Enhancement of multilayer perceptron model training accuracy through the optimization of hyperparameters: a case study of the quality prediction of injection-molded parts

Abstract

Injection molding has been broadly used in the mass production of plastic parts and must meet the requirements of efficiency and quality consistency. Machine learning can effectively predict the quality of injection-molded part. However, the performance of machine learning models largely depends on the accuracy of the training. Hyperparameters such as activation functions, momentum, and learning rate are crucial to the accuracy and efficiency of model training. This research aims to analyze the influence of hyperparameters on testing accuracy, explore the corresponding optimal learning rate, and provide the optimal training model for predicting the quality of injection-molded parts. In this study, stochastic gradient descent (SGD) and stochastic gradient descent with momentum (SGDM) are used to optimize the artificial neural network model. Through optimization of these training model hyperparameters, the width testing accuracy of the injection product is improved. The experimental results indicate that in the absence of momentum effects, all five activation functions can achieve more than 90% of the training accuracy with a learning rate of 0.1. Moreover, when optimized with the SGD, the learning rate of the Sigmoid activation function is 0.1, and the testing accuracy reaches 95.8%. Although momentum has the least influence on accuracy, it affects the convergence speed of the Sigmoid function, which reduces the number of required learning iterations (82.4% reduction rate). In summary, optimizing hyperparameter settings can improve the accuracy of model testing and markedly reduce training time.