Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method - Hedayati-Dezfooli, M.; Moayyedian, Mehdi; Dinc, Ali; Abdrabboh, Mostafa; Saber, Ahmed; Amer, A. M. (Emerging Science Journal, 2024)
This research explores multi-objective optimization in injection molding with a focus on identifying the optimal configuration for the moldability index in aviation propeller manufacturing. The study employs the Taguchi method and fuzzy analytic hierarchy process (FAHP) combined with the Technique for the Order Performance by Similarity to the Ideal Solution (TOPSIS) to systematically evaluate diverse objectives. The investigation specifically addresses two prevalent defects—shrinkage rate and sink mark—that impact the final quality of injection-molded components. Polypropylene is chosen as the injection material, and critical process parameters encompass melt temperature, mold temperature, filling time, cooling time, and pressure holding time. The Taguchi L25 orthogonal array is selected, considering the number of levels and parameters, and Finite Element Analysis (FEA) is applied to enhance precision in results. To validate both simulation outcomes and the proposed optimization methodology, Artificial Neural Network (ANN) analysis is conducted for the chosen component. The Fuzzy-TOPSIS method, in conjunction with ANN, is employed to ascertain the optimal levels of the selected parameters. The margin of error between the chosen optimization methods is found to be less than one percent, underscoring their suitability for injection molding optimization. The efficacy of the selected optimization method has been corroborated in prior research. Ultimately, employing the fuzzy-TOPSIS optimization method yields a minimum shrinkage value of 16.34% and a sink mark value of 0.0516 mm. Similarly, utilizing the ANN optimization method results in minimum values of 16.42% for shrinkage and 0.0519 mm for the sink mark. Doi: 10.28991/ESJ-2024-08-05-025 Full Text: PDF