Compensate for longitudinally discrepant springback and bow in chain-die forming processes by multiple sections optimization

Liang, Zhonglin; Zou, Ting; Dai, Wei; Zhang, Zhiheng; Liu, Yang; Lu, Kang; Li, Dayong; Ding, Shou-Nian; Peng, Yinghong

doi:10.1007/s00170-022-09728-6

Cited by 3 publications

(1 citation statement)

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“…Trzepiecinski et al [20] further considered anisotropy, taking an anisotropic cold-rolled steel plate as the research object, and used an artificial neural network based on multilayer perceptron combined with the genetic algorithm to predict bending springback. In addition to the conventional bending process, Liang et al [21] proposed a chain die design method using a multi-section compensation strategy in the field of chain die forming, and used the nondominated ranking genetic algorithm II to minimize the deviation of the calculated cross-sectional springback profile from the required geometry. The results showed that the springback and longitudinal arch were reduced by using the proposed mold design method.…”

Section: Introductionmentioning

confidence: 99%

Research on the Curvature Prediction Method of Profile Roll Bending Based on Machine Learning

Cao

Liu

et al. 2023

Metals

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Roll-bending technology has a high degree of flexibility and does not require special molds. However, based on the existing plastic mechanics theory and finite element simulation, it is difficult to accurately analyze the complex spatial relationship of profile roll forming. Therefore, a fixed-curvature prediction model is constructed based on XGBoost (extreme gradient boosting), and the coupling effect of the process parameters and material performance parameters on the roll-forming process is explored. Combined with a Bayesian optimization algorithm, the hyperparameters of the fixed-curvature prediction model are optimized. In addition, based on the prediction result of the fixed curvature, a variable-curvature prediction model is established using the conditional random field (CRF). To further improve the prediction accuracy, an error compensation network is added after the result of the CRF in order to map the discrete sequence to the continuous sequence. The experimental results show that the mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) predicted by the models above are much smaller than other methods, which verifies the superiority of the prediction models.

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Section: Introductionmentioning

confidence: 99%