{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T03:08:18Z","timestamp":1789182498230,"version":"build-2803163510"},"reference-count":231,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005271","name":"Nanjing University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005271","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Robotics and Computer-Integrated Manufacturing"],"published-print":{"date-parts":[[2027,2]]},"DOI":"10.1016\/j.rcim.2026.103375","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T07:07:18Z","timestamp":1783753638000},"page":"103375","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Closed-loop intelligent quality control for robotic welding: A survey on architecture, technologies, and system integration"],"prefix":"10.1016","volume":"103","author":[{"given":"Cheng","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kehong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.rcim.2026.103375_b1","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1016\/j.jmapro.2022.05.029","article-title":"Application of sensing technology in intelligent robotic arc welding: A review","volume":"79","author":"Xu","year":"2022","journal-title":"J. Manuf. Process."},{"issue":"7\u20138","key":"10.1016\/j.rcim.2026.103375_b2","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1177\/0020294019857747","article-title":"Thermal and structural modelling of arc welding processes: A literature review","volume":"52","author":"Arora","year":"2019","journal-title":"Meas. Control."},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b3","doi-asserted-by":"crossref","first-page":"283","DOI":"10.3390\/electronics13020283","article-title":"Real-time recognition of molten pools based on improved DeepLabV3+ in keyhole tungsten inert gas welding applications","volume":"13","author":"Yang","year":"2024","journal-title":"Electronics"},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b4","doi-asserted-by":"crossref","first-page":"4807","DOI":"10.1038\/s41467-025-60164-y","article-title":"A physics-informed and data-driven framework for robotic welding in manufacturing","volume":"16","author":"Liu","year":"2025","journal-title":"Nat. Commun."},{"key":"10.1016\/j.rcim.2026.103375_b5","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.jmapro.2024.04.023","article-title":"Physics-informed machine learning approach for molten pool morphology prediction and process evaluation in directed energy deposition of 12CrNi2 alloy steel","volume":"119","author":"Cao","year":"2024","journal-title":"J. Manuf. Process."},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b6","doi-asserted-by":"crossref","first-page":"2762","DOI":"10.1109\/LRA.2019.2918681","article-title":"Model predictive control of GTAW weld pool penetration","volume":"4","author":"Chen","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.rcim.2026.103375_b7","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.jmapro.2020.07.060","article-title":"Model predictive control of layer width in wire arc additive manufacturing","volume":"58","author":"Xia","year":"2020","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b8","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.jmapro.2020.05.033","article-title":"Vision based defects detection for keyhole TIG welding using deep learning with visual explanation","volume":"56","author":"Xia","year":"2020","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2024.102767","article-title":"Progress, challenges and trends on vision sensing technologies in automatic\/intelligent robotic welding: State-of-the-art review","volume":"89","author":"Guo","year":"2024","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b10","doi-asserted-by":"crossref","first-page":"21651","DOI":"10.1109\/TASE.2025.3614992","article-title":"Vision-based autonomous robotic arc welding: State-of-the-art review and perspectives","volume":"22","author":"Ma","year":"2025","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.rcim.2026.103375_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2024.119294","article-title":"Advancements in control systems and integration of artificial intelligence in welding robots: A review","volume":"312","author":"Kahnamouei","year":"2024","journal-title":"Ocean Eng."},{"key":"10.1016\/j.rcim.2026.103375_b12","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.jmsy.2023.05.026","article-title":"Deep learning-based welding image recognition: A comprehensive review","volume":"68","author":"Liu","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.rcim.2026.103375_b13","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.jmapro.2021.08.058","article-title":"Real-time sensing of gas metal arc welding process\u2014A literature review and analysis","volume":"70","author":"Cheng","year":"2021","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b14","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.jmsy.2020.06.020","article-title":"Intelligent welding system technologies: State-of-the-art review and perspectives","volume":"56","author":"Wang","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.rcim.2026.103375_b15","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.optlastec.2017.09.038","article-title":"Hybrid laser arc welding: State-of-art review","volume":"99","author":"Acherjee","year":"2018","journal-title":"Opt. Laser Technol."},{"key":"10.1016\/j.rcim.2026.103375_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmachtools.2025.104344","article-title":"Machine learning applications in welding processes: Progresses and opportunities","volume":"213","author":"Geng","year":"2025","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b17","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.jmapro.2025.02.039","article-title":"A comprehensive review of welding defect recognition from X-ray images","volume":"140","author":"Wang","year":"2025","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2020.103326","article-title":"A review of vision-aided robotic welding","volume":"123","author":"Lei","year":"2020","journal-title":"Comput. Ind."},{"issue":"9","key":"10.1016\/j.rcim.2026.103375_b19","doi-asserted-by":"crossref","first-page":"4529","DOI":"10.1007\/s00170-024-13002-2","article-title":"Real-time closed-loop control of molten pool transient area in direct laser deposition via PID algorithm with enhanced robustness","volume":"130","author":"Liu","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.rcim.2026.103375_b20","article-title":"Design of a PID control for a prototype of an automated GMAW welding bench","volume":"vol. 1257","author":"Trigos","year":"2019"},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b21","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1007\/s10845-023-02107-2","article-title":"Method for monitoring and controlling penetration of complex groove welding based on online multi-modal data","volume":"35","author":"Gao","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b22","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.jmapro.2017.07.015","article-title":"A comparison of model predictive control and PID temperature control in friction stir welding","volume":"29","author":"Taysom","year":"2017","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b23","article-title":"A defect detection system for wire arc additive manufacturing using incremental learning","volume":"27","author":"Li","year":"2022","journal-title":"J. Ind. Inf. Integr."},{"issue":"6","key":"10.1016\/j.rcim.2026.103375_b24","doi-asserted-by":"crossref","first-page":"4083","DOI":"10.1007\/s10845-024-02431-1","article-title":"A digital twin approach for weld penetration prediction of TIG welding with dual ellipsoid heat source","volume":"36","author":"Qu","year":"2025","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2025.102992","article-title":"Transfer learning and augmented data-driven parameter prediction for robotic welding","volume":"95","author":"Zhang","year":"2025","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b26","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1016\/j.jmapro.2025.07.002","article-title":"Real-time identification and measurement algorithm of typical welding defects based on line-structured light active vision","volume":"151","author":"Ji","year":"2025","journal-title":"J. Manuf. Process."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b27","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s10043-018-0479-3","article-title":"Online weld pool contour extraction and seam width prediction based on mixing spectral vision","volume":"26","author":"Zhang","year":"2019","journal-title":"Opt. Rev."