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 further analyzed the influence of hyperparameters on testing accuracy, explored the corresponding optimal learning rate, and provided 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 were used to optimize the artificial neural network model. Through optimization of these training model hyperparameters, the width testing accuracy of the injection product improved. The experimental results indicated 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 was 0.1, and the testing accuracy reached 95.8%. Although momentum had the least influence on accuracy, it affected the convergence speed of the Sigmoid function, which reduced the number of required learning iterations (82.4% reduction rate). Optimizing hyperparameter settings can improve the accuracy of model testing and markedly reduce training time.

Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process

“…Multilayer perceptron (MLP) is a widely used ANN with multilayer feedforward structure and has nonlinear system modeling capability . The MLP contains at least one hidden layer, which is fully connected to each other . MLP uses activation functions to find mathematical relationships between inputs and outputs.…”

“… 41 The MLP contains at least one hidden layer, which is fully connected to each other. 42 MLP uses activation functions to find mathematical relationships between inputs and outputs. In the data set of nonlinear relationship, nonlinear activation functions such as logistic function (Sigmoid) and hyperbolic tangent function (tanh) are often used.…”

Optimization of Multilayer Perceptron Hyperparameter in Classifying Pneumonia Disease Through X-Ray Images with Speeded-Up Robust Features Extraction Method

“…Ke [37] in his report 2021 discusses enhancement of multilayer perceptron model training accuracy through the optimization of hyperparameters: a case study of the quality prediction of injection-molded parts. In the experiment the learning rate used [0.1; 0.001; 0.0001; 0.0001; 0.00001] and the activation functions [Sigmoid, Tanh, ReLU, LeakyReLU, ELU] the most optimal accuracy results are at a learning rate of 0.1 both in the activation functions of Sigmoid, Tanh, ReLU, LeakyReLU, and ELU [37]. Kermany et al [39] research uses deep learning model inception to determine medical conditions.…”