding based on vision system coupled with Machine learning algorithm. Meas J Int Meas Confed 144. https://doi.org/10.1016/j.measurement.2019.05.018

  18. Verma S, Misra JP, Singh J et al (2021) Prediction of tensile behavior of FS welded AA7039 using machine learning. Mater Today Commun 26:101933. https://doi.org/10.1016/j.mtcomm.2020.101933

    Article  Google Scholar 

  19. Thapliyal S, Mishra A (2021) Machine learning classification-based approach for mechanical properties of friction stir welding of copper. Manuf Lett 29:52–55. https://doi.org/10.1016/j.mfglet.2021.05.010

    Article  Google Scholar 

  20. Houssein EH, Emam MM, Ali AA, Suganthan PN (2021) Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Syst Appl 167:114161

    Article  Google Scholar 

  21. Barragán-Montero A, Javaid U, Valdés G et al (2021) Artificial intelligence and machine learning for medical imaging: A technology review. Phys Medica 83:242–256

    Article  Google Scholar 

  22. Maliamanis TV, Papakostas GA (2021) Machine learning vulnerability in medical imaging. In: Machine learning, big data, and IoT for medical informatics. Elsevier, Academic Press, pp 53–70. https://doi.org/10.1016/B978-0-12-821777-1.00004-5

  23. Lu Z (2021) Computational discovery of energy materials in the era of big data and machine learning: A critical review. Mater Reports Energy.https://doi.org/10.1016/j.matre.2021.100047

  24. Gu GH, Noh J, Kim I, Jung Y (2019) Machine learning for renewable energy materials. J Mater Chem A 7(29):17096–17117. https://doi.org/10.1039/C9TA02356A

  25. Lai JP, Chang YM, Chen CH, Pai PF (2020) A survey of machine learning models in renewable energy predictions. Appl Sci 10:5975

    Article  Google Scholar 

  26. Donepudi PK (2017) Machine learning and artificial intelligence in banking. Eng Int 5:83–86. https://doi.org/10.18034/ei.v5i2.490

    Article  Google Scholar 

  27. Leo M, Sharma S, Maddulety K (2019) Machine learning in banking risk management: a literature review. Risks 7:29. https://doi.org/10.3390/RISKS7010029

    Article  Google Scholar 

  28. Hui SC, Jha G (2000) Data mining for customer service support. Inf Manag 38:1–13. https://doi.org/10.1016/S0378-7206(00)00051-3

    Article  Google Scholar 

  29. Nuruzzaman M, Hussain OK (2018) A survey on chatbot implementation in customer service industry through deep neural networks. Proc-2018 IEEE Int Conf E-bus Eng ICEBE 2018:54–61. https://doi.org/10.1109/ICEBE.2018.00019

    Article  Google Scholar 

  30. Knox J, Williamson B, Bayne S (2019) Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies. Learn Media Technol 45:31–45. https://doi.org/10.1080/17439884.2019.1623251

    Article  Google Scholar 

  31. Tizghadam A, Khazaei H, Moghaddam MHY, Hassan Y (2019) Machine learning in transportation. J Adv Transp 2019. https://doi.org/10.1155/2019/4359785

  32. Bhavsar P, Safro I, Bouaynaya N, et al (2017) Machine learning in transportation data analytics. Data Anal Intell Transp Syst 283–307.https://doi.org/10.1016/B978-0-12-809715-1.00012-2

  33. Zantalis F, Koulouras G, Karabetsos S, Kandris D (2019) A review of machine learning and IoT in smart transportation. Futur Internet 11:94. https://doi.org/10.3390/FI11040094

    Article  Google Scholar 

  34. Barua L, Zou B, Zhou Y (2020) Machine learning for international freight transportation management: a comprehensive review. Res Transp Bus Manag 34:100453. https://doi.org/10.1016/J.RTBM.2020.100453

    Article  Google Scholar 

  35. How IoT & Industry 4.0 Relate - and Why Manufacturers Should Care. https://lucidworks.com/post/how-are-iot-and-industry-4-related/. Accessed 16 Jul 2021

  36. CART – Regression Tree from scratch with a hands-on example(in R) – Insight – Data Science Society, IMI, New Delhi. https://insightimi.wordpress.com/2020/03/15/cart-regression-tree-from-scratch-with-a-hands-on-examplein-r/. Accessed 16 Jul 2021

  37. Anderson A (2011) Report to the President on ensuring American leadership in advanced manufacturing. In: Executive Office of the President. 1600, Pennsylvania Avenue NW, Washington, DC

  38. Mavrikios D, Papakostas N, Mourtzis D (2011) Chryssolouris G (2011) On industrial learning and training for the factories of the future: a conceptual, cognitive and technology framework. J Intell Manuf 243(24):473–485. https://doi.org/10.1007/S10845-011-0590-9

    Article  Google Scholar 

  39. Wiendahl HP, Scholtissek P (1994) Management and Control of Complexity in Manufacturing. CIRP Ann-Manuf Technol 43:533–540. https://doi.org/10.1016/S0007-8506(07)60499-5

    Article  Google Scholar 

  40. Monostori L (2003) AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing, Engineering Applications of Artificial Intelligence. Pergamon, 16(4):277-291. https://doi.org/10.1016/S0952-1976(03)00078-2

  41. Khan A, Baharudin B, Lee L et al (2010) A review of machine learning algorithms for text-documents classification. J Adv Inf Technol 1:4–20

