Tensile Test Optimization Using the Design of Experiment and Soft Computing
<p dir="ltr">The tensile testing of various materials to evaluate the influence of different machining parameters is a fundamental requirement in every industry. The objective of this study is to investigate the effects of temperature, the area of the contact point, and the operator...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2023
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513534742757376 |
|---|---|
| author | Mehdi Moayyedian (14880358) |
| author2 | Mohammad Reza Chalak Qazani (13893261) Vuk Cvorovic (22415623) Fahad Asi (22415626) Askhat Mussin (22415629) Mohsen Hedayati-Dezfooli (10852116) Ali Dinc (21393731) |
| author2_role | author author author author author author |
| author_facet | Mehdi Moayyedian (14880358) Mohammad Reza Chalak Qazani (13893261) Vuk Cvorovic (22415623) Fahad Asi (22415626) Askhat Mussin (22415629) Mohsen Hedayati-Dezfooli (10852116) Ali Dinc (21393731) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mehdi Moayyedian (14880358) Mohammad Reza Chalak Qazani (13893261) Vuk Cvorovic (22415623) Fahad Asi (22415626) Askhat Mussin (22415629) Mohsen Hedayati-Dezfooli (10852116) Ali Dinc (21393731) |
| dc.date.none.fl_str_mv | 2023-10-23T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/pr11113106 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Tensile_Test_Optimization_Using_the_Design_of_Experiment_and_Soft_Computing/30338908 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Manufacturing engineering Mechanical engineering Information and computing sciences Machine learning Mathematical sciences Numerical and computational mathematics Tensile test Taguchi method Strain and stress analysis Signal-to-noise ratio Fuzzy neural network Genetic algorithm |
| dc.title.none.fl_str_mv | Tensile Test Optimization Using the Design of Experiment and Soft Computing |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The tensile testing of various materials to evaluate the influence of different machining parameters is a fundamental requirement in every industry. The objective of this study is to investigate the effects of temperature, the area of the contact point, and the operator on the tensile test of brass samples. This study employs a hybrid soft computing approach, integrating an adaptive network-based fuzzy inference system (ANFIS), genetic algorithm (GA) optimization, and design of experiments (DOE). By combining these techniques, the study aims to leverage their individual strengths and achieve superior results. The results reveal that the area of the contact point exerts the most significant influence on the tensile test, followed by the operator and temperature. The optimal levels of these parameters are determined to be a level of two for the operator, a level of three for the area of the contact point, and a level of one for the temperature. The study demonstrated that the hybrid soft computing method outperformed the traditional DOE method, achieving a substantial improvement in elongation of 32.9%. The optimized combination of machining parameters led to a notable enhancement in the brass samples’ tensile properties, highlighting the effectiveness of the applied methodology. The marginal error of only 0.72% in the hybrid approach showcases its high precision and reliability in determining the optimal levels of machining parameters. These findings underscore the potential of the Taguchi optimization method, ANFIS, and GA in achieving superior results in the tensile testing of materials, particularly in cases where multiple parameters are involved. The research results provide valuable insights for industries relying on precise material characterization, offering a robust methodology for optimizing tensile testing procedures. The study’s success in leveraging a hybrid soft computing approach serves as a promising avenue for future research in the field of material testing and optimization techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: Processes<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/pr11113106" target="_blank">https://dx.doi.org/10.3390/pr11113106</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_466d4954d27c0a9fe4c0e30867974a55 |
| identifier_str_mv | 10.3390/pr11113106 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30338908 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Tensile Test Optimization Using the Design of Experiment and Soft ComputingMehdi Moayyedian (14880358)Mohammad Reza Chalak Qazani (13893261)Vuk Cvorovic (22415623)Fahad Asi (22415626)Askhat Mussin (22415629)Mohsen Hedayati-Dezfooli (10852116)Ali Dinc (21393731)EngineeringManufacturing engineeringMechanical engineeringInformation and computing sciencesMachine learningMathematical sciencesNumerical and computational mathematicsTensile testTaguchi methodStrain and stress analysisSignal-to-noise ratioFuzzy neural networkGenetic algorithm<p dir="ltr">The tensile testing of various materials to evaluate the influence of different machining parameters is a fundamental requirement in every industry. The objective of this study is to investigate the effects of temperature, the area of the contact point, and the operator on the tensile test of brass samples. This study employs a hybrid soft computing approach, integrating an adaptive network-based fuzzy inference system (ANFIS), genetic algorithm (GA) optimization, and design of experiments (DOE). By combining these techniques, the study aims to leverage their individual strengths and achieve superior results. The results reveal that the area of the contact point exerts the most significant influence on the tensile test, followed by the operator and temperature. The optimal levels of these parameters are determined to be a level of two for the operator, a level of three for the area of the contact point, and a level of one for the temperature. The study demonstrated that the hybrid soft computing method outperformed the traditional DOE method, achieving a substantial improvement in elongation of 32.9%. The optimized combination of machining parameters led to a notable enhancement in the brass samples’ tensile properties, highlighting the effectiveness of the applied methodology. The marginal error of only 0.72% in the hybrid approach showcases its high precision and reliability in determining the optimal levels of machining parameters. These findings underscore the potential of the Taguchi optimization method, ANFIS, and GA in achieving superior results in the tensile testing of materials, particularly in cases where multiple parameters are involved. The research results provide valuable insights for industries relying on precise material characterization, offering a robust methodology for optimizing tensile testing procedures. The study’s success in leveraging a hybrid soft computing approach serves as a promising avenue for future research in the field of material testing and optimization techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: Processes<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/pr11113106" target="_blank">https://dx.doi.org/10.3390/pr11113106</a></p>2023-10-23T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/pr11113106https://figshare.com/articles/journal_contribution/Tensile_Test_Optimization_Using_the_Design_of_Experiment_and_Soft_Computing/30338908CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303389082023-10-23T03:00:00Z |
| spellingShingle | Tensile Test Optimization Using the Design of Experiment and Soft Computing Mehdi Moayyedian (14880358) Engineering Manufacturing engineering Mechanical engineering Information and computing sciences Machine learning Mathematical sciences Numerical and computational mathematics Tensile test Taguchi method Strain and stress analysis Signal-to-noise ratio Fuzzy neural network Genetic algorithm |
| status_str | publishedVersion |
| title | Tensile Test Optimization Using the Design of Experiment and Soft Computing |
| title_full | Tensile Test Optimization Using the Design of Experiment and Soft Computing |
| title_fullStr | Tensile Test Optimization Using the Design of Experiment and Soft Computing |
| title_full_unstemmed | Tensile Test Optimization Using the Design of Experiment and Soft Computing |
| title_short | Tensile Test Optimization Using the Design of Experiment and Soft Computing |
| title_sort | Tensile Test Optimization Using the Design of Experiment and Soft Computing |
| topic | Engineering Manufacturing engineering Mechanical engineering Information and computing sciences Machine learning Mathematical sciences Numerical and computational mathematics Tensile test Taguchi method Strain and stress analysis Signal-to-noise ratio Fuzzy neural network Genetic algorithm |