Abstract: Machine learning algorithms can handle high-dimensional and multivariate data and extract hidden relationships in data in complex and dynamic environments, which has a good application prospect in injection molding part size prediction. The performance of injection molding part size prediction system depends on the choice of machine learning algorithm, however, the traditional machine learning algorithm can not achieve good prediction effect in practical application. In this paper, a fusion model based on Stacking integrated learning method is proposed, and the optimal performance model is obtained by comparing different Stacking learner combinations and combining multiple types of learners. The performance of the model in injection molding parts size prediction is greatly improved compared with the traditional model, and the model prediction results can be explained back to the actual production according to the characteristics, providing decision guidance for the optimization of manufacturing processes and processes.

Key words: Injection part size prediction, machine learning, model fusion, Stacking integrated learning.