An RMLU-DLNN and BF-GAO based heat transfer assessment of conformal cooling channel in injection molding process

Abstract

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 an RMLU-DLNN-based material prediction with optimization of derived variables using the BF-GAO approach. Initially, the features are extracted from the raw materials and then the material type is predicted by using RMLU-DLNN. Conversely, the various machine properties are clustered utilizing the BDDF-FFC methodology. Subsequently, parameters are derived from both the predicted material and the clustered machine property. The pressure of the machine is controlled by a QCO-PID controller. Then, the optimal parameters are selected from the BF-GAO 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.