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.

Reliability assessment is essential for ensuring the stable operation of industrial systems. This study presents a hybrid reliability assessment framework that integrates mechanism-based and data-driven approaches for online degradation evaluation. A representative degradation mode— impact fatigue—is investigated to demonstrate the method. The mechanistic component is formulated by using crack propagation theory and Miner’s cumulative damage rule; while the data-driven one employs a generalized power-law Wiener process with an additive nonlinear drift. Both sources of information are fused through an evolving unscented Kalman filter that performs optimal and unbiased online state estimation via prediction and observation updates. In the proposed framework, the Wiener process serves as the state equation, and the mechanistic model functions as the measurement equation. The reliability of impact fatigue is further evaluated by using the inverse Gaussian distribution, which is inherently associated with the Wiener process. Application to a thrust bearing in the main pump of a pressurized water reactor demonstrates the effectiveness and superiority of the proposed method over existing ones.

Cooling water intake systems in nuclear power plants are increasingly susceptible to operational disruptions due to marine organism intrusions. Traditional optimization methods based on static numerical simulations and evolutionary algorithms lack adaptability to complex, dynamic operational conditions and uncertainties. This study introduces a cognitive intelligence-integrated optimization framework, combining discrete element method (DEM) and finite element method (FEM) simulations with deep reinforcement learning (DRL), fuzzy analytic hierarchy process, and expert knowledge to optimize filtration net parameters adaptively and robustly.

Existing studies on traffic flow prediction primarily rely on on-board devices to collect vehicle trajectory data , which can potentially infringe upon the privacy of users and limit the applicability of the method. Additionally, traffic flow prediction remains challenging due to the complex spatial and temporal dependencies within real-world traffic networks. To address these limitations, this paper introduces a framework for analyzing discrete vehicle trajectory data at urban intersections. By incorporating various external physical factors into traffic flow prediction, this framework derives embedding vectors from vehicle trajectory sequences and road network topology , modeling their spatio-temporal dependencies using Skip-Gram and GraphSAGE, respectively. Additionally, the intersection similarity is introduced to capture and integrate traffic flow patterns between the target intersection and similar intersections. A Spatio-Temporal Graph Convolutional Neural Network (ST-GCN) algorithm, which combines Graph Convolutional Networks (GCN) with Long Short-Term Memory (LSTM), is developed to achieve precise traffic flow prediction. Extensive experiments on a real-world traffic flow dataset from Qingdao, China, validate that the proposed method outperforms state-of-the-art baseline methods .

Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated Heterogeneous Computing Tasks (HCTs) in IIoT environments remains a significant challenge. In this article, we first introduce the representation and notation of such HCTs and define a computing network structure. We then propose an initial mathematical programming-based scheduling model aimed at minimizing HCT completion time. To make this model easy to solve, we reformulate it by using logical constraints and derive a constraint programming-based model, for which a feasibility-guaranteed solution algorithm is developed. This algorithm leverages two easily-verified propositions to either identify feasible solutions or demonstrate the infeasibility of the problem.Furthermore, we have proven a critical proposition that facilitates the development of a hybrid programming-based scheduling approach, effectively combining the strengths of both mathematical and constraint programming models. As demonstrated through extensive computational experiments, our proposed approach achieves an average reduction of 20% in HCT completion time in comparison with its existing peers. It consistently and timely provides the high-quality solutions that meet the required deadlines.

