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All Journal Emerging Science Journal
Moayyedian, Mehdi
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Environmental Science

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Journal : Emerging Science Journal

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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 Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-025

Abstract

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
Multi-Objective Optimization of Injection Molding Using Taguchi, Fuzzy Methods, and GA Moayyedian, Mehdi; Chalak Qazani, Mohammad Reza; Hedayati-Dezfooli, M.; Mussin, Askhat; Bissekenova, Zhanel
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-028

Abstract

The objective of this research is to optimize the injection molding process of an automotive window regulator bracket by improving the moldability index while minimizing key defects. To achieve this, a multi-objective framework is developed that combines the Taguchi method with Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy-TOPSIS. Five critical processing parameters—melt temperature, mold temperature, filling time, holding pressure time, and cooling time—were investigated, with polypropylene as the base material. A Taguchi L25 orthogonal array was employed to reduce the number of experimental trials from 3,125 to just 25, thereby saving resources while maintaining reliability. The evaluation considered warpage, residual stress, and shear stress, which are the most influential defects affecting part performance. Finite Element Analysis (FEA) was incorporated to validate the accuracy of the results, while a hybrid ANFIS-GA predictive model was applied to forecast the moldability index, demonstrating an improvement of about 1% over conventional optimization methods. The optimized settings resulted in minimized warpage (1.8122 mm), residual stress (43.03 MPa), and shear stress (0.08 MPa). The novelty of this work lies in integrating Taguchi with FAHP and Fuzzy-TOPSIS for a single-objective transformation, offering a systematic and efficient approach for multi-objective optimization in injection molding applications.
Co-Authors Abdrabboh, Mostafa Amer, A. M. Bissekenova, Zhanel Chalak Qazani, Mohammad Reza Dinc, Ali Hedayati-Dezfooli, M. Mussin, Askhat Saber, Ahmed