    Google Scholar 

  42. Nasir T, Asmael M, Zeeshan Q, Solyali D (2020) Applications of machine learning to friction stir welding process optimization. J Kejuruter 32:171–186

    Google Scholar 

  43. Raina R, Battle A, Lee H et al (2007) Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th international conference on Machine learning, pp 759–766. https://doi.org/10.1145/1273496.1273592

  44. Gentleman R, Carey VJ (2008) Unsupervised machine learning. Bioconductor Case Stud 137–157.https://doi.org/10.1007/978-0-387-77240-0_10

  45. Alabi MO, Nixon K, Botef I (2018) A survey on recent applications of machine learning with big data in additive manufacturing industry. Am J Eng Appl Sci 11:1114–1124. https://doi.org/10.3844/AJEASSP.2018.1114.1124

    Article  Google Scholar 

  46. Lloyd S, Mohseni M, Rebentrost P (2013) Quantum algorithms for supervised and unsupervised machine learning. arXiv Prepr arXiv:1307.0411. https://doi.org/10.48550/arXiv.1307.0411

  47. Du Y, Mukherjee T, DebRoy T (2019) Conditions for void formation in friction stir welding from machine learning. NPJ Comput Mater 51(5):1–8. https://doi.org/10.1038/s41524-019-0207-y

    Article  Google Scholar 

  48. Du Y, Mukherjee T, Mitra P, DebRoy T (2020) Machine learning based hierarchy of causative variables for tool failure in friction stir welding. Acta Mater 192:67–77. https://doi.org/10.1016/J.ACTAMAT.2020.03.047

    Article  Google Scholar 

  49. Johnson NS, Vulimiri PS, To AC et al (2020) Invited review: Machine learning for materials developments in metals additive manufacturing. Addit Manuf 36:101641. https://doi.org/10.1016/J.ADDMA.2020.101641

    Article  Google Scholar 

  50. Ester M, Kriegel H, Sander J et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96:226–231

    Google Scholar 

  51. Learned-Miller EG (2014) Introduction to supervised learning. Department of Computer Science, University of Massachusetts, vol 3

  52. Gittler T, Glasder M, Öztürk E et al (2021) International Conference on Advanced and Competitive Manufacturing Technologies milling tool wear prediction using unsupervised machine learning. Int J Adv Manuf Technol 117:2213–2226. https://doi.org/10.1007/s00170-021-07281-2

    Article  Google Scholar 

  53. Wuest T, Irgens C, Thoben KD (2014) An approach to monitoring quality in manufacturing using supervised machine learning on product state data. J Intell Manuf 25(5):1167–1180. https://doi.org/10.1007/s10845-013-0761-y

  54. Hye Jun J, Chang TW, Jun S (2020) Quality prediction and yield improvement in process manufacturing based on data analytics. Processes 8:1068. https://doi.org/10.3390/pr8091068

    Article  Google Scholar 

  55. Aznar P (2020) Decision trees: Gini vs entropy

  56. Gordon ER, Shokrani A, Flynn JM et al (2016) A surface modification decision tree to influence design in additive manufacturing. Smart Innov Syst Technol 52:423–434. https://doi.org/10.1007/978-3-319-32098-4_36

    Article  Google Scholar 

  57. Random Forest Regression. Random Forest Regression is a… | by Chaya Bakshi | Level Up Coding. https://levelup.gitconnected.com/random-forest-regression-209c0f354c84. Accessed 16 Jul 2021

  58. Zhao Z, Guo Y, Bai L et al (2019) Quality monitoring in wire-arc additive manufacturing based on cooperative awareness of spectrum and vision. Optik (Stuttg) 181:351–360. https://doi.org/10.1016/j.ijleo.2018.12.071

    Article  Google Scholar 

  59. Sun L, Hu SJ, Freiheit T (2021) Feature-based quality classification for ultrasonic welding of carbon fiber reinforced polymer through Bayesian regularized neural network. J Manuf Syst 58:335–347. https://doi.org/10.1016/j.jmsy.2020.12.016

    Article  Google Scholar 

  60. Koskimäki HJ, Laurinen P, Haapalainen E et al (2007) Application of the extended knn method to resistance spot welding process identification and the benefits of process information. IEEE Trans Ind Electron 54:2823–2830. https://doi.org/10.1109/TIE.2007.901353

    Article  Google Scholar 

  61. Duan F, Yin S, Song P et al (2019) Automatic Welding Defect Detection of X-Ray Images by Using Cascade AdaBoost with Penalty Term. IEEE Access 7:125929–125938. https://doi.org/10.1109/ACCESS.2019.2927258

    Article  Google Scholar 

  62. Hong WC, Pai PF (2006) Predicting engine reliability by support vector machines. Int J Adv Manuf Technol 28:154–161. https://doi.org/10.1007/s00170-004-2340-z

    Article  Google Scholar 

  63. Cho S, Asfour S, Onar A, Kaundinya N (2005) Tool breakage detection using support vector machine learning in a milling process. Int J Mach Tools Manuf 45:241–249. https://doi.org/10.1016/j.ijmachtools.2004.08.016

    Article  Google Scholar 

  64. Wu D, Jennings C, Terpenny J, et al (2017) A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J ManufSci Eng Trans ASME 139.https://doi.org/10.1115/1.4036350

  65. Loyer JL, Henriques E, Fontul M, Wiseall S (2016) Comparison of machine learning methods applied to the estimation of manufacturing cost of jet engine components. Int J Prod Econ 178:109–119. https://doi.org/10.1016/j.ijpe.2016.05.006

