# Prediction of Short-Shot Defects in Injection Molding by Transfer Learning
## 1 Introduction
[8] proposed the use of the Taguchi Methods, Back Propagation Neural Network (BPNN), a hybrid Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to identify optimized process parameters. The results demonstrate that this approach not only enhances the quality of plastic products, but also effectively reduces process variations. [PERSON] also pointed out that the Back Propagation Neural Network can face challenges such as overfitting due to poor initial link values or excessive training iterations, making it difficult to find the best solution. Therefore, they used Genetic Algorithms to address this issue and increase prediction accuracy. [PERSON] et al. [9] mentioned that in order to prevent the delivering of defective products to customers and incurring unpredictable costs, they proposed the use of classification models based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results show that these models accurately classify product quality, whether it is related to short-shots, incomplete filling, burn marks, warping, or flash, into different categories. [PERSON] et al. [10] emphasized the significance of optimizing process parameters for improving product quality. Even when process parameters are adjusted to their optimum settings, variations in material properties and machine instability can still impact product quality. Using machine learning enables the effective monitoringof process parameters during product production and allows for rapid adjustments to improve product quality. [PERSON] et al. [11] discussed how to accurately bring the weight of injection-molded products close to the ideal weight and reduce process time. Optimizing process parameters is one of the most crucial steps in achieving this. Therefore, they proposed a model architecture that combines artificial neural networks and genetic algorithms. The results demonstrate that this technique can accurately identify optimized process parameters, effectively reducing process time, and bringing the final product weight closer to the ideal weight. [PERSON] et al. [12] mentioned that mold temperature, melt temperature, holding pressure, holding time, and cooling time are important parameters affecting warpage deformation. They proposed a Back Propagation Neural Network architecture with two hidden layers, each containing 20 neurons, to predict warpage deformation based on these five parameters. The network was trained using finite element analysis data, and the results show that this system had a prediction error of within 2%, effectively optimizing process parameters and reducing warpage deformation. [PERSON] et al. [13] mentioned that identifying the optimal machine setting parameters in the injection molding process can be time-consuming and costly in terms of trial runs. Therefore, they proposed a regression model based on an artificial neural network for injection molding. The input parameters included injection weight, screw position, material properties, and plasticization time, while the output parameters were screw speed and back pressure. The results show that using unseen data for prediction yielded an average error of 0.27% with a standard deviation of 0.37%. This approach reduces the time and trial run costs associated with the process stage. [PERSON] et al. [14] proposed a model architecture that combines an Artificial Neural Network (ANN) with Genetic Algorithms (GA). This approach effectively reduced warping in optical lenses and utilized the Taguchi Methods to identify the optimal injection parameters. They found that the factors affecting product warping were mold temperature, cooling time, holding pressure, and holding time. The results show that this method improved the shape accuracy of the lenses by 13.36%. [PERSON] et al. [15] proposed an optimization approach for the injection molding process of automobile front frames, utilizing a regression equation, genetic algorithms, and Back Propagation Neural Networks.