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b28","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s10845-022-02013-z","article-title":"In-process prediction of weld penetration depth using machine learning-based molten pool extraction technique in tungsten arc welding","volume":"35","author":"Baek","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109847","article-title":"Online monitoring of weld cross-sectional shape using optical emission spectroscopy and neural network during laser dissimilar welding","volume":"141","author":"Kang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b30","doi-asserted-by":"crossref","first-page":"3326","DOI":"10.1109\/JSEN.2022.3143113","article-title":"Electric arc length control of circular seam in welding robot based on arc voltage sensing","volume":"22","author":"Lei","year":"2022","journal-title":"IEEE Sensors J."},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b31","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1080\/10589759.2022.2094377","article-title":"Effects of welding defects on the damage evolution of Q245R steel using acoustic emission and infrared thermography","volume":"38","author":"Zhou","year":"2023","journal-title":"Nondestruct. Test. Eval."},{"issue":"10","key":"10.1016\/j.rcim.2026.103375_b32","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1007\/s40194-025-02052-z","article-title":"Research on the welding strategy and welding technology for medium-thick plates based on three-line structured light vision","volume":"69","author":"He","year":"2025","journal-title":"Weld. World"},{"key":"10.1016\/j.rcim.2026.103375_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125402","article-title":"Nondestructive inspection method of welding rate for heat sink fins with complex structure via infrared thermography principle and deep learning method","volume":"260","author":"Jiang","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"10.1016\/j.rcim.2026.103375_b34","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1088\/0022-3727\/33\/6\/312","article-title":"Numerical simulation of weld pool geometry in laser beam welding","volume":"33","author":"Sudnik","year":"2000","journal-title":"J. Phys. D: Appl. Phys."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b35","doi-asserted-by":"crossref","first-page":"373","DOI":"10.3390\/app7040373","article-title":"Influence of welding parameters on the weld pool dimensions and shape in a TIG configuration","volume":"7","author":"Stadler","year":"2017","journal-title":"Appl. Sci."},{"key":"10.1016\/j.rcim.2026.103375_b36","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.jmapro.2020.10.018","article-title":"Current mode effects on weld bead geometry and heat affected zone in pulsed wire arc additive manufacturing of Ti-6-4 and Inconel 718","volume":"60","author":"Benakis","year":"2020","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b37","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.jmapro.2019.09.009","article-title":"Development of novel MIG welding process with duplex current feeding","volume":"47","author":"Aoki","year":"2019","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.optlastec.2024.110794","article-title":"A novel methodology for investigating the through-thickness molten pool shape during remote laser beam welding","volume":"175","author":"Pamarthi","year":"2024","journal-title":"Opt. Laser Technol."},{"issue":"5\u20136","key":"10.1016\/j.rcim.2026.103375_b39","doi-asserted-by":"crossref","first-page":"4057","DOI":"10.1007\/s00170-021-08513-1","article-title":"Rapid detection of weld contour based on compound vision of projection structured light and shape from shading","volume":"119","author":"Yan","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"6","key":"10.1016\/j.rcim.2026.103375_b40","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s11837-003-0134-7","article-title":"Welding: Solidification and microstructure","volume":"55","author":"David","year":"2003","journal-title":"JOM"},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b41","doi-asserted-by":"crossref","first-page":"571","DOI":"10.3390\/met12040571","article-title":"Effect of transient thermal conditions on columnar-to-equiaxed transition during laser welding: A phase-field study","volume":"12","author":"Guo","year":"2022","journal-title":"Metals"},{"key":"10.1016\/j.rcim.2026.103375_b42","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.jmapro.2024.11.069","article-title":"Real-time prediction of temperature field during welding by data-mechanism driving","volume":"133","author":"Jiao","year":"2025","journal-title":"J. Manuf. Process."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b43","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1007\/s00339-016-9994-x","article-title":"Integrated analysis of millisecond laser irradiation of steel by comprehensive optical diagnostics and numerical simulation","volume":"122","author":"Doubenskaia","year":"2016","journal-title":"Appl. Phys. A"},{"issue":"11","key":"10.1016\/j.rcim.2026.103375_b44","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s10765-024-03433-0","article-title":"Black body cavity apparatus for measuring the emissivity of nickel-titanium-based shape-memory alloys and other metals","volume":"45","author":"Javed","year":"2024","journal-title":"Int. J. Thermophys."},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b45","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.measurement.2012.10.019","article-title":"Emissivity estimation for high temperature radiation pyrometry on Ti\u20136Al\u20134V","volume":"46","author":"Hagqvist","year":"2013","journal-title":"Measurement"},{"key":"10.1016\/j.rcim.2026.103375_b46","article-title":"Prediction of internal welding penetration based on IR thermal image supported by machine vision and ANN-model during automatic robot welding process","volume":"9","author":"Wang","year":"2024","journal-title":"J. Adv. Join. Process."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b47","doi-asserted-by":"crossref","first-page":"105","DOI":"10.3390\/s23010105","article-title":"Development of a multi-spectral pyrometry sensor for high-speed transient surface-temperature measurements in combustion-relevant harsh environments","volume":"23","author":"Neupane","year":"2022","journal-title":"Sensors"},{"issue":"7","key":"10.1016\/j.rcim.2026.103375_b48","doi-asserted-by":"crossref","first-page":"3291","DOI":"10.1007\/s10845-023-02203-3","article-title":"A novel surface temperature sensor and random forest-based welding quality prediction model","volume":"35","author":"Wang","year":"2024","journal-title":"J. Intell. Manuf."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b49","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40712-014-0015-6","article-title":"Usability of arc types in industrial welding","volume":"9","author":"Kah","year":"2014","journal-title":"Int. J. Mech. Mater. Eng."},{"issue":"10","key":"10.1016\/j.rcim.2026.103375_b50","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1007\/s42452-022-05150-5","article-title":"Assessment of arc stability features for selected gas metal arc welding conditions","volume":"4","author":"Kah","year":"2022","journal-title":"SN Appl. Sci."},{"key":"10.1016\/j.rcim.2026.103375_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.sab.2022.106474","article-title":"On-line monitoring and defect detection of arc-welding via plasma optical spectroscopy and LIBS","volume":"194","author":"Valdiande","year":"2022","journal-title":"Spectrochim. Acta Part B: At. Spectrosc."},{"key":"10.1016\/j.rcim.2026.103375_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.csite.2024.104056","article-title":"Influence of controlled and conventional short circuit waveforms on mechanical and microstructural effects in the gas metal arc welding processes","volume":"55","author":"Santhakumari","year":"2024","journal-title":"Case Stud. Therm. Eng."},{"key":"10.1016\/j.rcim.2026.103375_b53","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.procir.2020.09.137","article-title":"Process control by real-time pulse shaping in laser beam welding of different material combinations","volume":"94","author":"Seibold","year":"2020","journal-title":"Procedia CIRP"},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b54","doi-asserted-by":"crossref","first-page":"3517","DOI":"10.1007\/s00170-021-08598-8","article-title":"Acoustic emissions in directed energy deposition processes","volume":"119","author":"Hauser","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"9","key":"10.1016\/j.rcim.2026.103375_b55","doi-asserted-by":"crossref","first-page":"911","DOI":"10.3390\/machines11090911","article-title":"Welding penetration monitoring for ship robotic GMAW using arc sound sensing based on improved wavelet denoising","volume":"11","author":"Jiao","year":"2023","journal-title":"Machines"},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b56","doi-asserted-by":"crossref","first-page":"2643","DOI":"10.3390\/s23052643","article-title":"Deep learning-empowered digital twin using acoustic signal for welding quality inspection","volume":"23","author":"Ji","year":"2023","journal-title":"Sensors"},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b57","doi-asserted-by":"crossref","first-page":"267","DOI":"10.5545\/sv-jme.2010.181","article-title":"Monitoring gas metal arc welding process by using audible sound signal","volume":"57","author":"Horvat","year":"2011","journal-title":"Strojni\u0161ki Vestnik \u2013 J. Mech. Eng."