    Article  Google Scholar 

  66. García V, Sánchez JS, Rodríguez-Picón LA et al (2019) Using regression models for predicting the product quality in a tubing extrusion process. J Intell Manuf 30:2535–2544. https://doi.org/10.1007/s10845-018-1418-7

    Article  Google Scholar 

  67. Song L, Huang W, Han X, Mazumder J (2017) Real-time composition monitoring using support vector regression of laser-induced plasma for laser additive manufacturing. IEEE Trans Ind Electron 64:633–642. https://doi.org/10.1109/TIE.2016.2608318

    Article  Google Scholar 

  68. Lenz B, Barak B (2013) Data mining and support vector regression machine learning in semiconductor manufacturing to improve virtual metrology. In: Proceedings of the Annual Hawaii International Conference on System Sciences, pp 3447–3456. https://doi.org/10.1109/HICSS.2013.163

  69. Alfaro-Cortés E, Alfaro-Navarro J-L, Gámez M, García N (2020) Using random forest to interpret out-of-control signals. Acta Polytech Hungarica 17:115–130

    Article  Google Scholar 

  70. Forero-Ramírez JC, Restrepo-Girón AD, Nope-Rodríguez SE (2019) Detection of internal defects in carbon fiber reinforced plastic slabs using background thermal compensation by filtering and support vector machines. J Nondestruct Eval 38:1–11. https://doi.org/10.1007/S10921-019-0569-6/FIGURES/11

    Article  Google Scholar 

  71. Tootooni MS, Dsouza A, Donovan R, et al (2017) Classifying the dimensional variation in additive manufactured parts from laser-scanned three-dimensional point cloud data using machine learning approaches. J Manuf Sci Eng Trans ASME 139.https://doi.org/10.1115/1.4036641/477158

  72. Bergmann S, Feldkamp N, Strassburger S (2017) Emulation of control strategies through machine learning in manufacturing simulations. J Simul 11:38–50. https://doi.org/10.1057/S41273-016-0006-0

    Article  Google Scholar 

  73. Munirathinam S, Ramadoss B (2016) Predictive models for equipment fault detection in the semiconductor manufacturing process. Int J Eng Technol 8:273–285. https://doi.org/10.7763/IJET.2016.V8.898

    Article  Google Scholar 

  74. Das B, Pal S, Bag S (2017) Torque based defect detection and weld quality modelling in friction stir welding process. J Manuf Process 27.https://doi.org/10.1016/j.jmapro.2017.03.012

  75. Dogan A, Birant D (2021) Machine learning and data mining in manufacturing. Expert Syst Appl 166:114060. https://doi.org/10.1016/J.ESWA.2020.114060

    Article  Google Scholar 

  76. Zhang X, Kano M, Tani M et al (2020) Prediction and causal analysis of defects in steel products: Handling nonnegative and highly overdispersed count data. Control Eng Pract 95:104258. https://doi.org/10.1016/J.CONENGPRAC.2019.104258

    Article  Google Scholar 

  77. Ferreira RDSB, Sabbaghi A, Huang Q (2020) Automated geometric shape deviation modeling for additive manufacturing systems via Bayesian neural networks. IEEE Trans Autom Sci Eng 17:584–598. https://doi.org/10.1109/TASE.2019.2936821

    Article  Google Scholar 

  78. Lingitz L, Gallina V, Ansari F et al (2018) Lead time prediction using machine learning algorithms: a case study by a semiconductor manufacturer. Procedia CIRP 72:1051–1056. https://doi.org/10.1016/J.PROCIR.2018.03.148

    Article  Google Scholar 

  79. Cho E, Jun J-H, Chang T-W, Choi Y (2020) Quality prediction modeling of plastic extrusion process. ICIC express Lett Part B, Appl an Int J Res Surv 11:447–452

    Google Scholar 

  80. Alfaro-Cortés E, Alfaro-Navarro JL, Gámez M, García N (2020) Using random fores to interpret out-of-control signals. Acta Polytech Hungarica 17:115–130

    Article  Google Scholar 

  81. Kim J, Han Y, Lee J (2016) Euclidean distance based feature selection for fault detection prediction model in semiconductor manufacturing process. Adv Sci Technol Lett 133:85–89

    Article  Google Scholar 

  82. Tian Y, Fu M, Wu F (2015) Steel plates fault diagnosis on the basis of support vector machines. Neurocomputing 151:296–303. https://doi.org/10.1016/J.NEUCOM.2014.09.036

    Article  Google Scholar 

  83. Wang KS (2013) Towards zero-defect manufacturing (ZDM)-a data mining approach. Adv Manuf 1:62–74. https://doi.org/10.1007/S40436-013-0010-9/TABLES/3

    Article  Google Scholar 

  84. Arif F, Suryana N, Computer BH-IJ of, 2013 U (2013) A data mining approach for developing quality prediction model in multi-stage manufacturing. Int J Comput Appl 69:35–40

    Google Scholar 

  85. Kim D, Kang P, Cho S et al (2012) Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing. Expert Syst Appl 39:4075–4083. https://doi.org/10.1016/J.ESWA.2011.09.088

    Article  Google Scholar 

  86. Meidan Y, Lerner B, Rabinowitz G, Hassoun M (2011) Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining. IEEE Trans Semicond Manuf 24:237–248. https://doi.org/10.1109/TSM.2011.2118775

