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.

ABSTRACT Production data, a critical component of industrial datasets derived from production processes, is widely used to train data‐driven models for forecasting and managing industrial processes. However, shifts in data distribution, caused by changes in production environments, operating conditions, and equipment states, disrupt the consistency between the training and deployment, and lead to catastrophic forgetting and a significant deterioration in both model prediction accuracy and stability. Although existing incremental learning methods have improved adaptability and mitigated forgetting, challenges remain in balancing knowledge retention with dynamic sample selection and ensemble optimization, particularly in complex industrial settings. To address these challenges, this paper proposes an incremental learning framework that includes two key strategies: sample‐importance‐aware buffer update and elastic weight consolidation (EWC) based learner construction for knowledge retention, and performance‐driven iterative strong learner construction with multi‐objective weight optimization. The buffer update dynamically adjusts capacity according to training loss fluctuations, selects high‐information samples guided by loss rates and uncertainty estimation, and maintains diversity through K‐means clustering. EWC consolidates previously acquired knowledge to mitigate forgetting during weak learner training. The ensemble construction evaluates individual learner performance comprehensively and iteratively adjusts model weights using a multi‐objective optimization method, balancing prediction accuracy, stability, and uncertainty. Experimental results on multiple publicly available industrial datasets, complemented by an external validation on a financial dataset, demonstrate that the proposed method outperforms several representative approaches in both accuracy and stability of prediction.

As bird’s-eye-view (BEV) semantic segmentation is simple-to-visualize and easy-to-handle, it has been applied in autonomous driving to provide the surrounding information to downstream tasks. Inferring BEV semantic segmentation conditioned on multi-camera-view images is a popular scheme in the community as cheap devices and real-time processing. The recent work implemented this task by learning the content and position relationship via Vision Transformer (ViT). However, its quadratic complexity confines the relationship learning only in the latent layer, leaving the scale gap to impede the representation of fine-grained objects. In view of information absorption, when representing position-related BEV features, their weighted fusion of all view feature imposes inconducive features to disturb the fusion of conducive features. To tackle these issues, we propose a novel cross-scale hierarchical Transformer with correspondence-augmented attention for semantic segmentation inference. Specifically, we devise a hierarchical framework to refine the BEV feature representation, where the last size is only half of the final segmentation. To save the computation increase caused by this hierarchical framework, we exploit the cross-scale Transformer to learn feature relationships in a reversed-aligning way, and leverage the residual connection of BEV features to facilitate information transmission between scales. We propose correspondence-augmented attention to distinguish conducive and inconducive correspondences. It is implemented in a simple yet effective way, amplifying attention scores before the Softmax operation, so that the position-view-related and the position-view-disrelated attention scores are highlighted and suppressed. Extensive experiments demonstrate that our method has state-of-the-art performance in inferring BEV semantic segmentation conditioned on multi-camera-view images.

Virtual try-on is a promising computer vision topic with a high commercial value wherein a new garment is visually worn on a person with a photo-realistic effect. Previous studies conduct their shape and content inference at one stage, employing a single-scale warping mechanism and a relatively unsophisticated content inference mechanism. These approaches have led to suboptimal results in terms of garment warping and skin reservation under challenging try-on scenarios. To address these limitations, we propose a novel virtual try-on method via progressive inference paradigm (PGVTON) that leverages a top-down inference pipeline and a general garment try-on strategy. Specifically, we propose a robust try-on parsing inference method by disentangling semantic categories and introducing consistency. Exploiting the try-on parsing as the shape guidance, we implement the garment try-on via warping-mapping-composition. To facilitate adaptation to a wide range of try-on scenarios, we adopt a covering more and selecting one warping strategy and explicitly distinguish tasks based on alignment. Additionally, we regulate StyleGAN2 to implement re-naked skin inpainting, conditioned on the target skin shape and spatial-agnostic skin features. Experiments demonstrate that our method has state-of-the-art performance under two challenging scenarios. The code will be available athttps://github.com/NerdFNY/PGVTON.

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.

In online clothing sales, static model images only describe specific clothing statuses towards consumers. Without increasing shooting costs, it is a subject to display clothing dynamically by synthesizing a continuous image sequence between static images. This paper proposes a novel human image sequence synthesis method by pose-shape-content inference. In the condition of two reference poses, the pose is interpolated in the pose manifold controlled by a linear parameter. The interpolated pose is transferred into the end shape by AdaIN and the attention mechanism to infer target shape. Then the content in the reference image is transferred into this target shape. In the content transfer, the visual features of the human body cluster and clothing cluster are extracted, respectively. And the Sobel gradient is adopted to extract clothing texture variation. In the feature inferring, the multiscale feature-level optical flow warps source features, and style code infusion infers new region content without source features. Extensive experiments demonstrate that our method is superior in inferring clear layouts and transferring reasonable content compared to the pose transfer baselines. Moreover, our method has been verified to apply in parsing-guided image inference and dynamic display based on the pose sequence.

We present an exploratory study to improve the performance of a knowledge push system in product design. We focus on the domain of knowledge matching, where traditional matching algorithms need repeated calculations that result in a long response time and where accuracy needs to be improved. The goal of our approach is to meet designers’ knowledge demands with a quick response and quality service in the knowledge push system. To improve the previous work, two methods are investigated to augment the limited training set in practical operations, namely, oscillating the feature weight and revising the case feature in the case feature vectors. In addition, we propose a multi-classification radial basis function neural network that can match the knowledge from the knowledge base once and ensure the accuracy of pushing results. We apply our approach using the training set in the design of guides by computer numerical control machine tools for training and testing, and the results demonstrate the benefit of the augmented training set. Moreover, experimental results reveal that our approach outperforms other matching approaches.