Defects in Friction Stir Welding of Steel

Al-moussawi, M.; Smith, Alan

doi:10.1007/s13632-018-0438-1

Cited by 43 publications

(18 citation statements)

References 17 publications

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“…However, rather than to characterise the effectiveness of the NDT method, the scope of this work is to develop a computational framework that utilises the NDT method's data within a machine learning framework to enhance flaw detection. As such, we use representative FSW flaw sizes, without the intention of being exhaustive or as close to reality as possible (this would not be possible without utilising actual FSW flaws, mainly due to their irregular shapes [8], [36]). Note that a 0.6 mm thick layer was machined off the FS weld surface before machining the FBHs, to level out the weld's uneven surface.…”

Section: A Experimental Setup and Proceduresmentioning

confidence: 99%

Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach

Atwya

Panoutsos

2020

IEEE Trans. Ind. Inf.

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A systematic computational method to simulate and detect sub-surface flaws, through non-destructive transient thermography, in aluminium sheets and friction stir welded sheets is proposed. The proposed method relies on feature extraction methods and a data-driven machine learning modelling structure. In this work, we propose the use of a multi-layer perceptron feed-forward neural-network with feature extraction methods to improve the flaw-probing depth of transient thermography inspection. Furthermore, for the first time, we propose Thermographic Signal Linear Modelling (TSLM), a hyper-parameterfree feature extraction technique for transient thermography. The new feature extraction and modelling framework was tested with out-of-sample experimental transient thermography data and results show effectiveness in sub-surface flaw detection of up to 2.3 mm deep in aluminium sheets (99.8 % true positive rate, 92.1 % true negative rate) and up to 2.2 mm deep in friction stir welds (97.2 % true positive rate, 87.8 % true negative rate).

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Section: A Experimental Setup and Proceduresmentioning

confidence: 99%

Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach

Atwya

Panoutsos

2020

IEEE Trans. Ind. Inf.

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“…Zhang et al [33] observed that increasing the rotational speed to more than 1400 rpm caused the formation of voids in the SZ. Kim et al [34], Ren et al [35], and Moussawi et al [36] studied the influence of rotational and traverse speeds on defects formation during FSP of aluminum alloys. In this regard, investigating the thermal aspects (peak temperature and thermal distribution) of FSP has attracted attention.…”

Section: Introductionmentioning

confidence: 99%

Numerical and experimental investigation of defects formation during friction stir processing on AZ91

et al. 2021

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The heat generated during friction stir processing greatly affects defects formation in the processed zone of workpieces. In this paper, numerical modeling of this process is performed to determine the influence of tool rotational and traverse speeds and hence their ratio on the thermal distribution attained during the process. The aim is to produce defect-free processed samples by selecting adequate tool speeds. The mechanisms of defects formation depending on the peak temperature are also investigated. Experiments to verify the simulation results were conducted with the same process parameters. Several traverse speeds of 20, 40, 60, and 80 mm/min and rotational speeds of 700, 1000, 1200, and 2000 rpm were used during modeling and conducting the experiments. From the numerical and experimental results, it was found that; the high-speed processing conditions (low-generated heat) can produce defects such as tunnels and grooves, and the low-speed processing conditions (high-generated heat) can cause defects such as flashes. The experimental results show that during friction stir processing with the rotational speed of 1200 rpm and the traverse speed of 60 mm/min (speed ratio of 20), no macro defects in the processed zone were observed. According to the numerical results, the peak temperature during friction stir processing with these speeds was 475 °C. At this temperature, the material softened, the structure finely equiaxed and no large scale melting zone appeared in the processed zone. The developed model can be useful to investigate the occurrence of defects associated with different tool rotational and traverse speeds. Graphic abstract

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“…Furthermore, the near-surface stress concentration and the friction stir welded joint defect such as voids in the bottom of the welded joint, kissing bonds cracks, root sticking, weld root flaw, etc. will shorten the fatigue life [9]. Therefore, deep-rolling and post-weld heat treatment processes are used to improve FSW workpieces, making them stronger and giving them a longer fatigue life.…”

Section: Introductionmentioning

confidence: 99%

Sequential Effects of Deep Rolling and Post-Weld Heat Treatment on Surface Integrity of AA7075-T651 Aluminum Alloy Friction Stir Welding

Baisukhan

Nakkiew

2019

Materials

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The aim of this research is to investigate the sequence of processes for improving the welded surface integrity of AA7075-T651 aluminum alloy joined by friction stir welding (FSW). The improvement processes that will be investigated herein include mechanical surface improvement with deep rolling (DR) and post-weld heat treatment (PWHT). Therefore, this study investigated welded surface integrity, which comprises residual stress, microhardness, surface roughness, microstructure, and fatigue life (screening). The experiment consists of three sets of combinations. In the first set, only FSW was applied; in the second set, FSW was applied, followed by DR, and then PWHT processes (FSW-DR-PWHT); and in the last set, FSW was applied, followed by PWHT, and then DR processes (FSW-PWHT-DR). Fatigue testing was carried out by undertaking a four-point bending test using a bending stress of approximately 300 MPa with a test frequency of 2.5 Hz at room temperature and stress ratio R = 0. The study found that residual stress plays an important role in the fatigue life. Finally, the fatigue test showed that FSW workpieces subject to the PWHT process followed by the DR process (FSW-PWHT-DR) had the highest fatigue life, with an increase of 239% when compared with unprocessed FSW workpieces.

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Defects in Friction Stir Welding of Steel

Cited by 43 publications

References 17 publications

Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach

Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach

Numerical and experimental investigation of defects formation during friction stir processing on AZ91

Sequential Effects of Deep Rolling and Post-Weld Heat Treatment on Surface Integrity of AA7075-T651 Aluminum Alloy Friction Stir Welding

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