    Article  Google Scholar 

  87. Kumar S, Wu CS (2017) Review: Mg and its alloy - scope, future perspectives and recent advancements in welding and processing. J Harbin Inst Technol 24:1–37. https://doi.org/10.11916/j.issn.1005-9113.17065

    Article  Google Scholar 

  88. Zhan X, Ou W, Wei Y, Jiang J (2016) The feasibility of intelligent welding procedure qualification system for Q345R SMAW. Int J Adv Manuf Technol 83:765–777. https://doi.org/10.1007/s00170-015-7295-8

    Article  Google Scholar 

  89. Wang B, Hu SJ, Sun L, Freiheit T (2020) Intelligent welding system technologies: State-of-the-art review and perspectives. J Manuf Syst 56:373–391. https://doi.org/10.1016/j.jmsy.2020.06.020

    Article  Google Scholar 

  90. Kumar S, Wu CS (2018) A novel technique to join Al and Mg alloys: ultrasonic vibration assisted linear friction stir welding. Mater Today Proc 5:18142–18151. https://doi.org/10.1016/j.matpr.2018.06.150

    Article  Google Scholar 

  91. Kumar S (2021) Kar A (2021) A review of solid-state additive manufacturing processes. Trans Indian Natl Acad Eng 64(6):955–973. https://doi.org/10.1007/S41403-021-00270-7

    Article  Google Scholar 

  92. Kumar S, Wu CS, Padhy GK (2017) Ultrasonic vibrations in friction stir welding: state of the art. In: 7th International Conference on Welding Science and Engineering (WSE 2017)” in conjunction with “3rd International Symposium on Computer-Aided Welding Engineering (CAWE 2017). Shandong University, Jinan China, pp 272–276

  93. Zhou J, Li P, Zhou Y et al (2018) Toward new-generation intelligent manufacturing. Engineering 4:11–20. https://doi.org/10.1016/J.ENG.2018.01.002

    Article  Google Scholar 

  94. Tao F, Qi Q, Liu A et al (2018) Data-driven smart manufacturing. J. Manuf Syst 48:157–169

    Article  Google Scholar 

  95. Rout A, Deepak BBVL, Biswal BB (2019) Advances in weld seam tracking techniques for robotic welding: A review. Robot Comput Integr Manuf 56:12–37. https://doi.org/10.1016/J.RCIM.2018.08.003

    Article  Google Scholar 

  96. Kumar S, Kishor B (2021) Ultrasound added additive manufacturing for metals and composites: process and control. Additive and Subtractive Manufacturing of Composites. Springer, Singapore, pp 53–72

    Chapter  Google Scholar 

  97. Hong TS, Ghobakhloo M, Khaksar W (2014) Robotic Welding Technology Compr Mater Process 6:77–99. https://doi.org/10.1016/B978-0-08-096532-1.00604-X

    Article  Google Scholar 

  98. Teti R, Kumara SRT (1997) Intelligent computing methods for manufacturing systems. CIRP Ann 46:629–652. https://doi.org/10.1016/S0007-8506(07)60883-X

    Article  Google Scholar 

  99. Knaak C, Thombansen U, Abels P, Kröger M (2018) Machine learning as a comparative tool to determine the relevance of signal features in laser welding. Procedia CIRP 74:623–627. https://doi.org/10.1016/J.PROCIR.2018.08.073

    Article  Google Scholar 

  100. Wuest T, Weimer D, Irgens C, Thoben K-D (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4:23–45. https://doi.org/10.1080/21693277.2016.1192517

    Article  Google Scholar 

  101. Stavridis J, Papacharalampopoulos A (2018) Stavropoulos P (2017) Quality assessment in laser welding: a critical review. Int J Adv Manuf Technol 945(94):1825–1847. https://doi.org/10.1007/S00170-017-0461-4

    Article  Google Scholar 

  102. Bist A, Saini JS, Sharma B (2016) A review of tool wear prediction during friction stir welding of aluminium matrix composite. Trans Nonferrous Met Soc China 26:2003–2018. https://doi.org/10.1016/S1003-6326(16)64318-2

    Article  Google Scholar 

  103. Fan X, Gao X, Liu G et al (2021) (2021) Research and prospect of welding monitoring technology based on machine vision. Int J Adv Manuf Technol 11511(115):3365–3391. https://doi.org/10.1007/S00170-021-07398-4

    Article  Google Scholar 

  104. Dong H, Cong M, Zhang Y, et al (2017) Real time welding parameter prediction for desired character performance. Proc - IEEE IntConf Robot Autom 0:1794–1799.https://doi.org/10.1109/ICRA.2017.7989211

  105. Dong H, Cong M, Zhang Y et al (2018) (2018) Modeling and real-time prediction for complex welding process based on weld pool. Int J Adv Manuf Technol 965(96):2495–2508. https://doi.org/10.1007/S00170-018-1685-7

    Article  Google Scholar 

  106. Aviles-Viñas JF, Rios-Cabrera R (2015) Lopez-Juarez I (2015) On-line learning of welding bead geometry in industrial robots. Int J Adv Manuf Technol 831(83):217–231. https://doi.org/10.1007/S00170-015-7422-6

    Article  Google Scholar 

  107. Peters SR, Fulmer BE (2007) Non-linear adaptive control system and method for welding. U.S. Patent No. 8,963,045, Washington, DC. Issued 24 Feb 2015

