Braun, MoritzMoritzBraunNeuhäusler, JosefJosefNeuhäuslerDenk, MartinMartinDenkRenken, FinnFinnRenkenKellner, LeonLeonKellnerSchubnell, JanJanSchubnellJung, MatthiasMatthiasJungRother, KlemensKlemensRotherEhlers, SörenSörenEhlers2022-06-302022-06-302022-06-15Applied Sciences 12 (12): 6089 (2022)http://hdl.handle.net/11420/12976In order to ensure high weld qualities and structural integrity of engineering structures, it is crucial to detect areas of high stress concentrations along weld seams. Traditional inspection methods rely on visual inspection and manual weld geometry measurements. Recent advances in the field of automated measurement techniques allow virtually unrestricted numbers of inspections by laser measurements of weld profiles; however, in order to compare weld qualities of different welding processes and manufacturers, a deeper understanding of statistical distributions of stress concentrations along weld seams is required. Hence, this study presents an approach to statistically characterize different types of butt joint weld seams. For this purpose, an artificial neural network is created from 945 finite element simulations to determine stress concentration factors at butt joints. Besides higher quality of predictions compared to empirical estimation functions, the new approach can directly be applied to all types welded structures, including arc- and laser-welded butt joints, and coupled with all types of 3D-measurement devices. Furthermore, sheet thickness ranging from 1 mm to 100 mm can be assessed.In order to ensure high weld qualities and structural integrity of engineering structures, it is crucial to detect areas of high stress concentrations along weld seams. Traditional inspection methods rely on visual inspection and manual weld geometry measurements. Recent advances in the field of automated measurement techniques allow virtually unrestricted numbers of inspections by laser measurements of weld profiles; however, in order to compare weld qualities of different welding processes and manufacturers, a deeper understanding of statistical distributions of stress concentrations along weld seams is required. Hence, this study presents an approach to statistically characterize different types of butt joint weld seams. For this purpose, an artificial neural network is created from 945 finite element simulations to determine stress concentration factors at butt joints. Besides higher quality of predictions compared to empirical estimation functions, the new approach can directly be applied to all types welded structures, including arc- and laser-welded butt joints, and coupled with all types of 3D-measurement devices. Furthermore, sheet thickness ranging from 1 mm to 100 mm can be assessed.en2076-3417202212Multidisciplinary Digital Publishing Institutehttps://creativecommons.org/licenses/by/4.0/local weld toe geometryweld classification3-D scansnon-destructive testingstatistical assessmentmachine learningfatigue strengthstress concentration factorweld qualityartificial neural networkMLE@TUHHTechnikIngenieurwissenschaftenJournal Article2022-06-2310.15480/882.442910.3390/app1212608910.15480/882.4429Journal Article