},{"key":"10.1016\/j.rcim.2026.103375_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110962","article-title":"Acoustic signal-based automated control of welding penetration using digital twin technology","volume":"208","author":"Tao","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b59","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1109\/JSEN.2025.3634585","article-title":"DMAW: A dynamic multimodal measurement fusion network with attention for reliable welding process monitoring under harsh industrial environments","volume":"26","author":"Fan","year":"2025","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.rcim.2026.103375_b60","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.jmapro.2024.11.102","article-title":"Visual monitoring of weld penetration in aluminum alloy GTAW based on deep transfer learning enhanced by task-specific pre-training and semi-supervised learning","volume":"133","author":"Xue","year":"2025","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b61","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.jmapro.2025.01.033","article-title":"A multi-source heterogeneous information fusion network for comprehensive assessment of spot welding quality","volume":"135","author":"Dai","year":"2025","journal-title":"J. Manuf. Process."},{"issue":"21","key":"10.1016\/j.rcim.2026.103375_b62","doi-asserted-by":"crossref","first-page":"7500","DOI":"10.3390\/ma15217500","article-title":"Short circuiting transfer, formation, and microstructure of Ti-6Al-4V alloy by external longitudinal magnetic field hybrid metal inert gas welding additive manufacturing","volume":"15","author":"Shi","year":"2022","journal-title":"Materials"},{"issue":"7","key":"10.1016\/j.rcim.2026.103375_b63","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.3390\/ma17071580","article-title":"Online detection of laser welding penetration depth based on multi-sensor features","volume":"17","author":"She","year":"2024","journal-title":"Materials"},{"key":"10.1016\/j.rcim.2026.103375_b64","doi-asserted-by":"crossref","first-page":"147349","DOI":"10.1109\/ACCESS.2020.3015529","article-title":"A multi-sensor data fusion system for laser welding process monitoring","volume":"8","author":"Deng","year":"2020","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b65","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1007\/s40194-024-01857-8","article-title":"Weld penetration state identification based on time series multi-source data fusion","volume":"69","author":"Wang","year":"2025","journal-title":"Weld. World"},{"key":"10.1016\/j.rcim.2026.103375_b66","first-page":"1","article-title":"Online welding quality monitoring via vision-acoustic fusion with future-state prediction","author":"Shua","year":"2026","journal-title":"Weld. World"},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b67","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s10845-014-0971-y","article-title":"Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals","volume":"28","author":"Zhang","year":"2017","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b68","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1016\/j.jmapro.2025.10.114","article-title":"Online monitoring of typical welding defects in robotic laser-MAG hybrid welding based on multi-source information fusion and Stacking-PSO-LightGBM","volume":"156","author":"Lin","year":"2025","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b69","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.113473","article-title":"Deep multimodal fusion of spectral and visual data for laser welding defect classification","volume":"165","author":"Zhang","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.rcim.2026.103375_b70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10589759.2025.2572395","article-title":"An anti-interference attention fusion network-based weld penetration state recognition with multimodal data","author":"Hua","year":"2025","journal-title":"Nondestruct. Test. Eval."},{"key":"10.1016\/j.rcim.2026.103375_b71","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.jmapro.2024.01.055","article-title":"Online monitoring system for welding states of bottom-locking joints in high-speed trains via multi-information fusion and 3DCNN","volume":"113","author":"Yang","year":"2024","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b72","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.patrec.2025.01.004","article-title":"Intelligent detection method for aluminum alloy TIG welding quality by fusing multimodal data features","volume":"189","author":"Jiang","year":"2025","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.rcim.2026.103375_b73","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.jmapro.2026.02.014","article-title":"Transfer learning based on multimodal feature fusion: A study on intelligent detection of robotic MAG welding defects under cross-condition and small-sample scenarios","volume":"162","author":"Lin","year":"2026","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b74","article-title":"A digital shadow approach for enhancing process monitoring in wire arc additive manufacturing using sensor fusion","volume":"40","author":"Mu","year":"2024","journal-title":"J. Ind. Inf. Integr."},{"key":"10.1016\/j.rcim.2026.103375_b75","doi-asserted-by":"crossref","DOI":"10.1016\/j.optlastec.2024.111287","article-title":"Two-stage fusion framework driven by domain knowledge for penetration prediction of laser welding","volume":"179","author":"Li","year":"2024","journal-title":"Opt. Laser Technol."},{"key":"10.1016\/j.rcim.2026.103375_b76","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.jmapro.2025.12.006","article-title":"Unraveling the potential of multi-sensor fusion of acoustic signals and melt pool geometric images towards defect identification in additive manufacturing","volume":"157","author":"Zhang","year":"2026","journal-title":"J. Manuf. Process."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b77","first-page":"431","article-title":"Accurate detection of weld seams for laser welding in real-world manufacturing","volume":"44","author":"Ali","year":"2023","journal-title":"AI Mag."},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b78","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.3390\/app16031568","article-title":"A systematic review and taxonomy of machine learning methods for process optimization and control in laser welding","volume":"16","author":"Voets","year":"2026","journal-title":"Appl. Sci."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b79","first-page":"45","article-title":"A theoretical model for welding process with Gaussian heat source\u2014Part 1","volume":"68","author":"Iacobescu","year":"2006","journal-title":"UPB Sci. Bull. Ser. D"},{"key":"10.1016\/j.rcim.2026.103375_b80","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jmatprotec.2017.02.002","article-title":"Extension of the double-ellipsoidal heat source model to narrow-groove and keyhole weld configurations","volume":"246","author":"Flint","year":"2017","journal-title":"J. Mater. Process. Technol."},{"issue":"7\u20138","key":"10.1016\/j.rcim.2026.103375_b81","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1016\/j.ijheatmasstransfer.2010.07.069","article-title":"GTAW liquid pool convections and the weld shape variations under helium gas shielding","volume":"54","author":"Dong","year":"2011","journal-title":"Int. J. Heat Mass Transfer"},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b82","doi-asserted-by":"crossref","DOI":"10.1088\/2053-1591\/ac5a38","article-title":"An active visual monitoring method for GMAW weld surface defects based on random forest model","volume":"9","author":"Zhu","year":"2022","journal-title":"Mater. Res. Express"},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b83","doi-asserted-by":"crossref","first-page":"6519","DOI":"10.1109\/LRA.2022.3173659","article-title":"How to accurately monitor the weld penetration from dynamic weld pool serial images using CNN-LSTM deep learning model?","volume":"7","author":"Yu","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.rcim.2026.103375_b84","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.jmapro.2025.04.052","article-title":"Spatiotemporal prediction and mechanisms of molten pool instability in variable polarity plasma arc robotic welding via CNN-LSTM","volume":"145","author":"Jiang","year":"2025","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b85","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"Raissi","year":"2019","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.rcim.2026.103375_b86","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.jmapro.2026.01.035","article-title":"A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks","volume":"160","author":"Li","year":"2026","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b87","unstructured":"Douglas Bezerra de Ara\u00fajo, Paulo Roberto de Freitas Teixeira, Luiz Ant\u00f4nio Bragan\u00e7a da Cunda, Applicability of the Gaussian distribution heat source model to the thermal simulation of welding processes, in: Proceedings of the 22nd International Congress of Mechanical Engineering (COBEM 2013), 2013, Ribeir\u00e3o Preto, SP, Brazil, 3\u20137 November 2013."