  108. Mendes N, Neto P, Loureiro A, Moreira AP (2016) Machines and control systems for friction stir welding: a review. Mater Des 90:256–265. https://doi.org/10.1016/J.MATDES.2015.10.124

    Article  Google Scholar 

  109. Nong L, Shao C, Kim TH, Hu SJ (2018) Improving process robustness in ultrasonic metal welding of lithium-ion batteries. J Manuf Syst 48:45–54. https://doi.org/10.1016/J.JMSY.2018.04.014

    Article  Google Scholar 

  110. You DY, Gao XD, Katayama S (2014) Review of laser welding monitoring. Sci Technol Weld Join 19:181–201. https://doi.org/10.1179/1362171813Y.0000000180

    Article  Google Scholar 

  111. Zhang B, Hong KM, Shin YC (2020) Deep-learning-based porosity monitoring of laser welding process. Manuf Lett 23:62–66. https://doi.org/10.1016/J.MFGLET.2020.01.001

    Article  Google Scholar 

  112. Zhao H, Qi H (2016) Vision-based keyhole detection in laser full penetration welding process. J Laser Appl 28:022412. https://doi.org/10.2351/1.4944003

    Article  Google Scholar 

  113. Luo M, Shin YC (2015) Vision-based weld pool boundary extraction and width measurement during keyhole fiber laser welding. Opt Lasers Eng 64:59–70. https://doi.org/10.1016/J.OPTLASENG.2014.07.004

    Article  Google Scholar 

  114. 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. https://doi.org/10.3390/S100403549

    Article  Google Scholar 

  115. Kaplan AFH, Powell J (2011) Spatter in laser welding. J Laser Appl 23:032005. https://doi.org/10.2351/1.3597830

    Article  Google Scholar 

  116. Wan X, Wang Y, Zhao D et al (2017) Weld quality monitoring research in small scale resistance spot welding by dynamic resistance and neural network. Measurement 99:120–127. https://doi.org/10.1016/J.MEASUREMENT.2016.12.010

    Article  Google Scholar 

  117. You D, Gao X, Katayama S (2014) Multisensor fusion system for monitoring high-power disk laser welding using support vector machine. IEEE Trans Ind Informatics 10:1285–1295. https://doi.org/10.1109/TII.2014.2309482

    Article  Google Scholar 

  118. Gao X, Chen Y, You D et al (2017) Detection of micro gap weld joint by using magneto-optical imaging and Kalman filtering compensated with RBF neural network. Mech Syst Signal Process 84:570–583. https://doi.org/10.1016/J.YMSSP.2016.07.041

    Article  Google Scholar 

  119. You D, Gao X, Katayama S (2014) Visual-based spatter detection during high-power disk laser welding. Opt Lasers Eng 54:1–7. https://doi.org/10.1016/J.OPTLASENG.2013.09.010

    Article  Google Scholar 

  120. 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/FULL/HTML

    Article  Google Scholar 

  121. Yang B, Kong F, Lavoie J-P et al (2020) Monitoring of keyhole entrance and molten pool with quality analysis during adjustable ring mode laser welding. Appl Opt 59:1576–1584. https://doi.org/10.1364/AO.383232

    Article  Google Scholar 

  122. Stadter C, Schmoeller M, von Rhein L, Zaeh MF (2020) Real-time prediction of quality characteristics in laser beam welding using optical coherence tomography and machine learning. J Laser Appl 32:022046. https://doi.org/10.2351/7.0000077

    Article  Google Scholar 

  123. Huff S, Chen H, Lee YJ et al (2017) TIG welding skill extraction using a machine learning algorithm. https://digital.library.txstate.edu/handle/10877/6930

  124. Gao J, Wu CS (2003) Neurofuzzy control of weld penetration in gas tungsten arc welding. Sci Technol Weld Join 8:143–148. https://doi.org/10.1179/136217103225008856

    Article  Google Scholar 

  125. Gao J, Wu C (2013) Experimental determination of weld pool geometry in gas tungsten arc welding. Sci Technol Weld Join 6:288–292. https://doi.org/10.1179/136217101101538893

    Article  Google Scholar 

  126. Wu CS, Jia CB (2006) Statistical characteristic for detecting weld penetration defects in gas-metal arc welding. Proc Inst Mech Eng Part B J Eng Manuf 220:793–796. https://doi.org/10.1243/09544054JEMA420SC

    Article  Google Scholar 

  127. Wu CS, Gao JQ, Hu JK (2006) Real-time sensing and monitoring in robotic gas metal arc welding. Meas Sci Technol 18:303. https://doi.org/10.1088/0957-0233/18/1/037

    Article  Google Scholar 

  128. Wu CS, Hu QX, Sun JS et al (2005) Intelligent monitoring and recognition of the short-circuiting gas metal arc welding process. Proc Inst Mech Eng Part B J Eng Manuf 218:1145–1151. https://doi.org/10.1243/0954405041897121

    Article  Google Scholar 

  129. Öberg AE (2017) Åstrand E (2017) Improved productivity by reduced variation in gas metal arc welding (GMAW). Int J Adv Manuf Technol 921(92):1027–1038. https://doi.org/10.1007/S00170-017-0214-4

    Article  Google Scholar 

  130. Wu C, Polte T (2001) A fuzzy logic system for process monitoring and quality evaluation in GMAW. Weld J 80:33–38

    Google Scholar 

  131. Wu CS, Polte T, Rehfeldt D (2013) Gas metal arc welding process monitoring and quality evaluation using neural networks. Sci Technol Weld Join 5:324–328. https://doi.org/10.1179/136217100101538380

