@article{Mehta_Padhi_2023, title={Energy Monitoring of Process Variables and Optimization of Nozzle Section in Sustainable Plastic Injection Molding}, volume={11}, url={https://ijisae.org/index.php/IJISAE/article/view/2837}, abstractNote={<p>With respect to the improvement of energy monitoring and optimized nozzle section in Sustainable Plastic Injection Molding (EMONS-PIM) to achieve the Cost-Effective end product for the industries. During the process of molding of Polymethyl Methacrylate (PMMA) the effects of parameters in terms of energy consumption needs to get determined. Energy efficiency is a severe issue because of the increasing energy costs as well as the correlated environmental impact. The major parameters which are concentrated in terms of energy are melting and molding temperature, holding and cooling time, screw rotational speed and nozzle temperature. The highest impact on energy consumption is produced by cooling time and nozzle temperature. Simulation is done among the process parameters and the equivalent energy dissipation is recorded at each instant of time. Several hydraulic injection molding machines are considered for energy monitoring process and found out the energy saving opportunities. The optimization of nozzle section and energy monitoring is simulated using the software’s CATIA and MATLAB. In CATIA the effective nozzle section is performed and the relevant analysis is performed. In MATLAB the process of energy consumption reduction is concentrated and the parameters which are considered for the process of simulation analysis are sum rate, bit error rate, convergence plot and energy consumption. The materials which are considered in the PIM process are thermoplastic polystyrene, thermoplastic acrylonitrile butadiene styrene and thermoplastic polyvinyl chloride. To perform the process of comparative analysis the end results of the proposed EMONS-PIM method is compared with the earlier researched like AntLion Optimization, PSO-MSQPA and MLGS-PIM.</p>}, number={6s}, journal={International Journal of Intelligent Systems and Applications in Engineering}, author={Mehta, Ekta S. and Padhi, S. N.}, year={2023}, month={May}, pages={141–164} }