Quality control and assurance in fabrication of welded structures subjected to fatigue loading

Stenberg, Thomas; Barsoum, Zuheir; Åstrand, Erik; Öberg, Anna Ericson; Schneider, Christian; Hedegård, Joakim

doi:10.1007/s40194-017-0490-5

Cited by 42 publications

(41 citation statements)

References 15 publications

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“…The quality of a welded joint has historically been assessed manually using simple mechanical gauges to evaluate the local geometry at discrete points along a weld bead [8].…”

Section: Digitalized Weld Quality Measurementmentioning

confidence: 99%

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Development of a computational fatigue model for evaluation of weld quality

Stenberg¹,

Barsoum

Hedlund

et al. 2019

Weld World

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The current study focuses on the development of a predictive model for assessing the fatigue life of welded joints based on measured weld geometry and applied load. Two different materials (S355 and S960) and two different material thicknesses (2 mm and 8 mm) were considered. Experiments on cruciform joints were conducted to evaluate the fatigue performance for different types of weld geometries. A computational model based on FEM and linear elastic fracture mechanics was developed and adapted to fit the experimental results using optimization and surrogate models.It is observed that the general fatigue behavior differs for the different materials for the same variation in geometry. The fatigue performance depends on a combination of geometrical parameters. The use of FAT curves according to the weld quality systems, e.g., ISO 5817, is insufficient to describe fatigue properties for welds in thin high strength steel, and different geometries within different weld quality levels can give the same fatigue behavior. It is also concluded that the developed computational model is suitable for further development of weld quality systems.

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“…The quality of a welded joint has historically been assessed manually using simple mechanical gauges to evaluate the local geometry at discrete points along a weld bead [8].…”

Section: Digitalized Weld Quality Measurementmentioning

confidence: 99%

“…Classification of fatigue specimens, mean local weld geometry Winteria® [10] which carries the practice of weld quality assessment into the digital era giving fast and reliable access to the weld information needed for fatigue life assessment of welded structures[8].…”

mentioning

confidence: 99%

Development of a computational fatigue model for evaluation of weld quality

Stenberg¹,

Barsoum

Hedlund

et al. 2019

Weld World

Self Cite

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“…Stenberg et al [8] presented a comprehensive overview of weld quality control and assurance of welded structures where the major failure prevention is due to fatigue loading. They descrubed the drawbacks and limitation of quality control systems, international weld quality standards, and the guidelines used in today's weld production.…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment and Process Management of Welded Joints in Metal Construction—A Review

Péreira

Melo

2020

Metals

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This paper aims to express the maturity and dynamism of welding in industry. It follows the direct observation of international construction standards and rules, here described as an introduction to the most relevant themes in the area of welding processes. Quality management in welded construction involves well-regulated procedures which are, however, so vast that they are of great complexity. In fact, there are hundreds of rules to apply to each specific case. This makes it imperative to have a welding coordinator, at least in cases of greater responsibility. In fact, the application of the rules presents yet another difficulty: along with the breadth of the rules, each country may adopt certain special requirements in their own case. Thus, it is essential to know the necessary path for the proper development of responsible work. First, one must know the various welding processes, their advantages and limitations; then one must identify the regulations applicable to the work concerned; and finally, one must know what tests and qualifications are required, including the expected defects and acceptance criteria. One purpose of this work is to show the necessary path for the implementation of a welding quality management system in metal construction, as well as to present the numerous applicable norms, both for the welder and the welding coordinator, but also for the process and the product. This paper also presents an analysis of how to obtain the CE marking (Conformité Européene) for a welded metal construction and which normative ramifications apply.

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“…Hence, the more stringent weld quality requirements of optimized structures, resulting from the use of thinner materials, high-strength steels and reduction of weld material, are difficult to achieve [1][2][3]. Additionally, the quality and penetration inconsistency, caused by the inability to adapt, affects drastically to fatigue life and strength properties of the weld joint [4][5][6][7]. Traditionally, meeting such quality requirements have been approached by precise machining and fitting of the workpieces.…”

Section: Introductionmentioning

confidence: 99%

“…In the welding production point of view, the similar welds can be done with multiple processes and various different approaches, which increase the quality inconsistency. The inconsistency of the quality creates issues with life cycle analysis and other product properties [7,14,15]. .The welding industry is constantly trying to find solutions that enable automated systems to adapt to varying welding conditions and achieve consistent quality cost-efficiently.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Controlled GMAW System: Penetration and Quality Assurance in a Multi-Pass Butt Weld Application

Penttilä

Kah

Ratava

et al. 2019

Int J Adv Manuf Technol

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Intelligent welding parameter control is fast becoming a key instrument for attaining quality consistency in automated welding. Recent scientific breakthroughs in intelligent systems have turned the focus of adaptive welding control to artificial intelligencebased welding parameter control. The aim of this study is to combine artificial neural network (ANN) decision-making software and a machine vision system to develop an adaptive artificial intelligence (AI)-based gas metal arc welding (GMAW) parameter control system. The machine vision system uses a laser sensor to scan the upcoming seam and gather seam profile data. Based on further processing of the seam profile data, welding parameters are optimized by the decision-making system. In this work, the developed system is tested in a multivariable welding condition environment and its performance is evaluated. The quality of the welds was consistent and surpassed the required quality level. Additionally, the heat-affected zone (HAZ) was evaluated by microscopy, X-ray, and scanning electron microscope (SEM) imaging. It is concluded that the developed ANN system is suitable for implementation in automated applications, can improve quality consistency and cost efficiency, and reduce required workpiece preparation and handling.

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Quality control and assurance in fabrication of welded structures subjected to fatigue loading

Cited by 42 publications

References 15 publications

Development of a computational fatigue model for evaluation of weld quality

Development of a computational fatigue model for evaluation of weld quality

Quality Assessment and Process Management of Welded Joints in Metal Construction—A Review

Artificial Neural Network Controlled GMAW System: Penetration and Quality Assurance in a Multi-Pass Butt Weld Application

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