Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

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.

When the product is taken out after the injection process, the surface of the product and the mold are attached and to separate them, it is necessary to consider the frictional force between the mold surface and the product surface. Therefore, to reduce the frictional force, a subtraction gradient for the rib shape is generally applied, and a lapping process is performed to improve the surface roughness of the rib shape surface of the processed mold. Therefore, research is needed to improve the surface roughness when processing the rib. In this study, slotting processing was applied to improve surface roughness when processing ribs. Slotting processing is a processing method that removes material through the feed motion of the tool, and processing is possible regardless of the aspect ratio of the processing shape. A slotting tool was developed for rib machining and a comparative experiment with electric discharge machining was performed. Also after processing, the surface roughness and processing time were compared and analyzed, and the improved surface roughness and fast processing time characteristics of the slotting processing compared to electric discharge processing were confirmed.

This study investigates application cases of facility management system model for enhancing facility productivity of industry filed around medium and small facility processing companies and finds the inefficiency of the existing management model. Following items are researched to seek out methods and measures to maximize facility productivity through empirical analysis by exploring and establishing a new management model. First, the empirical analysis, it is found that the overall equipment efficiency index used for facility productivity management in the companies has a difficulty being used as the index for it in actual medium-small processing companies. Second, a new facility management system model applying standard cycle time is suggested among facility management index system to measure facility productivity. Third, the empirical analysis is used to verify that developed facility management system model is a useful method to manage the facility productivity by applying the model to actual medium-small processing companies. Finally, it is necessary to implement comparison analysis on whether actual productivity enhancement induces a distinctly different result by using a new facility management index system model to be inhibited in this study.

The optical fibers tend to have poor machinability because of its hardness and brittleness. In the previous study, we applied the electrochemical discharge machining to fabricate the tip of the optical fiber. We could machine the optical fiber using the electrochemical discharge machining however the machined optical fiber tip had rough surface. In this study, we use electrochemical discharge machining with rotation tool which of the rough-grinding and finishing-grinding process to obtain a smooth surface of the side firing fiber. As a result, we are able to machine the optical fiber tip with smooth surface effectively from the proposed fiber machining process and the emission from the side-firing fiber clearly demonstrated the directional emission as the emission beam was reflected at 80 ° relative to the fiber axis.