Development of a Quality Prediction Algorithm for an Injection Molding Process Considering Cavity Sensor and Vibration Data

This research develops a neural-network algorithm for predicting the quality levels of injection molding products to handle quality regarding problems. The main objective of this research is to predict the quality grade for each product utilizing the vibration data of the machine, as well as collected temperature and pressure data of each cavity, while a product is being processed. Among diverse features that can represent quality of injection molding products, we especially focus on the exterior features that could be driven from vision images of the products. We firstly explain how the infrastructure is constructed for collecting the vibration data, cavity sensor data, and vision-image data. Then, for the vibration and cavity sensor data, statistical features that stand for specific patterns of each data utilized as independent variables are derived. Quality grades of each product are also distinguished by two indicators: flection of the product’s housing and alignment of pinholes which are derived from the vision images of products utilizing the Canny-edge algorithm. Finally, a neural-network-based quality prediction algorithm is developed, and the performance of the algorithm is evaluated.