Xiao-guang Sun, Peng Lin, Cheng Man, Jian Cui, Hai-bo Wang, Chao-fang Dong, and Xiao-gang Li, Prediction model for atmospheric corrosion of 7005-T4 aluminum alloy in industrial and marine environments, Int. J. Miner. Metall. Mater., 25(2018), No. 11, pp. 1313-1319. https://doi.org/10.1007/s12613-018-1684-6
Cite this article as:
Xiao-guang Sun, Peng Lin, Cheng Man, Jian Cui, Hai-bo Wang, Chao-fang Dong, and Xiao-gang Li, Prediction model for atmospheric corrosion of 7005-T4 aluminum alloy in industrial and marine environments, Int. J. Miner. Metall. Mater., 25(2018), No. 11, pp. 1313-1319. https://doi.org/10.1007/s12613-018-1684-6
Research Article

Prediction model for atmospheric corrosion of 7005-T4 aluminum alloy in industrial and marine environments

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  • Corresponding author:

    Xiao-guang Sun    E-mail: sunxiaoguang@cqsf.com

  • Received: 7 February 2018Revised: 14 June 2018Accepted: 15 June 2018
  • Accelerated corrosion tests of the 7005-T4 aluminum alloy were conducted to determine a suitable service life prediction method by using alternating wet-dry cycles in three kinds of solutions. The morphology and composition analysis of the corrosion product revealed that slight corrosion occurred on the surfaces of the samples immersed in a 0.25wt% Na2S2O8 solution. However, pitting corrosion occurred on the surfaces of the samples immersed in a 3.5wt% NaCl solution, whereas exfoliation corrosion occurred on the surfaces of the samples immersed in a mixture of 0.25wt% Na2S2O8 and 3.5wt% NaCl solutions. A power exponent relationship was observed between the mass loss and exposure time of the 7005-T4 aluminum alloy immersed in the three kinds of solutions. In the mixture of 0.25wt% Na2S2O8 and 3.5wt% NaCl solutions, the mass loss of the aluminum alloy yielded the maximum value. Based on the calculation of the correlation coefficients, the alternating wet-dry procedure in a 3.5wt% NaCl solution could be used to predict the corrosion behavior of 7005-T4 aluminum alloy exposed in the atmosphere of Qingdao, China. The prediction model is as follows:T=104.28·t0.91, where T is the equivalent time and t is the exposure time.
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