Image-informed Multi-objective Optimization of Cartridge Heater Layout in Injection Molds Using CNN Features and Random Forest Surrogates

In thermoset injection molding, achieving uniform temperature distribution and rapid heating is essential to ensure product quality and process efficiency. This study presents a data-efficient optimization framework that combines simulation image-based feature extraction, and machine learning-based surrogate modeling. A total of 10 thermal simulations were conducted using Moldex3D, with heater depth and spacing conditions determined through Maximin Latin Hypercube Sampling. Thermal simulation images were captured for each configuration. Spatial features were extracted from these images using a pretrained ResNet34 convolutional neural network. Resulting feature vectors were averaged to form a compact thermal descriptor. These features, along with process parameters, were used to train Random Forest regression models for predicting average temperature and maximum–minimum temperature difference. To maximize heating performance and minimize thermal variation, a multi-objective optimization problem was formulated and solved using the NSGA-II evolutionary algorithm. The proposed method successfully identified Pareto-optimal heater layouts that improved both target metrics, outperforming conventional design of experiments-based strategies. This framework demonstrated the viability of integrating full-field thermal imagery with AI-driven surrogate modeling and evolutionary optimization, particularly in low-data scenarios. It provides a promising foundation for future work on intelligent process design and control in complex thermoset molding applications.