An advanced machine learning and meta-heuristic approach-based heat transfer assessment of conformal cooling channel in injection molding process

Injection molding is a widely utilized manufacturing process across various industries. The cooling time in injection molding is an important factor that affects the productivity and energy consumption of the process. The production efficiency is directly proportional to the cooling efficiency, yet optimizing this cooling process presents significant challenges. The fixed cooling parameters are not suitable for all types of material, thus increasing the molding deviation. To address these challenges, this research work proposed a Regularized with Mish Linear Unit-based Deep Learning Neural Network-based material prediction with optimization of derived variables using the Banana Function-centric Green Anaconda Optimization approach. Initially, the features are extracted from the raw materials, and then the material type is predicted by using Regularized with Mish Linear Unit-based Deep Learning Neural Network. Conversely, various machine properties are clustered utilizing the binomial distribution with damping factor-based farthest first clustering methodology. Subsequently, parameters are derived from both the predicted material and the clustered machine property. The pressure of the machine is controlled by a quasi-conservative with an optimized proportional integral derivative controller. Then, the optimal parameters are selected from the Banana Function-centric Green Anaconda Optimization algorithm. In the optimal parameter selection, the multi-objective is considered by minimization of temperature, cooling time, pressure drop, and power consumption. In experimental analysis, the performance of the proposed approach is analyzed with the existing approaches. The proposed approach attains 98.9% accuracy, which is higher than existing approaches.