Effects of tool wear on the microstructure evolution and mechanical properties of friction stir welded TA5 alloy

Du, Shuaishuai; Liu, Huijie; Jiang, Minghao; Zhou, Li; Li, Dongrui; Gao, Yisong

doi:10.1007/s00170-021-08278-7

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…However, FSW/P tools are subjected to severe stress and high temperatures, leading to tool wear (degradation) [ 7 ] or fracture [ 8 ] which causes concerns about consistency of product quality. For example, a reduction in FSW/P tool length can lead to the lack of penetration (Figure S1 (C 1 ) and (C 2 ), Supporting Information); the inadequate material flow can manifest as volumetric defects in the weld and process zone (Figure S1 (D 1‐3 ), (E 1‐3 ) and (F 1‐3 ), Supporting Information).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, favorable microstructures such as refined grains and second phases can be obtained from the severe plastic deformation introduced by the FSW/P tool involving temperature, mechanics, metallurgy, and interactions. [6] However, FSW/P tools are subjected to severe stress and high temperatures, leading to tool wear (degradation) [7] or fracture [8] which causes concerns about consistency of product quality. For example, a reduction in FSW/P tool length can lead to the lack of penetration (Figure S1 (C 1 ) and (C 2 ), Supporting Information); the inadequate material flow can manifest as volumetric defects in the weld and process zone (Figure S1 (D 1-3 ), (E 1-3 ) and (F 1-3 ), Supporting Information).…”

mentioning

confidence: 99%

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Numerically Trained Ultrasound AI for Monitoring Tool Degradation

Jin

Wang

Fox

et al. 2022

Advanced Intelligent Systems

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Monitoring tool degradation during manufacturing can ensure product accuracy and reliability. However, due to variations in degradation conditions and complexity in signal analysis, effective and broadly applicable monitoring is still challenging to achieve. Herein, a novel monitoring method using ultrasound signals augmented with a numerically trained machine learning technique is reported to monitor the wear condition of friction stir welding and processing tools. Ultrasonic signals travel axially inside the tools, and even minor tool wear will change the time and amplitude of the reflected signal. An artificial intelligence (AI) algorithm is selected as a suitable referee to identify the small variations in the tool conditions based on the reflected ultrasound signals. To properly train the AI referee, a human‐error‐free data bank using numerical simulation is generated. The simulation models the experimental conditions with high fidelity and can provide comparable ultrasound signals. As a result, the trained AI model can recognize the tool wear from real experiments with subwavelength accuracy prediction of the worn amount on the tool pins.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%