Nondestructive detection of defects in friction stir welded lap joints using computed tomography

Hamade, Ramsey F.; Baydoun, Ahmad

doi:10.1016/j.matdes.2018.11.034

Cited by 31 publications

(17 citation statements)

References 14 publications

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“…For example, the authors in [7] proposed the use of x-ray with an image enhancement methodology to qualitatively assess dissimilar FS welded lap joints of an AL 6061 2.0 mm thick sheet and a zinc-coated steel sheet of 1.0 mm thickness. The work in [12] successfully employ computed tomography (CT) to detect wormhole flaws amongst other flaws in FS welded lap joints of AL alloys 6061-T6 and 1050 1.59 mm thick sheets. However, the authors found that due to the limited resolution of the CT equipment, the CT estimated wormhole flaw cross-sectional area was approximately three times larger than the results of a destructive test (10.9255 mm 2 compared to 3.4345 mm 2 ) [12].…”

Section: Introductionmentioning

confidence: 99%

“…The work in [12] successfully employ computed tomography (CT) to detect wormhole flaws amongst other flaws in FS welded lap joints of AL alloys 6061-T6 and 1050 1.59 mm thick sheets. However, the authors found that due to the limited resolution of the CT equipment, the CT estimated wormhole flaw cross-sectional area was approximately three times larger than the results of a destructive test (10.9255 mm 2 compared to 3.4345 mm 2 ) [12].…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…One way to evaluate the quality of welded joints is to perform nondestructive tests. One of the techniques available is the X-ray computed tomography (CT) [1]. CT tests allow researchers to detect external and internal defects of welded joints such as pores, voids and micro cracks, which can reduce the joints' strength.…”

Section: Introductionmentioning

confidence: 99%

Verification of the Computed Tomography Results of Aluminum Alloy Welded Joint

Malicki¹,

Sobczak

2018

Fatigue of Aircraft Structures

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“…For obtaining full information on the structure of a joint, it is necessary to study longitudinal sections -many layers [13]. An alternative to this are studies using X-ray micro tomography [14,15]. Presented below are the results of the analysis in which a defect was found using this technique of imaging the material structure.…”

Section: Introductionmentioning

confidence: 99%

NON-INVASIVE ANALYSIS AND VISUALIZATION OF OBJECTS FROM FSW AlSi9Mg/2017A ALUMINUM ALLOY JOINTS

Wójcicka¹,

Mroczka²,

Kowalski³

2018

mafe

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Friction stir welding (FSW) technology allows for the welding of materials that differ in their chemical compositions, microstructures, and properties. When selecting the parameters, it is necessary to analyze the structure of a weld and eliminate the defects. The use of X-ray microtomography creates the possibility of precise imaging of a joint structure-especially when it contains defects (voids). The tests were carried out on an FSW joint made with both 2017A and AlSi9Mg aluminum alloys. The macrostructure was cross-sectional, and a defect was found in the middle part of the joint and on the advancing side. We used a conventional method of observation light microscopy. Then, an analysis was performed using X-ray microtomography, which revealed the shape of the defect in 3D. Individual cross-sections were extracted, which enabled us to measure the geometric quantities. A dependence was found for the shape of the defect on the welding parameters and the variability of its construction. The usefulness of this advanced material imaging technology for the analysis of FSW welds has been confirmed.

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Nondestructive detection of defects in friction stir welded lap joints using computed tomography

Cited by 31 publications

References 14 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

Verification of the Computed Tomography Results of Aluminum Alloy Welded Joint

NON-INVASIVE ANALYSIS AND VISUALIZATION OF OBJECTS FROM FSW AlSi9Mg/2017A ALUMINUM ALLOY JOINTS

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