Physical Logic Enhanced Network for Small-Sample Bi-layer Metallic Tubes Bending Springback Prediction
نویسندگان
چکیده
Bi-layer metallic tube (BMT) plays an extremely crucial role in engineering applications, with rotary draw bending (RDB) the high-precision processing can be achieved, however, product will further springback. Due to complex structure of BMT and high cost dataset acquisition, existing methods based on mechanism research machine learning cannot meet requirements springback prediction. Based preliminary analysis, a physical logic enhanced network (PE-NET) is proposed. The architecture includes ES-NET which equivalent single-layer tube, SP-NET for final prediction sufficient samples. Specifically, first stage, theory-driven pre-exploration data-driven pretraining, are constructed, respectively. In second under logic, PE-NET assembled by then fine-tuned small sample composite loss function. validity stability proposed method verified FE simulation dataset, small-sample angle potential interpretability applications demonstrated.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2022
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-20500-2_10