    Article  Google Scholar 

  132. Ludewig HW, Siwicke JH, Kilty AL et al (1996) Method for arc welding fault detection. US Pat no 5(521):354

    Google Scholar 

  133. Zhang WJ, Xiao J, Zhang YM (2016) A mobile sensing system for real-time 3D weld pool surface measurement in manual GTAW. Meas Sci Technol 27:045102. https://doi.org/10.1088/0957-0233/27/4/045102

    Article  Google Scholar 

  134. Saeed G, Zhang Y (2007) Weld pool surface depth measurement using a calibrated camera and structured light. Meas Sci Technol 18:2570

    Article  Google Scholar 

  135. Wu CS, Zhong LM, Gao JQ (2009) Visualization of hump formation in high-speed gas metal arc welding. Meas Sci Technol 20:115702. https://doi.org/10.1088/0957-0233/20/11/115702

    Article  Google Scholar 

  136. Chi SC, Hsu LC (2001) A fuzzy radial basis function neural network for predicting multiple quality characteristics of plasma arc welding. Annu Conf North Am Fuzzy Inf Process Soc - NAFIPS 5:2807–2812. https://doi.org/10.1109/NAFIPS.2001.943671

    Article  Google Scholar 

  137. Sun H, Yang J, Wang L (2016) Resistance spot welding quality identification with particle swarm optimization and a kernel extreme learning machine model. Int J Adv Manuf Technol 915(91):1879–1887. https://doi.org/10.1007/S00170-016-9944-Y

    Article  Google Scholar 

  138. Zamanzad Gavidel S, Lu S, Rickli JL (2019) Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints. Int J Adv Manuf Technol 105:3779–3796. https://doi.org/10.1007/S00170-019-03821-Z

    Article  Google Scholar 

  139. Das D, Pratihar DK, Roy GG, Pal AR (2018) Phenomenological model-based study on electron beam welding process, and input-output modeling using neural networks trained by back-propagation algorithm, genetic algorithms, particle swarm optimization algorithm and bat algorithm. Appl Intell 48:2698–2718. https://doi.org/10.1007/S10489-017-1101-2

    Article  Google Scholar 

  140. Rai R, Palmer TA, Elmer JW, Debroy T (2008) Heat transfer and fluid flow during electron beam welding of 304L stainless steel alloy models were used to calculate the three-dimensional temperature field and fluid velocities for electron beam welding of 304L stainless steel. J Phys D Appl Phys 42:025503

    Article  Google Scholar 

  141. Das D, Das AK, Pratihar DK, Roy GG (2020) Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms: Proc Inst Mech Eng Part C J Mech. Eng Sci 235:2008–2021. https://doi.org/10.1177/0954406220950343

    Article  Google Scholar 

  142. Jaypuria S, Gupta SK, Pratihar DK (2020) Comparative study of feed-forward and recurrent neural networks in modeling of electron beam welding. Adv Addit Manuf Join 521–531.https://doi.org/10.1007/978-981-32-9433-2_45

  143. Lin T, Horne BG, Tiňo P, Giles CL (1996) Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans neural networks 7:1329–1338. https://doi.org/10.1109/72.548162

    Article  Google Scholar 

  144. Medsker L, Jain DL, Raton London New York Washington B (2001) Recurrent neural networks: design and applications. CRC press

  145. Kumar S, Wu C (2021) Eliminating intermetallic compounds via Ni interlayer during friction stir welding of dissimilar Mg/Al alloys. J Mater Res Technol 15:4353–4369. https://doi.org/10.1016/J.JMRT.2021.10.065

    Article  Google Scholar 

  146. Threadgill P, Leonard A, Shercliff H (2013) Friction stir welding of aluminium alloys. Int Mater Rev 54:49–93

    Article  Google Scholar 

  147. Kumar S, Wu CS, Padhy GK, Ding W (2017) Application of ultrasonic vibrations in welding and metal processing: a status review. J Manuf Process 26:295–322. https://doi.org/10.1016/j.jmapro.2017.02.027

    Article  Google Scholar 

  148. Kumar S, Gaur V (2022) Advances in Fatigue Prediction Techniques. In: Advances in Fatigue and Fracture Testing and Modelling. Intech Open, London, pp 01–15

    Google Scholar 

  149. Asmael MBA, Glaissa MAA (2020) Effects of rotation speed and dwell time on the mechanical properties and microstructure of dissimilar aluminum-titanium alloys by friction stir spot welding (FSSW). Materwiss Werksttech 51:1002–1008. https://doi.org/10.1002/MAWE.201900115

    Article  Google Scholar 

  150. Talebizadehsardari P, Musharavati F, Khan A et al (2021) Underwater friction stir welding of Al-Mg alloy: Thermo-mechanical modeling and validation. Mater Today Commun 26:101965. https://doi.org/10.1016/J.MTCOMM.2020.101965

    Article  Google Scholar 

  151. Abu-Okail M, Mahmoud TS, Abu-Oqail A (2020) Improving microstructural and mechanical properties of AA2024 base metal by adding reinforced strip width of AA7075 via vertical compensation friction stir welding technique. J Fail Anal Prev 20:184–196. https://doi.org/10.1007/S11668-020-00814-Z/FIGURES/16

