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Line heating is a critical process for forming ship hull outer plates, involving complex thermo-mechanical coupling effects. The deformation process is intricate, making real-time, dynamic, and accurate prediction of its spatiotemporal evolution challenging. To address this issue, this paper proposes a CNN-Double-Layer LSTM-AM model that integrates spatiotemporal features. The model uses a single heating line as a time step to dynamically predict transverse shrinkage and angular deformation. The model incorporates key influencing factors of line heating deformation: it employs a Convolutional Neural Network (CNN) to extract spatial features from deformation data, utilizes Double-Layer Long Short-Term Memory (LSTM) networks to model both temporal dependencies among multiple heating lines within a single plate and deformation correlations between multiple plates, and introduces an Attention Mechanism (AM) to enhance feature weights of critical time steps. Results demonstrate that the model achieves a Mean Absolute Error (MAE) of 0.0383 for angular deformation prediction and an R2 coefficient of 0.96 for transverse shrinkage prediction, which effectively solves the spatiotemporal dynamic evolution prediction challenge in line heating deformation processes and provides technical support for realizing closed-loop intelligent control of "processing-prediction-feedback".
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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