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    The hybrid approach of genetic algorithm and particle swarm optimization on reduced weld line defect in plastic injection molding
    (Sage Publications Ltd, 2024) Oktem, Hasan; Uygur, Ilyas; Sari, Ece Simooglu; Shinde, Dinesh
    Weld lines are a serious defect observed in plastic injection molded parts, impacting both their cosmetic appearance and mechanical properties. Controlling the conditions of plastic injection is crucial to mitigate these weld lines. This study introduces a novel approach to identify polypropylene injection molding (PIM) conditions aimed at reducing weld lines in polypropylene parts. The PIM conditions considered in this study include melt temperature, injection pressure, packing pressure, packing time, and cooling time. An orthogonal array Taguchi L27 design was employed for the experimental setup, producing 27 polypropylene parts with varying combinations of process conditions. The width of weld lines generated on the parts' surfaces was measured using an optimum microscope for all trials. Parametric analysis was conducted using response surface plots and contour plots to estimate the process conditions yielding minimum weld lines. Analysis of variance and regression analysis were employed to interpret the experimental data, with the resulting regression equation used to predict weld lines for a set of PIM process conditions. Finally, two efficient optimization algorithms, genetic algorithm (GA), and particle swarm optimization (PSO), were implemented using MATLAB programming to estimate the optimum process conditions for minimizing weld lines. The GA and PSO predicted weld line widths of 6.12302 mu m and 6.123 mu m, respectively, representing an 18.51% improvement in results. These findings demonstrate that the novel approach presented in this study can be effectively and reliably applied to address plastic product defects in the industry.
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    A nature-inspired optimal design for a ventilated brake disc
    (Sage Publications Ltd, 2022) Shinde, Dinesh; Öktem, Hasan; Uygur, İlyas
    Brake discs are critical automobile components that provide the braking effect and ultimately ensure the safety of passengers. Because of intermittent braking operations, brake discs are subjected to fluctuating loads and are provided with ventilation to facilitate cooling. The present study utilized a combination of response surface methodology parametric analysis, finite element analysis, statistical analysis, predictive modeling, and design optimization. Based on the response surface methodology design of experimental runs, ventilated brake discs were modeled considering the critical design parameters, viz., radius of center hole, thickness of inboard and outboard plate, height of vanes, and offset. Using finite element analysis, the brake discs were simulated under actual braking conditions for fatigue life cycles. Thirty-two (32) trial finite element analysis trials were performed. The obtained results were interpreted using ANOVA and an effective predictive model was established. Parametric analysis was performed using plots of response surface methodology and contours to predict the optimal parameter settings. In addition, two nature-inspired optimization techniques, the genetic algorithm and particle swarm optimization, were implemented and the optimal design parameter settings were determined for maximum fatigue life. The genetic algorithm and particle swarm optimization produced 7.67% better results than the parametric analysis, clearly demonstrating that the proposed design techniques exhibited significant performance improvement compared to widely used classical techniques.