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.

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A B S T R A C T Analysis of welded structures still remains a challenge for the analyst and in fact cannot be considered as fully solved for practical applications. For many years, a large international aggregation of researchers has developed methods to assess fatigue behaviour of welded structures. Nowadays many suggestions and methods exist to estimate fatigue life of welded structures with respect to nominal, structural, notch stress or fracture mechanics approaches. All of them are still under improvement. The high motivation and many activities of experts in the International Institute of Welding (IIW) group of researchers is a good demonstration of the complexity and need for analysis methods in that field. The purpose of this paper is to provide some discussion on selected methods available. Both authors are giving lectures to transfer methods to industrial applications. It is their experience that a large amount of knowledge has been developed although proper applications require some grading and comments on the use of those methods. This paper should give some comments and recommendations for the practical application of a selection of methods already available. A hierarchical two-step procedure for the assessment of large welded structures will be described and recommended. Also benchmark results are presented on a sample structure for sake of comparison of a few selected methods. Finally a presentation of results obtained by application of selected methods on real structures in comparison with fatigue lives from experiments will be presented. The methods selected within the paper cover the approaches for modelling, structural analysis and assessment of welded structures using finite element analysis (FEA) and stress based concepts for fatigue life estimation. a = weld throat thickness e = element size e 1 = element size of 1st row of elements adjacent to hot spot e 2 = element size of 2nd row of elements adjacent to hot spot k = slope of component fatigue curve l 1 , l 2 , l 3 = distances for structural stress assessment using extrapolation schemes r CAB = weld radius according to CAB concept t = wall thickness A = area F x = force in x-direction F y = force in y-direction K t,r=1 = notch factor for weld toe radius r = 1 mm Correspondence: K. Rother.

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Multi-Objective Topology Optimization is a tool for finding lowweight solutions using a discretized geometry of several objective functions. In this contribution, the coupling of heat conduction and elastostatics is covered using the global criteria method. For the comparison of the different objectives and objective distributions typically the single target optimized solutions are selected as normalization criteria [1], [2]. The use of these optimized results requires additional calculation effort, so that this work uses the objective distribution of full material properties instead. For the normalization, the objective distributions will be standardized, and quantile normalized. The normalization strategies are compared to each other by the number of iterations and the resulting objective value.