# Prediction of Short-Shot Defects in Injection Molding by Transfer Learning
## 1 Introduction  
In recent years, with the development of Industry 4.0, many traditional industries have been transitioning towards smart manufacturing. In the injection molding industry, the challenge of shortening product cycles has emerged. Reducing the number of trial runs can significantly enhance product development speed and reduce material costs incurred during testing. Many companies have adopted Computer-Aided Engineering (CAE) to eliminate most factors that could lead to product defects. However, CAE cannot entirely eliminate potential issues, especially the variability in actual parameters during each molding process. Therefore, when problems arise with injection parameters, reliance on the experience of on-site personnel for parameter adjustments is still necessary, and CAEcannot be used for real-time decision-making in the field. Effectively predicting product quality using informative data is a significant topic in today's era of smart manufacturing. In the relevant literature on injection molding, [PERSON] et al. [1] mentioned that process parameters affect product quality, cycle time, and production costs in the manufacturing process. Therefore, finding optimized process parameters is a crucial process. [PERSON] et al. [2] emphasized the importance of optimizing injection parameters for product quality, and mentioned that iteratively changing processing parameters to improve product defects consumes a significant amount of time, material, and manpower. They proposed using the Taguchi Methods to identify injection parameters that cause shrinkage defects in products. The results show that this technique effectively reduces shrinkage defects in products under different injection parameters. [PERSON] et al. [3] discussed the parameters that cause warping in transmission parts of Flapping-Wing Micro-Aerial Vehicles (FW-MAV), including injection temperature, mold temperature, injection pressure, and holding time. They used the Taguchi Methods to find the optimal process parameters and validated the results through CAE system analysis and simulation. The findings demonstrate that these optimized process parameters minimized warping in the products. [PERSON] et al. [4] highlighted how CAE systems can optimize mold structures and modify problematic injection parameters before production, thus avoiding the cost of repeated testing in actual production. This technology not only shortens mold development cycles, but also reduces unnecessary human and material resources, lowering production costs and enhancing product quality. [PERSON] et al. [5] discussed how residual stresses can impact the presence of defects in final products. They proposed using the Taguchi Methods to identify the process parameters that have the most significant effect on shear force in PC/ABS mixtures. Through CAE simulations and verification, the Taguchi Methods were able to identify optimized process parameters that resulted in PC/ABS products with good shear stress characteristics and no defects, effectively enhancing the quality of injection-molded products. In the machine learning literature, [PERSON] et al. [6] used CAE simulation results as training data to train a neural network model for predicting the porosity generation ratio and volume deformation of plastic optical lenses based on the injection molding process parameters. They demonstrated the feasibility of this method through actual experiments. [PERSON] et al. [7] mentioned that injection molding process parameters have a significant impact on product quality. They proposed a model architecture that combines artificial neural networks with genetic algorithms. This approach utilizes backpropagation neural networks to handle the nonlinear relationship between process parameters and product quality. Additionally, genetic algorithms are employed for optimizing the model's process parameters. The results show that the combination of backpropagation neural networks and genetic algorithms effectively improved the product's volume shrinkage. [PERSON] et al.