Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method
<p dir="ltr">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)...
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2024
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| _version_ | 1864513538581594112 |
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| author | M. Hedayati-Dezfooli (21393728) |
| author2 | Mehdi Moayyedian (14880358) Ali Dinc (21393731) Mostafa Abdrabboh (21393734) Ahmed Saber (20037717) A. M. Amer (21393737) |
| author2_role | author author author author author |
| author_facet | M. Hedayati-Dezfooli (21393728) Mehdi Moayyedian (14880358) Ali Dinc (21393731) Mostafa Abdrabboh (21393734) Ahmed Saber (20037717) A. M. Amer (21393737) |
| author_role | author |
| dc.creator.none.fl_str_mv | M. Hedayati-Dezfooli (21393728) Mehdi Moayyedian (14880358) Ali Dinc (21393731) Mostafa Abdrabboh (21393734) Ahmed Saber (20037717) A. M. Amer (21393737) |
| dc.date.none.fl_str_mv | 2024-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.28991/esj-2024-08-05-025 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Optimizing_Injection_Molding_for_Propellers_with_Soft_Computing_Fuzzy_Evaluation_and_Taguchi_Method/30226849 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Manufacturing engineering Materials engineering Mechanical engineering Information and computing sciences Machine learning Mathematical sciences Numerical and computational mathematics Injection Molding Shrinkage Sink Mark Soft Computing FAHP TOPSIS Taguchi |
| dc.title.none.fl_str_mv | Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Emerging Science Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.28991/esj-2024-08-05-025" target="_blank">https://dx.doi.org/10.28991/esj-2024-08-05-025</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c28b894609f7f6df66268d9bbcf624e3 |
| identifier_str_mv | 10.28991/esj-2024-08-05-025 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30226849 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi MethodM. Hedayati-Dezfooli (21393728)Mehdi Moayyedian (14880358)Ali Dinc (21393731)Mostafa Abdrabboh (21393734)Ahmed Saber (20037717)A. M. Amer (21393737)EngineeringManufacturing engineeringMaterials engineeringMechanical engineeringInformation and computing sciencesMachine learningMathematical sciencesNumerical and computational mathematicsInjection MoldingShrinkageSink MarkSoft ComputingFAHPTOPSISTaguchi<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Emerging Science Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.28991/esj-2024-08-05-025" target="_blank">https://dx.doi.org/10.28991/esj-2024-08-05-025</a></p>2024-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.28991/esj-2024-08-05-025https://figshare.com/articles/journal_contribution/Optimizing_Injection_Molding_for_Propellers_with_Soft_Computing_Fuzzy_Evaluation_and_Taguchi_Method/30226849CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302268492024-10-01T00:00:00Z |
| spellingShingle | Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method M. Hedayati-Dezfooli (21393728) Engineering Manufacturing engineering Materials engineering Mechanical engineering Information and computing sciences Machine learning Mathematical sciences Numerical and computational mathematics Injection Molding Shrinkage Sink Mark Soft Computing FAHP TOPSIS Taguchi |
| status_str | publishedVersion |
| title | Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method |
| title_full | Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method |
| title_fullStr | Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method |
| title_full_unstemmed | Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method |
| title_short | Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method |
| title_sort | Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method |
| topic | Engineering Manufacturing engineering Materials engineering Mechanical engineering Information and computing sciences Machine learning Mathematical sciences Numerical and computational mathematics Injection Molding Shrinkage Sink Mark Soft Computing FAHP TOPSIS Taguchi |