    Article  Google Scholar 

  152. Kumar S (2016) Ultrasonic assisted friction stir processing of 6063 aluminum alloy. Arch Civ Mech Eng 16:473–484. https://doi.org/10.1016/j.acme.2016.03.002

    Article  Google Scholar 

  153. Kumar S, Wu CS, Sun Z, Ding W (2019) Effect of ultrasonic vibration on welding load, macrostructure, and mechanical properties of Al/Mg alloy joints fabricated by friction stir lap welding. Int J Adv Manuf Technol 100:1787–1799. https://doi.org/10.1007/s00170-018-2717-z

    Article  Google Scholar 

  154. Kumar S, Wu CS, Song G (2020) Process parametric dependency of axial downward force and macro- and microstructural morphologies in ultrasonically assisted friction stir welding of Al/Mg alloys. Metall Mater Trans A 51:2863–2881. https://doi.org/10.1007/s11661-020-05716-1

    Article  Google Scholar 

  155. Kumar S, Wu CS (2020) Suppression of intermetallic reaction layer by ultrasonic assistance during friction stir welding of Al and Mg based alloys. J Alloys Compd 827:154343. https://doi.org/10.1016/j.jallcom.2020.154343

    Article  Google Scholar 

  156. Kumar S, Wu CS, Shi L (2020) Intermetallic diminution during friction stir welding of dissimilar al/mg alloys in lap configuration via ultrasonic assistance. Metall Mater Trans A 51:5725–5742. https://doi.org/10.1007/s11661-020-05982-z

    Article  Google Scholar 

  157. Kumar S, Wu C (2021) Strengthening effects of tool-mounted ultrasonic vibrations during friction stir lap welding of Al and Mg alloys. Metall Mater Trans A Phys Metall Mater Sci 52:2909–2925. https://doi.org/10.1007/s11661-021-06282-w

    Article  Google Scholar 

  158. Lu H, Li Y, Chen M et al (2018) Brain intelligence: go beyond artificial intelligence. Mob Networks Appl 23:368–375. https://doi.org/10.1007/s11036-017-0932-8

    Article  Google Scholar 

  159. Patrício DI, Rieder R (2018) Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput Electron Agric 153:69–81. https://doi.org/10.1016/j.compag.2018.08.001

    Article  Google Scholar 

  160. Johnson KW, Soto JT, Glicksberg BS et al (2018) Artificial intelligence in cardiology. J Am Coll Cardiol 71:2668–2679. https://doi.org/10.1016/j.jacc.2018.03.521

    Article  Google Scholar 

  161. Shi F, Wang J, Shi J, Shen D et al (2020) Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE Rev Biomed Eng 14:4–15. https://doi.org/10.1109/RBME.2020.2987975

  162. Rajaee T, Ebrahimi H, Nourani V (2019) A review of the artificial intelligence methods in groundwater level modeling. J Hydrol 572:336–351. https://doi.org/10.1016/j.jhydrol.2018.12.037

    Article  Google Scholar 

  163. Armansyah AW, Saedon J (2018) Development of prediction system model for mechanical property in friction stir welding using support vector machine (SVM). J Mech Eng 5:216–225 https://ir.uitm.edu.my/id/eprint/40606

  164. Atwya M, Panoutsos G (2020) Transient thermography for flaw detection in friction stir welding: a machine learning approach. IEEE Trans Ind Informatics 16:4423–4435. https://doi.org/10.1109/TII.2019.2948023

    Article  Google Scholar 

  165. Mongan PG, Hinchy EP, O’Dowd NP, McCarthy CT (2020) Optimisation of ultrasonically welded joints through machine learning. In: Procedia CIRP, vol 93. Elsevier B.V, pp 527–531. https://doi.org/10.1016/j.procir.2020.04.060

  166. Balachandar K, Jegadeeshwaran R, Gandhikumar D (2019) Condition monitoring of FSW tool using vibration analysis-a machine learning approach. Mater Today Proc 27:2970–2974. https://doi.org/10.1016/j.matpr.2020.04.903

    Article  Google Scholar 

  167. Verma S, Misra JP, Popli D (2020) Modeling of friction stir welding of aviation grade aluminium alloy using machine learning approaches. Int J Model Simul 42:1–8. https://doi.org/10.1080/02286203.2020.1803605

    Article  Google Scholar 

  168. Eren B, Guvenc MA, Mistikoglu S (2021) Artificial intelligence applications for friction stir welding: a review. Met Mater Int 27:193–219. https://doi.org/10.1007/s12540-020-00854-y

  169. Liao TW, Roberts J, Wahab MA, Okeil AM (2019) Building a multi-signal based defect prediction system for a friction stir welding process. Procedia Manufacturing. 38:1775–1791. https://doi.org/10.1016/j.promfg.2020.01.089

  170. Mishra D, Gupta A, Raj P et al (2020) Real time monitoring and control of friction stir welding process using multiple sensors. CIRP J Manuf Sci Technol 30:1–11. https://doi.org/10.1016/j.cirpj.2020.03.004

    Article  Google Scholar 

  171. Huggett DJ, Liao TW, Wahab MA, Okeil A (2018) Prediction of friction stir weld quality without and with signal features. Int J AdvManuf Technol 95.https://doi.org/10.1007/s00170-017-1403-x

  172. Nadeau F, Thériault B, Gagné MO (2020) Machine learning models applied to friction stir welding defect index using multiple joint configurations and alloys. In: Proceedings of the Institution of Mechanical Engineers, Part L:Journal of Materials: Design and Applications, vol 234, pp 752–765. https://doi.org/10.1177/1464420720917415

