A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites
<p>Machine learning approaches that integrate physical laws with data-driven models are transforming process optimization and quality assurance in polymer matrix composite manufacturing. This review synthesizes recent developments in neural metamodels for injection molding, spatio-temporal dig...
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2025
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| _version_ | 1852017338814038016 |
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| author | Ivan P. Malashin (22113739) |
| author2 | Dmitry Martysyuk (22113742) Vladimir Nelyub (22113745) Aleksei Borodulin (22113748) Andrei Gantimurov (22113751) Vadim Tynchenko (22113754) |
| author2_role | author author author author author |
| author_facet | Ivan P. Malashin (22113739) Dmitry Martysyuk (22113742) Vladimir Nelyub (22113745) Aleksei Borodulin (22113748) Andrei Gantimurov (22113751) Vadim Tynchenko (22113754) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ivan P. Malashin (22113739) Dmitry Martysyuk (22113742) Vladimir Nelyub (22113745) Aleksei Borodulin (22113748) Andrei Gantimurov (22113751) Vadim Tynchenko (22113754) |
| dc.date.none.fl_str_mv | 2025-08-23T19:20:08Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.29974102.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/A_review_of_physics-informed_and_data-driven_approaches_for_manufacturing_process_optimization_in_polymer_matrix_composites/29974102 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified Polymer matrix composites pressure molding vacuum infusion physics-informed machine learning |
| dc.title.none.fl_str_mv | A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Machine learning approaches that integrate physical laws with data-driven models are transforming process optimization and quality assurance in polymer matrix composite manufacturing. This review synthesizes recent developments in neural metamodels for injection molding, spatio-temporal digital twins for resin infusion, and symbolic-regression surrogates for vacuum networks. Article identifies remaining challenges—such as extension to semicrystalline systems, uncertainty quantification under real-world noise, and deployment on industrial platforms—and outline strategies for addressing them. Building on these insights, a unified physics-informed surrogate concept is proposed that leverages temporal encoders, recurrent propagation, and multi-output decoders with embedded conservation constraints. This model is designed for rapid prediction of part quality metrics, cure state, flow front progression, and temperature fields, and supports gradient-based inversion for closed-loop control in advanced composite processing.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_7ecbcb2c8b0efacdcb99a84d442b7d09 |
| identifier_str_mv | 10.6084/m9.figshare.29974102.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29974102 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix compositesIvan P. Malashin (22113739)Dmitry Martysyuk (22113742)Vladimir Nelyub (22113745)Aleksei Borodulin (22113748)Andrei Gantimurov (22113751)Vadim Tynchenko (22113754)MedicineSpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedPolymer matrix compositespressure moldingvacuum infusionphysics-informed machine learning<p>Machine learning approaches that integrate physical laws with data-driven models are transforming process optimization and quality assurance in polymer matrix composite manufacturing. This review synthesizes recent developments in neural metamodels for injection molding, spatio-temporal digital twins for resin infusion, and symbolic-regression surrogates for vacuum networks. Article identifies remaining challenges—such as extension to semicrystalline systems, uncertainty quantification under real-world noise, and deployment on industrial platforms—and outline strategies for addressing them. Building on these insights, a unified physics-informed surrogate concept is proposed that leverages temporal encoders, recurrent propagation, and multi-output decoders with embedded conservation constraints. This model is designed for rapid prediction of part quality metrics, cure state, flow front progression, and temperature fields, and supports gradient-based inversion for closed-loop control in advanced composite processing.</p>2025-08-23T19:20:08ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.29974102.v1https://figshare.com/articles/dataset/A_review_of_physics-informed_and_data-driven_approaches_for_manufacturing_process_optimization_in_polymer_matrix_composites/29974102CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299741022025-08-23T19:20:08Z |
| spellingShingle | A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites Ivan P. Malashin (22113739) Medicine Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified Polymer matrix composites pressure molding vacuum infusion physics-informed machine learning |
| status_str | publishedVersion |
| title | A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites |
| title_full | A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites |
| title_fullStr | A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites |
| title_full_unstemmed | A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites |
| title_short | A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites |
| title_sort | A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites |
| topic | Medicine Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified Polymer matrix composites pressure molding vacuum infusion physics-informed machine learning |