},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b88","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1080\/17445302.2014.937059","article-title":"Parameter determination of double-ellipsoidal heat source model and its application in the multi-pass welding process","volume":"10","author":"Fu","year":"2015","journal-title":"Ships Offshore Struct."},{"issue":"7","key":"10.1016\/j.rcim.2026.103375_b89","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1080\/10407782.2021.1872257","article-title":"A simplified thermal approximation method to include the effects of marangoni convection in the melt pools of processes that involve moving point heat sources","volume":"79","author":"Nikam","year":"2021","journal-title":"Numer. Heat Transf. Part A: Appl."},{"key":"10.1016\/j.rcim.2026.103375_b90","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmatprotec.2021.117135","article-title":"The influence of magnetic field orientation on metal mixing in electromagnetic stirring enhanced wire feed laser beam welding","volume":"294","author":"Meng","year":"2021","journal-title":"J. Mater. Process. Technol."},{"key":"10.1016\/j.rcim.2026.103375_b91","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijheatmasstransfer.2023.124754","article-title":"Phase-field simulation of the dendrite growth in aluminum alloy AA5754 during alternating current electromagnetic stirring laser beam welding","volume":"218","author":"Yang","year":"2024","journal-title":"Int. J. Heat Mass Transfer"},{"key":"10.1016\/j.rcim.2026.103375_b92","doi-asserted-by":"crossref","DOI":"10.1016\/j.optlastec.2024.112236","article-title":"CFD numerical simulation of melt flow and weld pool formation in semiconductor laser direct welding of 6061 aluminium alloy with PET based on surface microtextured","volume":"183","author":"Li","year":"2025","journal-title":"Opt. Laser Technol."},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b93","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1007\/s12540-019-00486-x","article-title":"Modeling of phase transformation kinetics in resistance spot welding and investigation of effect of post weld heat treatment on weld microstructure","volume":"27","author":"Feujofack Kemda","year":"2021","journal-title":"Met. Mater. Int."},{"key":"10.1016\/j.rcim.2026.103375_b94","doi-asserted-by":"crossref","DOI":"10.1016\/j.matchar.2022.112424","article-title":"Linear friction welding of Ti60 near-\u03b1 titanium alloy: Investigating phase transformations and dynamic recrystallization mechanisms","volume":"194","author":"Guo","year":"2022","journal-title":"Mater. Charact."},{"issue":"15","key":"10.1016\/j.rcim.2026.103375_b95","doi-asserted-by":"crossref","first-page":"3656","DOI":"10.3390\/ma17153656","article-title":"Prediction of welding deformation using the thermal elastic-plastic finite element method by considering welding interpass temperature","volume":"17","author":"Han","year":"2024","journal-title":"Materials"},{"key":"10.1016\/j.rcim.2026.103375_b96","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijthermalsci.2024.109066","article-title":"Influence of thermal conductivity on the predicted temperature fields in welding of steels","volume":"202","author":"Sun","year":"2024","journal-title":"Int. J. Therm. Sci."},{"key":"10.1016\/j.rcim.2026.103375_b97","article-title":"Numerical prediction and control of deformation and residual stress in double-sided arc welding of large pressure hull","volume":"242","author":"Li","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.rcim.2026.103375_b98","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.jmapro.2023.07.043","article-title":"Prediction of transverse welding residual stress considering transverse and bending constraints in butt welding","volume":"102","author":"Park","year":"2023","journal-title":"J. Manuf. Process."},{"issue":"7","key":"10.1016\/j.rcim.2026.103375_b99","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1007\/s40194-023-01502-w","article-title":"A novel approach to prediction of welding residual stress and distortion","volume":"67","author":"Grams","year":"2023","journal-title":"Weld. World"},{"issue":"21","key":"10.1016\/j.rcim.2026.103375_b100","doi-asserted-by":"crossref","first-page":"8687","DOI":"10.3390\/s23218687","article-title":"Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling","volume":"23","author":"Yin","year":"2023","journal-title":"Sensors"},{"issue":"11","key":"10.1016\/j.rcim.2026.103375_b101","doi-asserted-by":"crossref","DOI":"10.1115\/1.4047947","article-title":"Advanced welding manufacturing: A brief analysis and review of challenges and solutions","volume":"142","author":"Zhang","year":"2020","journal-title":"J. Manuf. Sci. Eng."},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b102","doi-asserted-by":"crossref","DOI":"10.2351\/1.4799556","article-title":"The effect of fit-up geometry on melt flow and weld quality in laser hybrid welding","volume":"25","author":"Lamas","year":"2013","journal-title":"J. Laser Appl."},{"key":"10.1016\/j.rcim.2026.103375_b103","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.jmapro.2021.04.007","article-title":"In situ monitoring and penetration prediction of plasma arc welding based on welder intelligence-enhanced deep random forest fusion","volume":"66","author":"Wu","year":"2021","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b104","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.jmapro.2020.03.006","article-title":"A quality diagnosis method of GMAW based on improved empirical mode decomposition and extreme learning machine","volume":"54","author":"Huang","year":"2020","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b105","doi-asserted-by":"crossref","DOI":"10.1016\/j.ultras.2019.106057","article-title":"Segmented analysis of time-of-flight diffraction ultrasound for flaw detection in welded steel plates using extreme learning machines","volume":"102","author":"Silva","year":"2020","journal-title":"Ultrasonics"},{"issue":"6","key":"10.1016\/j.rcim.2026.103375_b106","doi-asserted-by":"crossref","first-page":"632","DOI":"10.3390\/jmse9060632","article-title":"Multi-pass welding distortion analysis using layered shell elements based on inherent strain","volume":"9","author":"Lee","year":"2021","journal-title":"J. Mar. Sci. Eng."},{"key":"10.1016\/j.rcim.2026.103375_b107","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.jmapro.2020.01.044","article-title":"End-to-end prediction of weld penetration: A deep learning and transfer learning based method","volume":"63","author":"Jiao","year":"2021","journal-title":"J. Manuf. Process."},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b108","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1007\/s10845-022-01909-0","article-title":"Research on recognition methods of spot-welding surface appearances based on transfer learning and a lightweight high-precision convolutional neural network","volume":"34","author":"Xiao","year":"2023","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b109","doi-asserted-by":"crossref","first-page":"120193","DOI":"10.1109\/ACCESS.2021.3108462","article-title":"Feature extraction of laser welding pool image and application in welding quality identification","volume":"9","author":"Gao","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.rcim.2026.103375_b110","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.neucom.2022.01.004","article-title":"Deep learning approach for defective spot welds classification using small and class-imbalanced datasets","volume":"477","author":"Dai","year":"2022","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b111","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1007\/s40194-025-01951-5","article-title":"Applicable and generalizable machine learning for intelligent welding in automotive manufacturing","volume":"69","author":"Wang","year":"2025","journal-title":"Weld. World"},{"key":"10.1016\/j.rcim.2026.103375_b112","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.rcim.2026.103375_b113","doi-asserted-by":"crossref","first-page":"5437","DOI":"10.1109\/ACCESS.2024.3521220","article-title":"Combining multi-scale U-Net with transformer for welding defect detection of oil\/gas pipeline","volume":"13","author":"Zhang","year":"2025","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b114","doi-asserted-by":"crossref","DOI":"10.1016\/j.jksuci.2024.102049","article-title":"Multi-model feature aggregation for classification of laser welding images with vision transformer","volume":"36","author":"Din","year":"2024","journal-title":"J. King Saud Univ. \u2013 Comput. Inf. Sci."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b115","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1038\/s41598-025-32639-x","article-title":"Contextual priority attention enables linear time sequence modeling in transformers","volume":"16","author":"Khaled","year":"2026","journal-title":"Sci. Rep."