Long short-term memory (LSTM) -based neural network model for optimizing composite manufacturing process using autoclave.
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| Title: | Long short-term memory (LSTM) -based neural network model for optimizing composite manufacturing process using autoclave. |
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| Authors: | Bolar, Sourav1 (AUTHOR), Corns, Steven1 (AUTHOR), Pundhir, Nayan2 (AUTHOR), Chandrashekhara, Kumbla2 (AUTHOR) chandra@mst.edu |
| Source: | International Journal of Advanced Manufacturing Technology. Feb2026, Vol. 142 Issue 9/10, p4779-4794. 16p. |
| Subjects: | Long short-term memory, Autoclaves, Artificial neural networks, Composite material manufacturing, Prediction models, Data augmentation, Temperature control, Process optimization |
| Abstract: | Producing high-quality fiber-reinforced composites requires precise temperature control during autoclave curing, as even small variations can lead to defects that compromise strength and reliability. At the same time, manufacturers aim to reduce energy use and shorten curing cycles without sacrificing material performance. To address these challenges, this study develops a data-driven Long Short-Term Memory (LSTM) neural network model capable of forecasting temperature evolution inside the autoclave throughout the curing cycle. The model is trained on time-series temperature data collected from multiple sensing locations, enabling it to learn the spatial and temporal trends that govern heat flow during curing. Data augmentation techniques such as time shifting, scaling, and jittering were applied, helping the model better handle noise and inconsistencies in the dataset. The resulting predictions closely match expected temperature patterns, showing that learning-based models can effectively capture the complex and dynamic thermal behavior within the autoclave. By offering early insight into temperature behavior during curing, the LSTM approach can support better heating control, improve curing consistency, and help reduce overall cycle time. This capability leads to more uniform temperature distribution, fewer unnecessary dwell periods, and higher-quality composite parts. These results show that predictive deep learning can be successfully integrated into autoclave operations, providing a strong foundation for future real-time, adaptive process control in smart composite manufacturing. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191606249 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Long short-term memory (LSTM) -based neural network model for optimizing composite manufacturing process using autoclave. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bolar%2C+Sourav%22">Bolar, Sourav</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Corns%2C+Steven%22">Corns, Steven</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pundhir%2C+Nayan%22">Pundhir, Nayan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chandrashekhara%2C+Kumbla%22">Chandrashekhara, Kumbla</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> chandra@mst.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. Feb2026, Vol. 142 Issue 9/10, p4779-4794. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Autoclaves%22">Autoclaves</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Composite+material+manufacturing%22">Composite material manufacturing</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Temperature+control%22">Temperature control</searchLink><br /><searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Producing high-quality fiber-reinforced composites requires precise temperature control during autoclave curing, as even small variations can lead to defects that compromise strength and reliability. At the same time, manufacturers aim to reduce energy use and shorten curing cycles without sacrificing material performance. To address these challenges, this study develops a data-driven Long Short-Term Memory (LSTM) neural network model capable of forecasting temperature evolution inside the autoclave throughout the curing cycle. The model is trained on time-series temperature data collected from multiple sensing locations, enabling it to learn the spatial and temporal trends that govern heat flow during curing. Data augmentation techniques such as time shifting, scaling, and jittering were applied, helping the model better handle noise and inconsistencies in the dataset. The resulting predictions closely match expected temperature patterns, showing that learning-based models can effectively capture the complex and dynamic thermal behavior within the autoclave. By offering early insight into temperature behavior during curing, the LSTM approach can support better heating control, improve curing consistency, and help reduce overall cycle time. This capability leads to more uniform temperature distribution, fewer unnecessary dwell periods, and higher-quality composite parts. These results show that predictive deep learning can be successfully integrated into autoclave operations, providing a strong foundation for future real-time, adaptive process control in smart composite manufacturing. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00170-025-17224-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 4779 Subjects: – SubjectFull: Long short-term memory Type: general – SubjectFull: Autoclaves Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Composite material manufacturing Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Temperature control Type: general – SubjectFull: Process optimization Type: general Titles: – TitleFull: Long short-term memory (LSTM) -based neural network model for optimizing composite manufacturing process using autoclave. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bolar, Sourav – PersonEntity: Name: NameFull: Corns, Steven – PersonEntity: Name: NameFull: Pundhir, Nayan – PersonEntity: Name: NameFull: Chandrashekhara, Kumbla IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02683768 Numbering: – Type: volume Value: 142 – Type: issue Value: 9/10 Titles: – TitleFull: International Journal of Advanced Manufacturing Technology Type: main |
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