The free-bending technique, distinguished by its exceptional flexibility in axis control, is emerging as a transformative paradigm for manufacturing complex tubular structures, overcoming geometric limitations inherent to conventional tube bending manufacturing processes. However, the high flexibility in multi-axis free-bending systems introduces nonlinear control complexities that critically compromise the tube forming accuracy. Real-time machine vision approaches enable in-process tracking of tubular geometric deviations, providing a fast method for axis prediction. To this end, this paper presents a real-time vision-enhanced prediction system that integrates with an LSTM-Transformer framework. A high-precision visual sensing system is developed to capture tube-end trajectory, integrating 3D-printed markers, depth camera, kinematic decoupling, and instance segmentation for accurate motion tracking and process parameter inversion. Subsequently, a physics-informed hybrid LSTM-Transformer architecture is proposed for dynamic bend axis springback prediction, incorporating trajectory-derived physical constraints and multi-objective optimization for spatio-temporal springback prediction during dynamic forming. Additionally, an online differential geometry mapping method for real-time curvature parameter estimation is introduced, eliminating the need for post-scanning and additional equipment, enabling closed-loop process parameter compensation during bending. Experimental results show that the proposed method reduces the mean absolute error of axial springback prediction by more than 60% compared to traditional theoretical models, with the mean absolute error for all groups remaining below 12 mm.

Dependency-based models are widely used to extract semantic relations in text. Most existing dependency-based models establish stacked structures to merge contextual and dependency information, which encode the contextual information first and then encode the dependency information. However, this unidirectional information flow weakens the representation of words in the sentence, which further restricts the performance of existing models. To establish bidirectional information flow, a dual attention graph convolutional network (DAGCN) with a parallel structure is proposed. Most importantly, DAGCN can build multi-turn interactions between contextual and dependency information to imitate the multi-turn looking-back actions of human beings. In addition, multi-layer adjacency matrix-aware multi-head attention (AMAtt), including context-to-dependency attention and dependency-to-context attention, is carefully designed as a merge mechanism in the parallel structure to preserve the structural information of sentences and dependency trees during interactions. Furthermore, DAGCN is evaluated on the popular PubMed dataset, TACRED dataset and SemEval 2010 Task 8 dataset to demonstrate its validity. Experimental results show that our model outperforms the existing dependency-based models.

Predicting the cross-sectional profile of the whole bending segment for metal tube bending is essential to achieve high-precision bending, yet still remains challenging. The existing prediction methods mainly base on theoretical derivation under certain assumptions and approximations, which do not fully characterize the whole bending segment profile neither do they fully utilize the information in the bending process. In this study, a Bo-LSTM-based progressive prediction method for the cross-sectional profile sequence is proposed, which comprehensively utilizes the profile information during the bending process and achieves an accurate prediction of the cross-sectional profile of the whole bending segment in the subsequent bending process. Firstly, the method of describing the cross-sectional profile in polar radial vector and the cross-sections of the bending segment in discrete sequences are proposed, which cover the information of cross-sectional distortion and wall thickness variation (viz. cross-sectional defects) for the whole bending segment. Secondly, an LSTM network is constructed integrating Bayesian-optimization-based hyper-parameters selection approach to progressively predict the tube cross-sectional profile sequence. Finally, the proposed methods are verified on simulated datasets as well as experimental data, and the accuracy is compared with networks of different structures. The results show that Bo-LSTM has better prediction accuracy. Meanwhile, the progressive prediction pattern has better robustness compared to chain prediction pattern.

While it is risky considering spacecraft constraints and unknown environment on asteroid, surface sampling is an important technique for asteroid exploration. One of the sample return missions is to seek an optimal landing site, which may be in hazardous terrain. Since autonomous landing is particularly challenging, it is necessary to simulate the effectiveness of this process and prove the onboard optical hazard avoidance is robust to various uncertainties. This paper aims to generate realistic surface images of asteroids for simulations of asteroid exploration. A SinGAN-based method is proposed, which only needs a single input image for training a pyramid of multi-scale patch generators. Various images with high fidelity can be generated, and manipulations such as shape variation, illumination direction variation, super resolution generation are well achieved. The method's applicability is validated by extensive experimental results and evaluations. At last, the proposed method has been used to help set up a test environment for landing site selection simulation.