},{"issue":"22","key":"10.1016\/j.rcim.2026.103375_b116","doi-asserted-by":"crossref","first-page":"7024","DOI":"10.3390\/s25227024","article-title":"WELD-DETR: A real-time welding defect detection framework with multi-scale feature fusion and multi-kernel perception optimization","volume":"25","author":"Liang","year":"2025","journal-title":"Sensors"},{"issue":"20","key":"10.1016\/j.rcim.2026.103375_b117","doi-asserted-by":"crossref","first-page":"6561","DOI":"10.3390\/s24206561","article-title":"Welding defect monitoring based on multi-scale feature fusion of molten pool videos","volume":"24","author":"Shi","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.rcim.2026.103375_b118","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.101963","article-title":"An expert knowledge-empowered CNN approach for welding radiographic image recognition","volume":"56","author":"Liu","year":"2023","journal-title":"Adv. Eng. Inform."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b119","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1007\/s10845-021-01892-y","article-title":"Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding","volume":"33","author":"Zhou","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b120","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.109473","article-title":"Physics-informed ensemble learning for online joint strength prediction in ultrasonic metal welding","volume":"181","author":"Meng","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.rcim.2026.103375_b121","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2026.113983","article-title":"Few-shot transfer learning for laser welding prediction","volume":"169","author":"Wu","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b122","doi-asserted-by":"crossref","DOI":"10.1016\/j.asej.2025.103958","article-title":"A recurrent attention semi-supervised domain adaptive network for cross-domain diagnosis of resistance spot weld nugget quality","volume":"17","author":"Luo","year":"2026","journal-title":"Ain Shams Eng. J."},{"key":"10.1016\/j.rcim.2026.103375_b123","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijfatigue.2024.108566","article-title":"A modified physics-informed neural network to fatigue life prediction of deck-rib double-side welded joints","volume":"189","author":"Li","year":"2024","journal-title":"Int. J. Fatigue"},{"key":"10.1016\/j.rcim.2026.103375_b124","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijfatigue.2026.109539","article-title":"CINAS-PINN: Causal inference-based neural architecture search in physics-informed neural networks for fatigue life prediction with welding strain energy","volume":"208","author":"Gao","year":"2026","journal-title":"Int. J. Fatigue"},{"key":"10.1016\/j.rcim.2026.103375_b125","article-title":"A guide to neural ordinary differential equations: Machine learning for data-driven digital engineering","volume":"6","author":"Worsham","year":"2025","journal-title":"Digit. Eng."},{"issue":"11","key":"10.1016\/j.rcim.2026.103375_b126","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1038\/s42256-022-00556-7","article-title":"Closed-form continuous-time neural networks","volume":"4","author":"Hasani","year":"2022","journal-title":"Nat. Mach. Intell."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b127","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1038\/s41524-023-01080-x","article-title":"End-to-end differentiability and tensor processing unit computing to accelerate materials\u2019 inverse design","volume":"9","author":"Liu","year":"2023","journal-title":"NPJ Comput. Mater."},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b128","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.3390\/s24031001","article-title":"Digital twin virtual welding approach of robotic friction stir welding based on co-simulation of FEA model and robotic model","volume":"24","author":"Chen","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.rcim.2026.103375_b129","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1016\/j.jmapro.2024.09.022","article-title":"Digital twin for weld pool evolution by data-physics integrated driving","volume":"131","author":"Jiao","year":"2024","journal-title":"J. Manuf. Process."},{"issue":"18","key":"10.1016\/j.rcim.2026.103375_b130","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.3390\/math9182263","article-title":"Real-time data assimilation in welding operations using thermal imaging and accelerated high-fidelity digital twinning","volume":"9","author":"Pereira \u00c1lvarez","year":"2021","journal-title":"Mathematics"},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b131","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s44245-025-00113-7","article-title":"Modeling and strategies for arc welding penetration control algorithm: A review","volume":"4","author":"Zeng","year":"2025","journal-title":"Discov. Mech. Eng."},{"key":"10.1016\/j.rcim.2026.103375_b132","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2025.104332","article-title":"A sensor-integrated digital twin framework for molten pool monitoring of laser powder bed fusion","volume":"171","author":"Peng","year":"2025","journal-title":"Comput. Ind."},{"key":"10.1016\/j.rcim.2026.103375_b133","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.jmapro.2025.09.070","article-title":"MSDF-TL: A transfer learning and physics-constrained multi-source data fusion framework for weld state prediction","volume":"154","author":"Cheng","year":"2025","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b134","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.122682","article-title":"Physics-assisted transfer learning metamodels to predict bead geometry and carbon emission in laser butt welding","volume":"359","author":"Wu","year":"2024","journal-title":"Appl. Energy"},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b135","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1109\/TCST.2013.2266662","article-title":"Model-based predictive control of weld penetration in gas tungsten arc welding","volume":"22","author":"Liu","year":"2013","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"10.1016\/j.rcim.2026.103375_b136","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.jmapro.2021.05.005","article-title":"Active disturbance rejection control of layer width in wire arc additive manufacturing based on deep learning","volume":"67","author":"Wang","year":"2021","journal-title":"J. Manuf. Process."},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b137","doi-asserted-by":"crossref","first-page":"5651","DOI":"10.1109\/JESTPE.2025.3589232","article-title":"Research on fast-frequency pulsed welding power supply based on phase-shifted full bridge with adaptive auxiliary current","volume":"13","author":"He","year":"2025","journal-title":"IEEE J. Emerg. Sel. Top. Power Electron."},{"key":"10.1016\/j.rcim.2026.103375_b138","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.rcim.2018.07.001","article-title":"Stiffness-based pose optimization of an industrial robot for five-axis milling","volume":"55","author":"Xiong","year":"2019","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b139","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.finel.2014.02.003","article-title":"Simulation-based numerical optimization of arc welding process for reduced distortion in welded structures","volume":"84","author":"Islam","year":"2014","journal-title":"Finite Elem. Anal. Des."},{"key":"10.1016\/j.rcim.2026.103375_b140","article-title":"Optimization and simulation of arc welding robot parameters based on offline programming","author":"Abebe","year":"2023","journal-title":"Mater. Today: Proc."},{"key":"10.1016\/j.rcim.2026.103375_b141","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rcim.2018.03.013","article-title":"Model reference adaptive controller for enhancing depth of penetration and bead width during cold metal transfer joining process","volume":"53","author":"Rahul","year":"2018","journal-title":"Robot. Comput.-Integr. Manuf."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b142","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3390\/standards5040031","article-title":"Research on the structural model of welding process specifications for aviation products based on trade-off design","volume":"5","author":"Wang","year":"2025","journal-title":"Standards"},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b143","doi-asserted-by":"crossref","first-page":"56","DOI":"10.20517\/ir.2023.03","article-title":"GMAW welding procedure expert system based on machine learning","volume":"3","author":"Wang","year":"2023","journal-title":"Intell. Robot."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b144","doi-asserted-by":"crossref","first-page":"22","DOI":"10.3390\/jmmp8010022","article-title":"Review on the application of the attention mechanism in sensing information processing for dynamic welding processes","volume":"8","author":"Xu","year":"2024","journal-title":"J. Manuf. Mater. Process."