Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal.

Haijun Wang, Diqiu He, Mingjian Liao, Peng Liu, Ruilin Lai

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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1 citing paper in PubMed.

Effect of Natural Aging on Cold Forming Performance of 2219 Aluminum Alloy.Materials (Basel, Switzerland) · 2023
Article

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5 · Who and what money

Authors and funding

5 authors.

Haijun WangState Key Laboratory of High-Performance Complex Manufacturing, Light Alloy Research Institute of Central South University, Changsha 410083, China.

Diqiu HeState Key Laboratory of High-Performance Complex Manufacturing, Light Alloy Research Institute of Central South University, Changsha 410083, China.

Mingjian LiaoState Key Laboratory of High-Performance Complex Manufacturing, Light Alloy Research Institute of Central South University, Changsha 410083, China.

Peng LiuState Key Laboratory of High-Performance Complex Manufacturing, Light Alloy Research Institute of Central South University, Changsha 410083, China.

Ruilin LaiPowder Metallurgy Research Institute, Central South University, Changsha 410083, China.

Funding

National Key Research and Development Project of China 2019YFA0709000

6 · The paper itself

Abstract

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

Indexed as

AA2219-T6friction stir weldingtemperature signalwavelet packetweld quality prediction

Identifiers

PMID34201644

DOI10.3390/ma14133496

PMCPMC8269529

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