  173. Rovinelli A, Sangid MD, Proudhon H (2018) Ludwig W (2018) Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials. NPJ Comput Mater 41(4):1–10. https://doi.org/10.1038/s41524-018-0094-7

    Article  Google Scholar 

  174. Bird RB (2002) Transport phenomena. Appl Mech Rev 55:R1–R4. https://doi.org/10.1115/1.1424298

    Article  Google Scholar 

  175. Rai R, De A, Bhadeshia H, DebRoy T (2011) Review: friction stir welding tools. Sci Technol Weld Join 16:325–342

    Article  Google Scholar 

  176. Collier CT (2015) Tool material degradation due to friction stir welding of aluminum alloys. University of South Carolina, Diss. https://www.proquest.com/openview/563c4ade83ee2d2c1e1cf71a6561ef66/1?pq-origsite=gscholar&cbl=18750

  177. Thompson B (2010) Tool degradation characterization in the friction stir welding of hard metals. The Ohio State University, Columbus

    Google Scholar 

  178. Vaira Vignesh R, Padmanaban R (2018) Artificial neural network model for predicting the tensile strength of friction stir welded aluminium alloy AA1100. Mater Today Proc 5:16716–16723. https://doi.org/10.1016/J.MATPR.2018.06.035

    Article  Google Scholar 

  179. Wakchaure KN, Thakur AG, Gadakh V, Kumar A (2018) Multi-objective optimization of friction stir welding of aluminium alloy 6082–T6 using hybrid Taguchi-grey relation analysis- ANN method. Mater Today Proc 5:7150–7159. https://doi.org/10.1016/J.MATPR.2017.11.380

    Article  Google Scholar 

  180. Kurtulmuş M, Kiraz A (2018) Artificial neural network modelling for polyethylene FSSW parameters. Sci Iran 25:1266–1271. https://doi.org/10.24200/SCI.2018.50030.1473

    Article  Google Scholar 

  181. Ranjith R, Iridharan PK, Senthil KB (2017) Predicting the tensile strength of friction stir welded dissimilar aluminum alloy using ann. Int J Civ Eng Technol 8:345–353

    Google Scholar 

  182. Dehabadi VM, Ghorbanpour S, Azimi G (2016) Application of artificial neural network to predict Vickers microhardness of AA6061 friction stir welded sheets. J Cent South Univ 239(23):2146–2155. https://doi.org/10.1007/S11771-016-3271-1

    Article  Google Scholar 

  183. Anand K, Barik BK, Tamilmannan K, Sathiya P (2015) Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints. Eng Sci Technol an Int J 18:394–407. https://doi.org/10.1016/J.JESTCH.2015.02.001

    Article  Google Scholar 

  184. Paoletti A, Lambiase F, Di Ilio A (2015) Optimization of friction stir welding of thermoplastics. Procedia CIRP 33:562–567. https://doi.org/10.1016/J.PROCIR.2015.06.078

    Article  Google Scholar 

  185. Ghetiya ND, Patel KM (2014) Prediction of tensile strength in friction stir welded aluminium alloy using artificial neural network. Procedia Technol 14:274–281

    Article  Google Scholar 

  186. Shojaeefard M, Behnagh R, Akbari M, Givi M (2013) Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm. Mater Des 44:190–198

    Article  Google Scholar 

  187. Manvatkar VD, Arora A, De A, DebRoy T (2013) Neural network models of peak temperature, torque, traverse force, bending stress and maximum shear stress during friction stir welding. Sci Technol Weld Join 17:460–466. https://doi.org/10.1179/1362171812Y.0000000035

    Article  Google Scholar 

  188. Buffa G, Fratini L, Micari F (2012) Mechanical and microstructural properties prediction by artificial neural networks in FSW processes of dual phase titanium alloys. J Manuf Process 14:289–296. https://doi.org/10.1016/J.JMAPRO.2011.10.007

    Article  Google Scholar 

  189. Okuyucu H, Kurt A, Arcaklioglu E (2007) Artificial neural network application to the friction stir welding of aluminum plates. Mater Des 28:78–84. https://doi.org/10.1016/J.MATDES.2005.06.003

    Article  Google Scholar 

  190. Weiss SM, Dhurandhar A, Baseman RJ et al (2014) Continuous prediction of manufacturing performance throughout the production lifecycle. J Intell Manuf 274(27):751–763. https://doi.org/10.1007/S10845-014-0911-X

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Science (IISc) Bengaluru, Karnataka, 560012, India

    Sachin Kumar

  2. Department of Mechanical and Industrial Engineering, Indian Institute of Technology (IIT) Roorkee, Uttarakhand, 247667, India

    Vidit Gaur

  3. MOE Key Lab for Liquid-Solid Structure Evolution and Materials Processing, Institute of Materials Joining, Shandong University, 250061, Jinan, China

    ChuanSong Wu

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Sachin Kumar: collect and analyzing literature, analyzed the data and wrote the original draft. Vidit Gaur: discussion, manuscript review and editing. ChuanSong Wu: supervision, giving suggestion and draft revision. All authors have read and agreed to the published version of the manuscript.

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Kumar, S., Gaur, V. & Wu, C. Machine learning for intelligent welding and manufacturing systems: research progress and perspective review. Int J Adv Manuf Technol 123, 3737–3765 (2022). https://doi.org/10.1007/s00170-022-10403-z

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