Plastic injection molding is widely used in the automotive, electronics, and other manufacturing industries. Since product quality is significantly affected by process variables such as temperature, pressure, speed, and cycle time, datadriven quality management has become increasingly important. However, conventional quality control methods mainly depend on operator experience and post-process inspection, making it difficult to detect defects and abnormal process conditions in real time. In addition, manufacturing datasets often suffer from severe class imbalance because defective samples are much fewer than normal samples. This study proposes an AI-based defect prediction and anomaly detection system using plastic injection molding process data. For defect prediction, XGBoost was selected as the supervised learning model, and SMOTE was applied to address class imbalance. Recall, F1-score, and PR-AUC were used as the main evaluation metrics instead of accuracy. SHAP analysis was also applied to identify important process variables affecting defect occurrence. For anomaly detection, Isolation Forest and AutoEncoder were used together. Isolation Forest was adopted for fast first-stage detection, while AutoEncoder was used to detect complex nonlinear abnormal patterns. The results indicate that the proposed system can support both defect prediction and process anomaly detection. In the supervised learning results, XGBoost showed the best performance on the CN7 dataset, achieving a recall of 1.000 and an F1-score of 0.800. However, all supervised models showed limited defect detection performance on the RG3 dataset, indicating the effect of severe class imbalance and dataset-specific characteristics. In anomaly detection, AutoEncoder achieved the highest F1-score among the compared models, while Isolation Forest demonstrated practical advantages for real-time application due to its computational efficiency. Therefore, the proposed AI-based system can contribute to smart quality management and data-driven decision-making in plastic injection molding processes.

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

Virtual metrology(VM) is a promising technology which can convert off-line sampling inspection into on-line total inspection at the manufacturing process. This paper provides an economic evaluation model of VM system which predicts the defects of a target process with Bernoulli sampling inspection. For this purpose, we build M/G/1 queueing models of two systems. One is VM non-applied system and the other is VM applied one. We derive total costs per unit time of each system and conduct sensitivity analysis according to variations of input parameters such as defect rates, various process costs and VM prediction error rates. The proposed analysis model is expected to be used for evaluating economic values of VM system implementation projects.

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manuf

제조 기업들은 공정 내에 불량을 파악하고 품질 특성치를 찾아내기 위해서 대용량의 샘플 데이터를 수집하며 분석하고 있다. 이렇게 수집되어진 데이터를 분석하기 위하여 데이터마이닝 기법이 많이 이용되어지고 있다. 본 연구에서는 제조 공정내의 불량 요인의 데이터를 수집하고 수집된 데이터를 데이터마이닝 기법 중 연관규칙을 이용하여 공정 내 불량간의 연관관계를 파악하고 공정 불량요인을 효과적으로 분석함으로서 제조 공정 내에 불량항목과 공정 간의 변화패턴 관계를 알아보기 위함이다.

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing process. The purpose of this paper is to model the recognition of defect type patterns and prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.