부산대학교 도서관

Injection molding is widely used to produce plastic components with large lot size. However, quality failures occur during molding cycles. These can be minimized through real-time process monitoring. This article reports on a cloud-based digital twin (DT) that is supported by A-based control of process parameters and can be used to help companies detect product failures in real time. Process parameters and their interrelationship with quality failure were studied and used to generate models for real-time prediction of part quality. Two injection manufacturing lines in industry were chosen for data acquisition, implementation, and validation of the DT. While the DT successfully predicted faulty products in real time, adoption of traditional cloud-centric Internet of Things (IoT) approaches poses unforeseen practical challenges, such as the risk of losing data due to network issues and the prohibitive cost of regularly transferring a large amount data to cloud services.