},{"issue":"12","key":"10.1016\/j.rcim.2026.103375_b145","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1016\/j.conengprac.2011.07.015","article-title":"Welding current and arc voltage control in a GMAW process using ARMarkov based MPC","volume":"19","author":"Anzehaee","year":"2011","journal-title":"Control Eng. Pract."},{"key":"10.1016\/j.rcim.2026.103375_b146","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.jmapro.2022.08.049","article-title":"Effect of closed-loop controlled melt pool width on microstructure and tensile property for Fe-Ni-Cr alloy in directed energy deposition","volume":"82","author":"Su","year":"2022","journal-title":"J. Manuf. Process."},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b147","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1007\/s00170-021-07682-3","article-title":"Review on model predictive control: An engineering perspective","volume":"117","author":"Schwenzer","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"5","key":"10.1016\/j.rcim.2026.103375_b148","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.conengprac.2008.10.002","article-title":"Closed-loop model re-identification of processes under MPC with zone control","volume":"17","author":"Sotomayor","year":"2009","journal-title":"Control Eng. Pract."},{"key":"10.1016\/j.rcim.2026.103375_b149","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.jmapro.2022.12.013","article-title":"Closed-loop control of thin plate TIG butt welding with reserved gap based on fusion hole","volume":"85","author":"Li","year":"2023","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b150","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cirpj.2020.03.004","article-title":"Real time monitoring and control of friction stir welding process using multiple sensors","volume":"30","author":"Mishra","year":"2020","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"10.1016\/j.rcim.2026.103375_b151","series-title":"2016 Chinese Control and Decision Conference","first-page":"3646","article-title":"The model reference adaptive control method of submerged arc welding power supply system","author":"Liu","year":"2016"},{"key":"10.1016\/j.rcim.2026.103375_b152","doi-asserted-by":"crossref","first-page":"192282","DOI":"10.1109\/ACCESS.2020.2995744","article-title":"Open-closed-loop iterative learning control with a self-adjustive factor of the gas tungsten arc welding (GTAW) process","volume":"8","author":"Yin","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.rcim.2026.103375_b153","article-title":"Welding process tracking control based on multiple model iterative learning control","volume":"2019","author":"Li","year":"2019","journal-title":"Math. Probl. Eng."},{"issue":"7","key":"10.1016\/j.rcim.2026.103375_b154","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.1007\/s00170-020-05998-0","article-title":"Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing","volume":"110","author":"Xia","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"9","key":"10.1016\/j.rcim.2026.103375_b155","doi-asserted-by":"crossref","first-page":"3929","DOI":"10.1007\/s00170-019-04142-x","article-title":"Weld penetration in situ prediction from keyhole dynamic behavior under time-varying VPPAW pools via the OS-ELM model","volume":"104","author":"Wu","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.rcim.2026.103375_b156","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.measurement.2018.12.074","article-title":"Spectral diagnosis and defects prediction based on ELM during the GTAW of Al alloys","volume":"136","author":"Huang","year":"2019","journal-title":"Measurement"},{"key":"10.1016\/j.rcim.2026.103375_b157","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.jmapro.2026.02.018","article-title":"An incremental learning method for resistance projection welding quality evaluation in complex multi-condition working environments","volume":"162","author":"Zhou","year":"2026","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b158","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.116407","article-title":"Online detection of weld surface defects based on improved incremental learning approach","volume":"195","author":"Wang","year":"2022","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b159","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1007\/s40194-023-01641-0","article-title":"Reinforcement learning as data-driven optimization technique for GMAW process","volume":"68","author":"Mattera","year":"2024","journal-title":"Weld. World"},{"issue":"6","key":"10.1016\/j.rcim.2026.103375_b160","doi-asserted-by":"crossref","first-page":"4331","DOI":"10.1007\/s10845-024-02465-5","article-title":"Optimization framework of laser oscillation welding based on a deep predictive reward reinforcement learning net","volume":"36","author":"Tian","year":"2025","journal-title":"J. Intell. Manuf."},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b161","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1007\/s10845-023-02307-w","article-title":"Optimal data-driven control of manufacturing processes using reinforcement learning: An application to wire arc additive manufacturing","volume":"36","author":"Mattera","year":"2025","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b162","first-page":"11","article-title":"Deep reinforcement learning for optimal control of induction welding process","volume":"41","author":"Surana","year":"2024","journal-title":"Manuf. Lett."},{"key":"10.1016\/j.rcim.2026.103375_b163","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1007\/s40194-025-02161-9","article-title":"Robotizing GTAW through learning human response","volume":"70","author":"Cao","year":"2026","journal-title":"Weld. World"},{"key":"10.1016\/j.rcim.2026.103375_b164","series-title":"2024 9th International Conference on Control and Robotics Engineering","first-page":"27","article-title":"Collision-free welding torch pose determination through imitation learning with expert demonstrations","author":"Bhuiyan","year":"2024"},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b165","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/JAS.2020.1003518","article-title":"Digital twin for human-robot interactive welding and welder behavior analysis","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"issue":"9","key":"10.1016\/j.rcim.2026.103375_b166","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1007\/s42452-025-07180-1","article-title":"A comprehensive review of power converter topologies and control techniques for welding power supply applications","volume":"7","author":"Hassan","year":"2025","journal-title":"Discov. Appl. Sci."},{"key":"10.1016\/j.rcim.2026.103375_b167","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.jmsy.2022.09.012","article-title":"Towards intelligent welding systems from a HCPS perspective: A technology framework and implementation roadmap","volume":"65","author":"Wang","year":"2022","journal-title":"J. Manuf. Syst."},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b168","doi-asserted-by":"crossref","first-page":"44","DOI":"10.3390\/jmmp10020044","article-title":"Machine learning for in situ quality assessment and defect diagnosis in refill friction stir spot welding","volume":"10","author":"Andersen","year":"2026","journal-title":"J. Manuf. Mater. Process."},{"key":"10.1016\/j.rcim.2026.103375_b169","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.jmapro.2020.04.043","article-title":"Study on the intelligent model database modeling the laser welding for aerospace aluminum alloy","volume":"63","author":"He","year":"2021","journal-title":"J. Manuf. Process."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b170","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1038\/s41598-024-52451-3","article-title":"Research on surface defect detection algorithm of pipeline weld based on YOLOv7","volume":"14","author":"Xu","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.rcim.2026.103375_b171","first-page":"1","article-title":"YOLO-MSAPF: Multiscale alignment fusion with parallel feature filtering model for high accuracy weld defect detection","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b172","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/s10479-023-05405-3","article-title":"Weld defect detection with convolutional neural network: an application of deep learning","volume":"350","author":"Madhav","year":"2025","journal-title":"Ann. Oper. Res."