Weld defects and quality control

Mathers, Gene

doi:10.1533/9781855737631.199

Cited by 6 publications

(7 citation statements)

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“…Cracking phenomena in these alloys during welding have been attributed to specific characteristics, e.g. differences between solidus and liquidus temperatures, the coefficient of thermal expansion (CTE), solidification shrinkage, and poor fluidity of the molten phase [125,126]. Further, they have relatively large freezing ranges (Figure 11) in comparison with the near-eutectic AlSiMg alloy (Figure 2 (b)).…”

Section: Wrought Al Alloys In Ammentioning

confidence: 99%

A review of Laser Powder Bed Fusion Additive Manufacturing of aluminium alloys: Microstructure and properties

Kotadia

Gibbons

Das

et al. 2021

Additive Manufacturing

145

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Section: Wrought Al Alloys In Ammentioning

confidence: 99%

A review of Laser Powder Bed Fusion Additive Manufacturing of aluminium alloys: Microstructure and properties

Kotadia

Gibbons

Das

et al. 2021

Additive Manufacturing

145

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“…However, defect-free weld cannot be guaranteed and fault may occur. 26 types of defects are listed in [11]. The four most common of them that are usually encountered and used for classification studies, are lack of penetration, lack of fusion, slag inclusion and excess penetration [11].…”

Section: Weld Defectsmentioning

confidence: 99%

“…Possible causes are: current too low, travel speed too high, incorrect weld preparation, root gap too small, incorrect torch angle and misalignment [11].…”

Section: Lack Of Penetrationmentioning

confidence: 99%

“…Possible causes are: current too low, travel speed too high, incorrect torch angle, oxide film on prepared surfaces, inadequate joint cleaning and too narrow weld preparation [11].…”

Section: Lack Of Fusionmentioning

confidence: 99%

“…Current too high, travel speed too low, root gap too wide and root face too thin are the defect causes [11].…”

Section: Excess Penetration (Exp)mentioning

confidence: 99%

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Performance Evaluation of Neural Network Based Pulse-Echo Weld Defect Classifiers

Seyedtabaii¹

2012

Measurement Science Review

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Pulse-echo ultrasonic signal is used to detect weld defects with high probability. However, utilizing echo signal for defects classification is another issue that has attracted attention of many researchers who have devised algorithms and tested them against their own databases. In this paper, a study is conducted to score the performance of various algorithms against a single echo signal database. Algorithms tested the use of Wavelet Transform (WT), Fast Fourier Transform (FFT) and time domain echo signal features and employed several NN's architectures such as Multi-Layer Perceptron Neural Network (MLP), Self Organizing Map (SOM) and others known to be good classifiers. The average performance of all can be viewed fair (90%) while some algorithms render success rate of about 94%. It seems that acquiring higher success rates out of a single fixed angle probe pulseecho set up needs new arrangements of data collection, which is under investigation.

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High‐throughput preparation for alloy composition design in additive manufacturing: A comprehensive review

Liu,

Lei,

Wang

et al. 2024

Materials Genome Engineering Advances

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Additive Manufacturing (AM) is revolutionizing aerospace, transportation, and biomedical sectors with its potential to create complex geometries. However, the metallic materials currently used in AM are not intended for high‐energy beam processes, suggesting performance improvement. The development of materials for AM still faces challenge because of the inefficient trial‐and‐error conventional methods. This review examines the challenges and current state of materials including aluminum alloys, titanium alloys, superalloys, and high‐entropy alloys (HEA) in AM, and summarizes the high‐throughput methods in alloy development for AM. In addition, the advantages of high‐throughput preparation technology in improving the properties and optimizing the microstructure mechanism of major additive manufacturing alloys are described. This article concludes by emphasizing the importance of high‐throughput techniques in pushing the boundaries of AM materials development, pointing toward a future of more effective and innovative material solutions.

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Weld defects and quality control

Cited by 6 publications

References 0 publications

A review of Laser Powder Bed Fusion Additive Manufacturing of aluminium alloys: Microstructure and properties

A review of Laser Powder Bed Fusion Additive Manufacturing of aluminium alloys: Microstructure and properties

Performance Evaluation of Neural Network Based Pulse-Echo Weld Defect Classifiers

High‐throughput preparation for alloy composition design in additive manufacturing: A comprehensive review

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