Hyperparameter optimization strategy of multilayer perceptron model for injection molding quality prediction

The challenge of maintaining consistent quality in injection molding is critical, yet conducting a comprehensive inspection is both costly and time consuming. Leveraging artificial intelligence, this study proposed using machine learning-specifically multilayer perception (MLP) models-to predict the quality of injection-molded parts. The accuracy of this approach largely relies on hyperparameter tuning, a process that can be cumbersome and suboptimal if performed through trial and error. The Taguchi method has the advantages of robustness, efficiency, and simplicity, and is a widely used robust optimization tool. However, this method assumes a linear relationship between factors, which limits the processing of complex systems where interactions between factors are nonlinear. Furthermore, the Taguchi method is sensitive to initial assumptions about factors and their levels, and the results may not reflect the true behavior of the system. To address this, a two-stage design-of-experiments method was devised that systematically identifies the optimal hyperparameter settings, including the maximum number of epochs, learning rate, momentum, activation function, minimum batch size, and numbers of hidden layers and nodes. The method is executed in two stages: (1) an L12 (21 x 35) orthogonal array is used to identify the primary factors affecting model accuracy and (2) an L8 (23) full-factorial experiment is conducted discover the combinations that yield the highest performance. Two experimental case studies, integrated circuit (IC) tray width prediction and optical component weight prediction, were used to validate the proposed method. The results revealed that the best hyperparameter settings resulted in validation and test accuracy of 96.83% and 95.30%, respectively, for IC tray width prediction. The average root-mean-square errors are 0.019 and 0.022 in model validation and test, respectively, for optical component weight prediction, with short computational time. The proposed method demonstrates how the systematic optimization of hyperparameters for MLP model can enhance the efficiency and stability of model training and can be used to advance quality control in the field of injection molding.