Human-cyber-physical system has emerged as a pivotal tool for intelligent production throughout the whole lifecycle. In this paper, a health state assessment approach based on hierarchical data fusion is proposed under human-cyber-physical system architecture. The human-cyber-physical system emphasizes the flexibility of dynamic operating states and the evolvability of the enabling models. First of all, a multiple hierarchy consisting of equipment, systems and function is established as a physical basis for human-cyber-physical system. Regarding the equipment-level assessment characterized by extensive input parameters, a dynamic weighting method is exploited to highlight the abnormal parameters. Thus, the degraded device can be significantly reflected in the system-level assessment by fusing equipment parameters. To evaluate the operation function of a plant, a Nearest Class Mean classifiers-based Random Forests algorithm is explored considering the small-scale and imbalanced instances in different health levels. However, the conventional fixed and changeable models threaten the accuracy and rationality of the human-cyber-physical system. We introduce incremental learning to reform Random Forests that can update promptly with new data and knowledge. Finally, the proposed scheme and technologies are applied in a nuclear power plant to demonstrate their effectiveness.Note to Practitioners—The modern production desires to be further intelligent for improvement of economic benefits but with the consistent requirements of high safety and reliability. The trades-off between efficiency and safety bring the greater challenges to the operation and maintenance for production. This paper proposes an evolvable human-cyber-physical system scheme that not only integrates more intelligent elements but also attaches some solution to the abovementioned problem. The scheme is implemented on the multi-hierarchy health states assessment which is the core in production maintenance against lagging. This equipment-system-function fusion and assessment provide a unified platform for multi-departments to collaborate and self-manage. The novel weighting method abandons the conventional subjective evaluation criteria with high time consumption. Computers are convenient to possess sensing data efficiently and highlight the anomaly obviously. In particular, it is mentioned that the intelligent models in human-cyber-physical systems are always fixed and unable to alter with the physical system changes. Incremental learning, a promising branch of machine learning, is introduced to reform the assessment model to be stainable developed with new data adding. The scheme takes a nuclear plant as example because it is more corresponding to these issues. The future studies can extend the scheme to other scenarios.

During the service process of active phased array antenna (APAA), factors such as array heating and wind loading cause significant deformation, which will seriously affect the electromagnetic (EM) performance. Obtaining the geometric parameters of the array through sensors and predicting the real-time EM performance based on the digital twin (DT) technique are crucial to guarantee the service quality of the array antenna (e.g., the detection accuracy of a target). Traditional offline simulation methods can calculate the real EM performance of APAA with geometric errors. However, it requires a large number of computational resources and computation time, which is difficult to meet the demand for real-time prediction in DT. In this article, a generalized DT framework based on the reduced-order model for APAA is proposed. First, we introduce a dynamic-static attention-enhanced convolutional network for real-time computation of key EM indicators. Then, a super-resolution generating network is proposed, which realizes the mapping of array geometrical errors to the 3-D far-field pattern, and provides support for comprehensive performance evaluation. The framework proposed in this article constructs a twin model of APAA, realizes the virtual-reality mirroring of the EM performance, and is deployed and applied in an APAA DT platform.

Trajectory planning is a fundamental step for robotic spraying. This article proposes a semantic segmentation based spraying trajectory planning method for complex products. In order to divide the complex product surface into different painting regions, a geometric feature guided segmentation network (GFGS-Net) is developed. In the GFGS-Net, a geometric local encoding module is constructed to capture edge points based on the proposed local geometric feature, then encode the geometric feature and semantic feature of spraying surface. An attentive feature augmentation module is further designed to adaptively reweight and concatenate the encoded geometric feature and encoded semantic feature. With the aid of such feature representation, GFGS-Net can better describe the industrial product surface and perform accurate product surface segmentation. Afterwards, different spraying patterns are designed for each region, and key spraying parameters spraying overlap distance and spray gun speed are optimized considering the paint thickness and spraying time. Based on these two aspects, the spraying trajectory can be generated through point cloud slicing followed by alpha-shapes and intercept reduction. Alpha-shapes is used to obtain the contour lines of complex product surface, and the contour lines is further smoothed by intercept reduction to make the painting robot work more smoothly. The experiment results demonstrate that the proposed method can significantly improve painting quality and efficiency for complex products.

We propose a novel and effective deep learning method, called deep pattern matching, for predicting the energy consumption of complex structures, which helps designers to develop ecological solutions with minimal fabrication energy for additive manufacturing. This new method does not necessitate the real energy consumption values of complex structures for training the prediction model, substantially reducing the cost of training data collection, which can be prohibitively expensive. This novel method exploits simple structures whose real energy consumption values are far cheaper to measure, by matching the similar infill pattern of complex structures from simple structures, and then approximating the energy value of the pattern in the complex structures by the matched one in training phase. This effective algorithm is designed dynamically for allowing us to match patterns with arbitrary shapes. We evaluate our deep pattern matching algorithm on various complex structures, where the highest total energy accuracy is up to 97.3%. The extensive empirical results confirm the effectiveness and robustness of the proposed method, exhibiting a great potential to advance the real usage of deep learning models for energy consumption prediction of complex structures in ecological additive manufacturing.

