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.

This study numerically investigates buckling behavior of press braked steel plates with a free edge. In order to improve structural stability during construction, the top flanges of press-braked U section girder are laterally braced by the installation of prefabricated half-deck. Thus, an unbraced length is taken as the longitudinal spacing of pockets on the half-deck, which are to make composite section. This study performed 3D finite element analyses to evaluate an equivalent effective width of cold-formed flange with a free edge. Through the parametric numerical analyses, the elastic buckling stresses of the cold-formed flanges with rounded corner in the cross-section were compared with those of general flat plates. Then, the equivalent effective width of the cold-formed (press-braked) flanges were numerically examined for some representative cases.

이 연구에서는 강교량과 같은 토목 구조물에서 유도파의(Guided waves)한 종류인 램파(Lamb wave)를 이용하여 실시간으로 균열손상을 감지할 수 있는 새로운 비파괴 검사방법을 제안한다. 기존의 유도파를 이용한 기술들은, 손상을 감지하기 위해 비손상 상태의 자료를 저장하고 이를 새로이 얻어진 결과와 비교하는 방법을 사용함으로써 잠재적인 손상을 진단해 왔다. 그러나, 공용중인 강구조물은 다양한 하중 뿐 아니라 상시로 변화하는 자연환경에 노출되어 있기 때문에 동일한 비손상 상태의 응답을 얻는 것이 매우 어려우며 이러한 방법을 적용할 경우 오보(false alarm)의 우려도 매우 높다고 할 수 있다. 따라서 이 연구에서는 보다 안정적인 손상감지기법을 개발하기 위해 기존에 얻어진 초기치를 이용하지 않으면서 실시간으로 손상 여부를 판단할 수 있는 방법을 제안하고자 한다. 이 연구에서 제안된 감지 기술은, 압전소자의 극성과 판파의 특성을 이용하는 것으로 얇은 판의 양면에 부착된 압전소자를 통하여 균열손상에 의한 신호를 선택적으로 감지해 내는 데에 그 목적이 있다. 균열이 발생한 판에서 진행하는 판파는 균열로 인한 판의 두께변화로 인해 모드 변화를 일으키게 되는데, 제안된 감지기법으로 이러한 모드 변화만을 선택적으로 추출할 수 있다. 다양한 수치해석과 실험을 통해 이 연구에서 제안된 손상감지기법의 효율성과 적용성을 입증한다.

Recently warm compaction techniques are focused on and commercialization of one high-density compaction process in the P/M industry. Another development is a new SEGLESS using a developed lubricant that reduces ejection force at room temperature compaction. It is possible to achieve high-density by reducing lubricant amount. In this paper we confirmed that green density was 7.35 g/cm3 at 686MPa of compaction pressure when the new SEGLESS was applied to relatively lower temperature warm compaction process, such as 80℃.

본 연구에서는 미국 뉴욕주 로체스터시 소재 강합성 데크 트러스 보도교를 대상으로 사용성을 평가하기 위한 상시진동 실험(ambient vibration test)을 수행하였고 이를 해석적 방법에 의한 결과와 비교하였다. 교량전체에 대한 상시진동실험은 수치모델 작성 시 도입되는 여러 가정들에 대한 타당성을 평가하는데 있어서 유용한 방법이며, 교량의 고유진동수나 모드형상과 같이 구조 동력학에서 중요한 구조적인 변수를 결정하는데 있어 중요한 역할을 한다. 본 연구에서는 교량의 수직방향 및 수평방향 진동 특성과 변위를 측정하기 위하여 실제 교량에서 보행자에 의해 발생하는 진동을 입력하중으로 사용하였다. 교량 구조물에 대한 모델링을 위하여 3차원 유한 요소법을 사용하여 해석을 수행하였으며, 이를 통하여 현장실험 결과와의 유효성을 입증하였다.