Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization
<p dir="ltr">This research focuses on utilizing injection moulding to assess defects in plastic products, including sink marks, shrinkage, and warpages. Process parameters, such as pure cooling time, mould temperature, melt temperature, and pressure holding time, are carefully select...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513545990832128 |
|---|---|
| author | Mehdi Moayyedian (14880358) |
| author2 | Mohammad Reza Chalak Qazani (13893261) Parisa Jourabchi Amirkhizi (19324981) Houshyar Asadi (13305666) Mohsen Hedayati-Dezfooli (10852116) |
| author2_role | author author author author |
| author_facet | Mehdi Moayyedian (14880358) Mohammad Reza Chalak Qazani (13893261) Parisa Jourabchi Amirkhizi (19324981) Houshyar Asadi (13305666) Mohsen Hedayati-Dezfooli (10852116) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mehdi Moayyedian (14880358) Mohammad Reza Chalak Qazani (13893261) Parisa Jourabchi Amirkhizi (19324981) Houshyar Asadi (13305666) Mohsen Hedayati-Dezfooli (10852116) |
| dc.date.none.fl_str_mv | 2024-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-024-62618-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multiple_objectives_optimization_of_injection-moulding_process_for_dashboard_using_soft_computing_and_particle_swarm_optimization/29108939 |
| 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 Artificial intelligence Machine learning Injection moulding Warpage/shrinkage/sink mark Sof computing Multiple objectives particle swarm optimisation Pareto front |
| dc.title.none.fl_str_mv | Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This research focuses on utilizing injection moulding to assess defects in plastic products, including sink marks, shrinkage, and warpages. Process parameters, such as pure cooling time, mould temperature, melt temperature, and pressure holding time, are carefully selected for investigation. A full factorial design of experiments is employed to identify optimal settings. These parameters significantly affect the physical and mechanical properties of the final product. Soft computing methods, such as finite element (FE), help mitigate behaviour by considering different input parameters. A CAD model of a dashboard component integrates into an FE simulation to quantify shrinkage, warpage, and sink marks. Four chosen parameters of the injection moulding machine undergo comprehensive experimental design. Decision tree, multilayer perceptron, long short-term memory, and gated recurrent units models are explored for injection moulding process modelling. The best model estimates defects. Multiple objectives particle swarm optimisation extracts optimal process parameters. The proposed method is implemented in MATLAB, providing 18 optimal solutions based on the extracted Pareto-Front.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<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/10.1038/s41598-024-62618-7" target="_blank">https://dx.doi.org/10.1038/s41598-024-62618-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_017f9307c0335ccf55bc0ce24c836f00 |
| identifier_str_mv | 10.1038/s41598-024-62618-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29108939 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimizationMehdi Moayyedian (14880358)Mohammad Reza Chalak Qazani (13893261)Parisa Jourabchi Amirkhizi (19324981)Houshyar Asadi (13305666)Mohsen Hedayati-Dezfooli (10852116)EngineeringManufacturing engineeringMaterials engineeringMechanical engineeringInformation and computing sciencesArtificial intelligenceMachine learningInjection mouldingWarpage/shrinkage/sink markSof computingMultiple objectives particle swarm optimisationPareto front<p dir="ltr">This research focuses on utilizing injection moulding to assess defects in plastic products, including sink marks, shrinkage, and warpages. Process parameters, such as pure cooling time, mould temperature, melt temperature, and pressure holding time, are carefully selected for investigation. A full factorial design of experiments is employed to identify optimal settings. These parameters significantly affect the physical and mechanical properties of the final product. Soft computing methods, such as finite element (FE), help mitigate behaviour by considering different input parameters. A CAD model of a dashboard component integrates into an FE simulation to quantify shrinkage, warpage, and sink marks. Four chosen parameters of the injection moulding machine undergo comprehensive experimental design. Decision tree, multilayer perceptron, long short-term memory, and gated recurrent units models are explored for injection moulding process modelling. The best model estimates defects. Multiple objectives particle swarm optimisation extracts optimal process parameters. The proposed method is implemented in MATLAB, providing 18 optimal solutions based on the extracted Pareto-Front.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<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/10.1038/s41598-024-62618-7" target="_blank">https://dx.doi.org/10.1038/s41598-024-62618-7</a></p>2024-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-024-62618-7https://figshare.com/articles/journal_contribution/Multiple_objectives_optimization_of_injection-moulding_process_for_dashboard_using_soft_computing_and_particle_swarm_optimization/29108939CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291089392024-10-01T00:00:00Z |
| spellingShingle | Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization Mehdi Moayyedian (14880358) Engineering Manufacturing engineering Materials engineering Mechanical engineering Information and computing sciences Artificial intelligence Machine learning Injection moulding Warpage/shrinkage/sink mark Sof computing Multiple objectives particle swarm optimisation Pareto front |
| status_str | publishedVersion |
| title | Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization |
| title_full | Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization |
| title_fullStr | Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization |
| title_full_unstemmed | Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization |
| title_short | Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization |
| title_sort | Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization |
| topic | Engineering Manufacturing engineering Materials engineering Mechanical engineering Information and computing sciences Artificial intelligence Machine learning Injection moulding Warpage/shrinkage/sink mark Sof computing Multiple objectives particle swarm optimisation Pareto front |