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.
Recommended Citation
S. Bolar et al., "Long Short-term Memory (LSTM) -Based Neural Network Model for Optimizing Composite Manufacturing Process using Autoclave," International Journal of Advanced Manufacturing Technology, Springer, Jan 2026.
The definitive version is available at https://doi.org/10.1007/s00170-025-17224-w
Department(s)
Engineering Management and Systems Engineering
Second Department
Mechanical and Aerospace Engineering
Publication Status
Open Access
Keywords and Phrases
Autoclave curing; Composite materials; Deep learning; Long Short-Term memory (LSTM); Temperature prediction; Time-series forecasting
International Standard Serial Number (ISSN)
1433-3015; 0268-3768
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Springer, All rights reserved.
Publication Date
01 Jan 2026
Included in
Aerospace Engineering Commons, Mechanical Engineering Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