},{"key":"10.1016\/j.rcim.2026.103375_b173","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.jmsy.2021.12.004","article-title":"Multi-sensing signals diagnosis and CNN-based detection of porosity defect during Al alloys laser welding","volume":"62","author":"Ma","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.rcim.2026.103375_b174","first-page":"1","article-title":"An automatic deep segmentation network for pixel-level welding defect detection","volume":"71","author":"Yang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.rcim.2026.103375_b175","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3154814","article-title":"Deep Siamese semantic segmentation network for PCB welding defect detection","volume":"71","author":"Ling","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b176","doi-asserted-by":"crossref","first-page":"664","DOI":"10.3390\/s25030664","article-title":"Aluminum reservoir welding surface defect detection method based on three-dimensional vision","volume":"25","author":"Huang","year":"2025","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b177","doi-asserted-by":"crossref","DOI":"10.1080\/17452759.2023.2294336","article-title":"Detection and quantitative evaluation of surface defects in wire and arc additive manufacturing based on 3D point cloud","volume":"19","author":"Liu","year":"2024","journal-title":"Virtual Phys. Prototyp."},{"key":"10.1016\/j.rcim.2026.103375_b178","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114174","article-title":"Multiscale welding defect detection method based on image adaptive enhancement","volume":"327","author":"Cheng","year":"2025","journal-title":"Knowl.-Based Syst."},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b179","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TMECH.2023.3327713","article-title":"An X-ray-based automatic welding defect detection method for special equipment system","volume":"29","author":"Zuo","year":"2023","journal-title":"IEEE\/ASME Trans. Mechatronics"},{"key":"10.1016\/j.rcim.2026.103375_b180","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112640","article-title":"Ultrasonic internal multiple migration: An imaging method for defect characterization in complex-structure welds","volume":"231","author":"Xu","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.rcim.2026.103375_b181","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2022.111815","article-title":"Study on the mechanical properties and defect detection of low alloy steel weldments for large cruise ships","volume":"258","author":"Liu","year":"2022","journal-title":"Ocean Eng."},{"key":"10.1016\/j.rcim.2026.103375_b182","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.112821","article-title":"A state-of-the-art survey of welding radiographic image analysis: Challenges, technologies and applications","volume":"214","author":"Liu","year":"2023","journal-title":"Measurement"},{"issue":"20","key":"10.1016\/j.rcim.2026.103375_b183","doi-asserted-by":"crossref","first-page":"6553","DOI":"10.3390\/s24206553","article-title":"Ultrasonic weld quality inspection involving strength prediction and defect detection in data-constrained training environments","volume":"24","author":"Mohandas","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.rcim.2026.103375_b184","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcsr.2025.109583","article-title":"Monitoring of weld damage based on deep learning and acoustic emission methods","volume":"231","author":"Ma","year":"2025","journal-title":"J. Constr. Steel Res."},{"key":"10.1016\/j.rcim.2026.103375_b185","article-title":"Effect of friction stir welding parameters on microstructure-based defect formation and mechanical properties of tailor welded blanks","volume":"33","author":"Majeed","year":"2022","journal-title":"Mater. Today Commun."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b186","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s00170-022-08700-8","article-title":"An artificial neural network approach for parametric study on welding defect classification","volume":"120","author":"Ho","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.rcim.2026.103375_b187","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1007\/s12008-025-02478-1","article-title":"Explainable deep learning approaches for classifying welding defects\u2014integrating CNN architectures with XAI","volume":"20","author":"Satsangi","year":"2026","journal-title":"Int. J. Interact. Des. Manuf. (IJIDeM)"},{"key":"10.1016\/j.rcim.2026.103375_b188","doi-asserted-by":"crossref","first-page":"99240","DOI":"10.1109\/ACCESS.2024.3430113","article-title":"Detection of manufacturing defects in steel using deep learning with explainable artificial intelligence","volume":"12","author":"Aboulhosn","year":"2024","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.rcim.2026.103375_b189","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1007\/s10845-023-02108-1","article-title":"Comparison and explanation of data-driven modeling for weld quality prediction in resistance spot welding","volume":"35","author":"Russell","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.rcim.2026.103375_b190","doi-asserted-by":"crossref","DOI":"10.1016\/j.istruc.2025.110012","article-title":"Machine learning-based Shapley additive explanations method for fatigue failure mode identification of load-carrying welded cruciform joints","volume":"80","author":"Gu","year":"2025","journal-title":"Structures"},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b191","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3390\/jmmp6010010","article-title":"Causal discovery in manufacturing: A structured literature review","volume":"6","author":"Vukovi\u0107","year":"2022","journal-title":"J. Manuf. Mater. Process."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b192","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1038\/s41597-025-06067-5","article-title":"A dataset of fatigue properties for welded joints","volume":"12","author":"Deng","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.rcim.2026.103375_b193","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.ymssp.2014.12.021","article-title":"Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding","volume":"60","author":"Zhang","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b194","first-page":"237","article-title":"Federated learning for weld quality prediction","volume":"3","author":"Chakraborty","year":"2024","journal-title":"J. Dyn. Monit. Diagn."},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b195","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10845-025-02569-6","article-title":"Handling data drift in deep learning-based quality monitoring: Evaluating calibration methods using the example of friction stir welding","volume":"37","author":"Bauer","year":"2026","journal-title":"J. Intell. Manuf."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b196","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s10723-024-09785-z","article-title":"Adapting to changes: A novel framework for continual machine learning in industrial applications","volume":"22","author":"Antony","year":"2024","journal-title":"J. Grid Comput."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b197","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TII.2024.3456671","article-title":"Meta-learning-based domain generalization for cost-effective tool condition monitoring in ultrasonic metal welding","volume":"21","author":"Meng","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"22","key":"10.1016\/j.rcim.2026.103375_b198","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.3390\/polym17223025","article-title":"Dynamic monitoring method of polymer injection molding product quality based on operating condition drift detection and incremental learning","volume":"17","author":"Shen","year":"2025","journal-title":"Polymers"},{"key":"10.1016\/j.rcim.2026.103375_b199","doi-asserted-by":"crossref","DOI":"10.1016\/j.ndteint.2022.102784","article-title":"Understanding the effect of transfer learning on the automatic welding defect detection","volume":"134","author":"Wang","year":"2023","journal-title":"NDT E Int."},{"key":"10.1016\/j.rcim.2026.103375_b200","series-title":"Technology readiness levels","author":"NASA","year":"2023"},{"key":"10.1016\/j.rcim.2026.103375_b201","series-title":"Technology Readiness Assessment Best Practices Guide","author":"Kimmel","year":"2020"},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b202","first-page":"1","article-title":"Time-sensitive networking (TSN) for industrial automation: Current advances and future directions","volume":"57","author":"Zhang","year":"2024","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b203","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1109\/COMST.2024.3422613","article-title":"Toward the integration and convergence between 5G and TSN technologies and architectures for industrial communications: A survey","volume":"27","author":"Sasiain","year":"2025","journal-title":"IEEE Commun. Surv. Tutorials"},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b204","doi-asserted-by":"crossref","first-page":"758","DOI":"10.3390\/electronics14040758","article-title":"5G-TSN integrated prototype for reliable industrial communication using frame replication and elimination for reliability","volume":"14","author":"Kehl","year":"2025","journal-title":"Electronics"},{"key":"10.1016\/j.rcim.2026.103375_b205","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2023.102581","article-title":"Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition","volume":"84","author":"Chen","year":"2023","journal-title":"Robot. Comput.