Anomaly Detection‐Based Framework for Online Profile Monitoring

Abstract

Due to significant developments in technology, manufacturing processes are being equipped with sensors that provide continuous monitoring of the input and output process parameters. Signals observed through such sensors provide crucial details about the quality of the manufactured items. While significant amount of work has been found in the literature that aims at monitoring products quality through acquired process signals, these studies assume enough frequency of defective products implying balanced models training data. In the case of data imbalance, such methods provide biased and misleading prediction results. This work presents an anomaly detection‐based framework for monitoring profile generating processes in the case of infrequent process defectives. The framework integrates three unsupervised anomaly detection algorithms: Isolation Forest (IF), Local Outlier Factor (LOF) and Density Based Scan (DBSCAN). The proposed framework is illustrated through two industrial case studies, a thread tapping process and a 3D printing process. For the tapping process, the DBSCAN model provided the best performance with AUC = 0.838, accuracy = 71.32%, and sensitivity = 92.0%. For the 3D printing process, IF algorithm achieved the best performance, with AUC = 0.862, accuracy = 75.19%, and sensitivity = 86.67%. Furthermore, an anomaly‐based control chart is introduced to enable continuous monitoring of profile generating processes and facilitate early detection of abnormal process behaviour. The results demonstrate the effectiveness of anomaly detection approaches for handling imbalanced process data and highlight their promising potential for intelligent quality control in modern manufacturing processes.