This study employed a cross-rolling process to fabricate oxide dispersion strengthened (ODS) steel plates and investigated their microstructures and mechanical properties. The 9Cr-1W ODS ferritic steel was fabricated using mechanical alloying and hot isostatic pressing. The hot cross-rolling process produced thick ODS ferritic steel plates with a well-extended rectangular shape. The working direction greatly affected the grain structure and crystal texture of the ODS ferritic steel. Cross-rolled plates showed fine micro-grains with random crystal orientation, while unidirectionally rolled plates exhibited a strong orientation with larger, elongated grains. Transmission electron microscopy revealed a uniform distribution of nano-oxide particles in both rolling methods, with no major differences. Tensile tests of the ODS ferritic steel plates showed that the unidirectional rolled plates had anisotropic elongation, while cross-rolled plates exhibited isotropic behavior with uniform elongation. Cross-rolling produced finer, more uniform grains, reducing anisotropy and improving mechanical properties, making it ideal for manufacturing wide ODS steel components.

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

This study suggests a machine learning model for predicting the production quality of free-machining 303-series stainless steel small rolling wire rods according to the manufacturing process's operation condition. The operation condition involves 37 features such as sulfur, manganese, carbon content, rolling time, and rolling temperature. The study procedure includes data preprocessing (integration and refinement), exploratory data analysis, feature selection, machine learning modeling. In the preprocessing stage, missing values and outlier are removed, and variables for the interaction between processes and quality influencing factors identified in existing studies are added. Features are selected by variable importance index of lasso regression, extreme gradient boosting (XGBoost), and random forest models. Finally, logistic regression, support vector machine, random forest, and XGBoost is developed as a classifier to predict good or defective products with new operating condition. The hyper-parameters for each model are optimized using k-fold cross validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963 and logarithmic loss of 0.0209. In this study, the quality prediction model is expected to be able to efficiently perform quality management by predicting the production quality of small rolling wire rods in advance.

Hot rolling of Mg-6Zn-0.6Zr-0.4Ag-0.2Ca-(0, 8 wt%)Li powder was conducted at the temperature of 300 oC by putting the powder into the Cu pipe. The microstructure and mechanical properties of the samples were observed. Mg-6Zn- 0.6Zr-0.4Ag-0.2Ca without Li element was consisted of α phase and precipitates. The microstructure of the 8 wt%Li containing alloy consisted of two phases (α-Mg phase and β-Li phase). In addition, Mg2Zn3Li was formed in 8%Li added Mg-6Zn-0.6Zr- 0.4Ag-0.2Ca alloy. By addition of the Li element, the non-basal planes were expanded to the rolling direction, which was different from the based Mg alloy without Li. The tensile strength was gradually decreased from 357.1 MPa to 264 MPa with increasing Li addition from 0% to 8%Li. However, the elongation of the alloys was remarkably increased from 10 % to 21% by addition of the Li element to 8%. It is clearly considered that the non-basal texture and β phase contribute to the increase of elongation and formability.

본 연구는 압연공정을 이용하여 금속망 만을 사용하여 금속막을 제조하는 과정과 금속망과 금속분말을 사용하여 금속막을 제조하는 과정에 대해 연구했다. 금속망 압연과정에서 선택된 금속망은 각각 10%, 20% 그리고 30%의 감소율로 압연했다. 이 압연공정은 망 wire의 지름을 변화시키거나 망의 단면적의 감소를 통해 망의 공경 크기를 감소시킨다. 압연된 금속망의 여과율은 압연시키지 않은 금속망의 여과율과 거의 동일한 여과율을 보였으며 금속막의 공경크기 분포 또한 더 균일했다. 금속망 위에 금속분말 층을 제조하는데 있어 분말 접합제로서 PVA를 사용하였으며 1시간 동안 100℃에서 금속분말 층을 건조시키고 진공에서 3시간 동안 1000℃에서 소결시키는 방법이 높은 공경 밀도와 균열이 없는 금속망 위에 금속분말 층을 형성하는 최적 조건이었다. 소결 전 30%감소율을 가지는 금속망에 대해 금속분말 층 형성에 압연공정을 적용할 경우 여과율이 약 0.7 μm인 금속막이 성공적으로 제조되었다.

Two grades of Permalloy strips, Fe-45Ni(PB) and Fe-78Ni-4Mo-5Cu(PC) were fabricated by powder rolling process from elemental powder mixtures. The roll compacted green strips were sintered, homogenized, cold rolled with or without an intermediate annealing and finally heat treated to measure magnetic properties. For a given thickness reduction, rolling with an intermediate annealing was found more effective to achieve a full densification with no visible micropores and also to obtain better magnetic properties. Increasing the final rolling reduction also produced a marked improvement of the magnetic properties whereas the cooling rate during the final heat treatment has little effect in both grades. Addition of a small amount, 0.4% Mn slightly degraded the properties. As an overall, The PM strips produced via powder rolling yielded the similar soft magnetic properties to the corresponding commercial grades produced via wrought processing.

Electrolyte matrix fabrication process can be classifed as hot pressing, tape casting, callendering, electrophoretic deposition. however, these have limits in practice. Hot pressing is cumbersome method, because of careful heating and cooling. Furthermore, the perfected tile is so fragile that it is difficult to fit in a cell. Therefore this method is not adequate for mass production of the electrolyte matrix. Using electrophoretic deposition method, a very thin matrix can be made, but many attempts of the electrolyte embeding were found to be failure. Tape casting and callendering methods are employed in most of the matrix fabrication for the present. But these methods require lots of water as a solvent, so that coating of the LiAlO sub(2) with electrolyte is difficult. Recently, hot roll milling method has been developed and the perfected matrix was proved to be free from crack. The method, however, needs a roller to make a matrix and a perfected matrix is carefully striped off from the cooled roller. Therefore, this method requires a long time due to the cooling process. The author proposes a cold rolling process. On this method, heated slurry of the LiAlO sub(2) mixed with binder, is rolled with a cold roller. The heated slurry dose not adhere to the roller, since contacted hot slurry is rapidly solidified. Therefore fabrication speed is increased, without getting rid of merits of the hot rolling process.

In this study, for thermal neutron absorption, an aluminum metal composite in which B4C particles were uniformly dispersed was prepared using stirring casting and hot rolling processes. The microstructure, thermal neutron absorption rate, mechanical properties and dispersibility of the reinforcement of the prepared B4C/Al composite were analyzed. The composite in which the 40 μm sized B4C particles were uniformly dispersed increased the tensile strength as the volume ratio of the reinforcement increased.