-Integr. Manuf."},{"issue":"9","key":"10.1016\/j.rcim.2026.103375_b206","doi-asserted-by":"crossref","first-page":"798","DOI":"10.3390\/machines13090798","article-title":"A cloud-edge-end collaborative framework for adaptive process planning by welding robots","volume":"13","author":"Shi","year":"2025","journal-title":"Machines"},{"key":"10.1016\/j.rcim.2026.103375_b207","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jmapro.2026.01.101","article-title":"Data-driven artificial intelligence methods for real-time welding defect diagnosis: A critical review and future outlook","volume":"161","author":"Zhang","year":"2026","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b208","doi-asserted-by":"crossref","DOI":"10.1016\/j.ndteint.2024.103074","article-title":"Towards an in-process ultrasonic phased array inspection method for narrow-gap welds","volume":"144","author":"Nicolson","year":"2024","journal-title":"NDT E Int."},{"issue":"4","key":"10.1016\/j.rcim.2026.103375_b209","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1049\/cps2.12092","article-title":"Cyber-physical-based welding systems: Components and implementation strategies","volume":"9","author":"Sz\u0151l\u0151si","year":"2024","journal-title":"IET Cyber-Physical Syst.: Theory Appl."},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b210","doi-asserted-by":"crossref","first-page":"13","DOI":"10.53375\/ijecer.2023.320","article-title":"RIAWELC: A novel dataset of radiographic images for automatic weld defects classification","volume":"3","author":"Totino","year":"2023","journal-title":"Int. J. Electr. Comput. Eng. Res."},{"key":"10.1016\/j.rcim.2026.103375_b211","doi-asserted-by":"crossref","first-page":"77442","DOI":"10.1109\/ACCESS.2024.3407019","article-title":"LoHi-WELD: A novel industrial dataset for weld defect detection and classification, a deep learning study, and future perspectives","volume":"12","author":"Block da Silva","year":"2024","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.rcim.2026.103375_b212","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s10921-025-01186-w","article-title":"SWRD: A dataset of radiographic image of seam weld for defect detection","volume":"44","author":"Zhao","year":"2025","journal-title":"J. Nondestruct. Eval."},{"key":"10.1016\/j.rcim.2026.103375_b213","doi-asserted-by":"crossref","DOI":"10.3389\/fmtec.2025.1676365","article-title":"WAAM-ViD: Towards universal vision-based monitoring for wire arc additive manufacturing","volume":"5","author":"Kim","year":"2025","journal-title":"Front. Manuf. Technol."},{"key":"10.1016\/j.rcim.2026.103375_b214","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2025.111381","article-title":"Dataset for weld seam analysis and discontinuity prediction in laser beam welding scenarios","volume":"59","author":"Walther","year":"2025","journal-title":"Data Brief"},{"key":"10.1016\/j.rcim.2026.103375_b215","doi-asserted-by":"crossref","DOI":"10.1007\/s40194-026-02376-4","article-title":"A comparative study of deep learning models for welding process analysis using sequential models","author":"El Assal","year":"2026","journal-title":"Weld. World"},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b216","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1038\/s41597-025-04638-0","article-title":"A co-registered in-situ and ex-situ dataset from wire arc additive manufacturing process","volume":"12","author":"Orlyanchik","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.rcim.2026.103375_b217","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-informed machine learning","volume":"3","author":"Karniadakis","year":"2021","journal-title":"Nat. Rev. Phys."},{"issue":"7","key":"10.1016\/j.rcim.2026.103375_b218","doi-asserted-by":"crossref","first-page":"3591","DOI":"10.1007\/s00170-024-14706-1","article-title":"Thermal-fluid modeling and physics-informed machine learning for predicting molten pool depth in single-layer multi-track fiber laser cladding","volume":"135","author":"Hu","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.rcim.2026.103375_b219","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.jmapro.2025.07.057","article-title":"A data-driven framework for molten pool morphology prediction in laser welding with dimensionless numbers discovery","volume":"151","author":"Deng","year":"2025","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.rcim.2026.103375_b220","article-title":"Neural ordinary differential equations","volume":"vol. 31","author":"Chen","year":"2018"},{"key":"10.1016\/j.rcim.2026.103375_b221","doi-asserted-by":"crossref","first-page":"97581","DOI":"10.1109\/ACCESS.2024.3425448","article-title":"A review of differentiable simulators","volume":"12","author":"Newbury","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.rcim.2026.103375_b222","doi-asserted-by":"crossref","first-page":"277","DOI":"10.3390\/s25010277","article-title":"Leveraging segment anything model (SAM) for weld defect detection in industrial ultrasonic B-scan images","volume":"25","author":"Naddaf-Sh","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.rcim.2026.103375_b223","series-title":"WRT-SAM: Foundation model-driven segmentation for generalized weld radiographic testing","author":"Zhou","year":"2025"},{"issue":"24","key":"10.1016\/j.rcim.2026.103375_b224","doi-asserted-by":"crossref","first-page":"4785","DOI":"10.3390\/electronics14244785","article-title":"Zero-shot industrial anomaly detection via CLIP-DINOv2 multimodal fusion and stabilized attention pooling","volume":"14","author":"Jiang","year":"2025","journal-title":"Electronics"},{"key":"10.1016\/j.rcim.2026.103375_b225","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103121","article-title":"Do multimodal large language models understand welding?","volume":"120","author":"Khvatskii","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.rcim.2026.103375_b226","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.116096","article-title":"An efficient welding state monitoring model for robotic welding based on ensemble learning and generative adversarial knowledge distillation","volume":"242","author":"Xiao","year":"2025","journal-title":"Measurement"},{"issue":"18","key":"10.1016\/j.rcim.2026.103375_b227","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.3390\/ma17184441","article-title":"Penetration state recognition during laser welding process control based on two-stage temporal convolutional networks","volume":"17","author":"Liu","year":"2024","journal-title":"Materials"},{"issue":"9","key":"10.1016\/j.rcim.2026.103375_b228","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1007\/s40194-025-02021-6","article-title":"Real-time determination of weld penetration status during A-TIG welding of stainless steel employing deep learning approach","volume":"69","author":"Chandrasekhar","year":"2025","journal-title":"Weld. World"},{"issue":"18","key":"10.1016\/j.rcim.2026.103375_b229","doi-asserted-by":"crossref","first-page":"5921","DOI":"10.3390\/s24185921","article-title":"Cloud-edge collaborative defect detection based on efficient YOLO networks and incremental learning","volume":"24","author":"Lei","year":"2024","journal-title":"Sensors"},{"issue":"12","key":"10.1016\/j.rcim.2026.103375_b230","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.3390\/machines13121140","article-title":"Reinforcement learning for industrial automation: A comprehensive review of adaptive control and decision-making in smart factories","volume":"13","author":"Alginahi","year":"2025","journal-title":"Machines"},{"key":"10.1016\/j.rcim.2026.103375_b231","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/j.jmapro.2024.09.044","article-title":"Human-robot collaborative assembly and welding: A review and analysis of the state of the art","volume":"131","author":"Cao","year":"2024","journal-title":"J. Manuf. Process."}],"container-title":["Robotics and Computer-Integrated Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0736584526001973?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0736584526001973?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T03:02:08Z","timestamp":1789182128000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0736584526001973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,2]]},"references-count":231,"alternative-id":["S0736584526001973"],"URL":"https:\/\/doi.org\/10.1016\/j.rcim.2026.103375","relation":{},"ISSN":["0736-5845"],"issn-type":[{"value":"0736-5845","type":"print"}],"subject":[],"published":{"date-parts":[[2027,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Closed-loop intelligent quality control for robotic welding: A survey on architecture, technologies, and system integration","name":"articletitle","label":"Article Title"},{"value":"Robotics and Computer-Integrated Manufacturing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.rcim.2026.103375","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103375"}}