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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.1
- 수록면
- 62 - 68 (7page)
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Artificial neural network techniques show an excellent ability to predict the data (output) for various com plex characteristics (input). It is primarily specialized to solve nonlinear relationship problems. This studyis an experimental investigation that applies artificial neural network techniques and an experimentaldesign to predict the cyclic polarization curves of the super-austenitic stainless steel AL-6XN alloy withsensitization. A cyclic polarization test was conducted in a 3.5% NaCl solution based on an experimentaldesign matrix with various factors (degree of sensitization, temperature, pH) and their levels, and a total of36 cyclic polarization data were acquired. The 36 cyclic polarization patterns were used as training data forthe artificial neural network model. As a result, the supervised learning algorithms with back-propagationshowed high learning and prediction performances. The model showed an excellent training performance(R2=0.998) and a considerable prediction performance (R2=0.812) for the conditions that were not includedin the training data.
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참고문헌 (11)
참고문헌 신청
Z. Cui / 2019 / Passivation behavior and surface chemistry of 2507 super duplex stainless steel in artificial seawater:Influence of dissolved oxygen and pH / Corrosion Science 150:218
R. T. Loto / 2019 / Effect of elevated temperature variations on the corrosion resistance of S31603 and SS2562 austenitic stainless steels in chloride-sulphate environments / Journal of Materials Research and Technology 8:5415
장형민 / 2020 / 304L 스테인리스강의 열처리에 따른 입계부식민감도 연구 / CORROSION SCIENCE AND TECHNOLOGY 19(1):37~42
Z. G. Song / 2017 / Precipitated phases of superaustenitic stainless steel 654SMO / Journal of Iron and Steel Research International 24:743
Q. Hu / 2019 / Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network / Journal ofMaterials Science & Technology 35:168
R. T. Loto / 2019 / Effect of elevated temperature variations on the corrosion resistance of S31603 and SS2562 austenitic stainless steels in chloride-sulphate environments / Journal of Materials Research and Technology 8:5415
장형민 / 2020 / 304L 스테인리스강의 열처리에 따른 입계부식민감도 연구 / CORROSION SCIENCE AND TECHNOLOGY 19(1):37~42
Z. G. Song / 2017 / Precipitated phases of superaustenitic stainless steel 654SMO / Journal of Iron and Steel Research International 24:743
Q. Hu / 2019 / Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network / Journal ofMaterials Science & Technology 35:168
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