Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

Zirconium(Zr) alloys are commonly used in the nuclear industry for applications such as fuel cladding and pressure tubes. To minimize the levels and volumes of radioactive waste, molten salts have been employed for decontaminating Zr alloys. Recently, a two-step Zr metal recovery process, combining electrolysis and thermal decomposition, has been proposed. In the electrolysis process, potentiostatic electrorefining is utilized to control the chemical form of electrodeposits(ZrCl). Although Zr metals are expected to dissolve into molten salts, reductive alloy elements can also be co-dissolved and deposited on the cathode. Therefore, a better understanding of the anodic side’s response during potentiostatic electrorefining is necessary to ensure the purity of recovered Zr and long-term process operation. As the first step, potentiodynamic polarization curves were obtained using Zr, Nb, and Zr-Nb alloy to investigate the anodic dissolution behavior in the molten salts. Nb, which has a redox potential close to Zr, and Zr exhibit active or passivation dissolution mechanisms depending on the potential range. It was confirmed that Zr-Nb alloy also has a passivation region between -0.223 to -0.092 V influenced by the major elements Zr and Nb. Secondly, active dissolution of Zr-Nb was performed in the range of -0.9 to -0.6 V. The dissolution mechanism can be explained by percolation theory, which is consistent with the observed microstructure of the alloy. Thirdly, passivation dissolution of Zr, Nb, and Zr-Nb alloy was investigated to identify the pure passivation products and additional products in the Zr-Nb alloy case. K2ZrCl6 and K3NbCl6 were identified as the pure passivation products of the major elements. In the Zr-Nb alloy case, additional products, such as Nb and NbZr, produced by the redox reaction of nanoparticles in the high viscous salt layer near the anode, were also confirmed. The anodic dissolution mechanism of Zr-Nb alloy can be summarized as follows. During active dissolution, only Zr metal dissolves into molten salts by percolation. Above the solubility near the anode, passivation products begin to form. The anode potential increases due to the disturbance of passivation products on ion flow, leading to co-dissolution of Nb. When the concentration of Nb ion exceeds the solubility, a passivation product of Nb also forms. In this scenario, a high viscous salt layer is formed, which traps nanoparticles of Zr metal, resulting in redox behavior between Zr metal and Nb ion. Some nanoparticles of Zr and Nb metal are also present in the form of NbZr.

Zr-Sn-Nb 합금의 재결정에 미치는 Nb과 Sn의 첨가영향을 연구하기 위해 냉간압연한 시편을 300˚C~750˚C의 온도구간에서 열처리한 후에 미소경도와 TEP (Thermoelectric Power)를 측정하여 재결정 거동을 조사하였으며 광학현미경, 주사전자 현미경 (SEM), 투과전자현미경 (TEM)으로 미세조직을 관찰하였다 미소경도 및 미세조직의 분석 결과에 따르면, Nb과 Sn의 첨가에 의해 재결정 활성화 에너지가 증가하여 재결정이 지연되었으며, 재결정 완료 이후의 결정립 성장도 억제되었음을 관찰하였다. Zr내의 고용도가 매우 낮은 Nb의 첨가는 석출물을 쉽게 형성하는 반면에 고용도가 비교적 큰 Sn은 기지상 내에 대부분 고용되어 석출물의 양이 매우 작았으나, Sn 첨가에 의한 재결정의 지연 효과가 더욱 컸다. Nb보다 Sn의 첨가가 Zr 합금의 재결정 거동을 효과적으로 지연시킨 것은 고용도가 높은 SR에 의한 치환형 고용체 형성과정에서 발생된 응력장이 전위의 이동을 효과적으로 억제했기 때문으로 생각된다. 한편, 회복과 재결정이 진행됨에 따라 전자 산란인자의 감소로 TEP는 증가하였으며, 재결정이 완료되면 TEP의 포화가 발생하였다. 석출물의 형성은 석출물 주변의 용질농도 감소로 인한 전자 산란인자의 감소에 기인하여 TEP의 증가를 가져왔다