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.

Accurate 3D object segmentation in point clouds is a basis for industrial robot applications, such as robot manipulation and digital twin, which require an understanding of the 3D environment. However, the unstructured and disordered nature of point clouds makes it challenging, especially for the incomplete 3D data under a single view in the real-world scenario. To this end, this article proposes a novel 3D object segmentation framework (3DT-Seg) based on Cross-Window Point Transformer (CP-Former). CP-Former captures the long-range dependencies between local windows and latent semantic boundaries to enhance the point-wise features extracted from irregular point clouds via a bidirectional cross-attention mechanism. In addition, a contrastive learning loss and an adaptive dual aggregation strategy are introduced on semantic transition regions during the semantic supervising and instance clustering process, respectively. In this way, the latent boundary information is further utilized to improve the overall segmentation performance. Experiments on the popular benchmark dataset (S3DIS) show the state-of-the-art performance of the proposed approach in terms of semantic and instance segmentation. Furthermore, a real-world point cloud dataset (IP-Cloud) for the robotic grasping task is presented to fully validate the effectiveness of our method in practice, where it also achieves remarkable performance.

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.

Industrial Internet of Things (IIoT) is changing the way in which factories operate with the help of various industrial applications. However, the execution uncertainty of computing tasks has always been ignored in IIoT applications. In this article, we define a novel conditional task graph to describe the execution uncertainty and present a generation algorithm to obtain all task scenario graphs and corresponding occurrence probabilities. Then, a new IIoT-oriented multitask scheduling model under execution uncertainty is built. This model is simplified by reformulating the nonlinear constraints and subsequently decomposed into several small-scale models using the Lagrange multipliers, from which a decomposition-based algorithm is derived to solve the decomposed small-scale models and progressively acquire a well-optimized solution of the initial model. Furthermore, a patching algorithm is constructed to improve the obtained solution. Finally, many test cases are generated, and four selected algorithms are taken for comparison to evaluate the performance of our algorithms. The results demonstrate that our algorithms remarkably outperform the others. Besides, the solutions of the proposed algorithms can completely satisfy the execution deadline constraints of different task scenarios.

Nowadays, most high-precision products are still assembled manually, which leads to a low one-time pass rate of products. Workers need to adjust unqualified products repeatedly based on experience, resulting in inefficiency and poor quality consistency. In this work, we propose an explore-exploit reinforcement learning (EERL) framework to suggest the assembly parameters and quantity that workers need to adjust at each step. Jointed with the pretrained product performance prediction model, EERL can output a sequential decision to guide workers hand in hand to adjust unqualified products. EERL includes a two-phase learning process: 1) exploration; and 2) exploitation. In exploration phase, agents are encouraged by the curiosity to fully explore the qualified assembly states in the assembly-adjustment feature space. The regulated difference of random network distillation is used as a measure of curiosity. During exploitation, the agent is trained to learn an assembly-adjustment guidance policy of moving from any unqualified initial assembly state to the corresponding qualified state while satisfying the assembly-adjustment constraints. The curriculum learning mechanism is introduced to learn effectively in the complex environment with sparse reward and adjustment constraints. The proposed approach is validated on an benchmark optimization function and a case study of the gyroscope. The experimental results demonstrate that the proposed approach outperforms other existing approaches.

Planning a disassembly sequence of product structure in a remanufacturing-oriented manner is a newly emerging and important problem, which has attracted considerable attention owing to high demand for sustainable development. However, current approaches cannot lead to a reliable disassembly plan focused on the product structure itself, starting from functional properties to application demand stimulated by remanufacturing. First, they fail to focus on the role that EOL products play in the promotion of remanufacturing. Second, determining a disassembly solution for advanced and sophisticated products is complicated and time-consuming. Third, the uncertainty of mixed information triggered by the intercrossing of multiple subjects is neglected. In this study, an improved DSP method is first proposed by integrating the remanufacturing properties of EOL products, an improved QICA, and the measure and update of the reliability of experts’ decision-making to perform DSP. The functional recoverability of EOL product parts is marked to identify the remanufacturing value of a disassembly plan. The expected reproduction probability is adopted to increase the population diversity and improve the ability of QICA to search for feasible solutions. The model and update mechanism of experts’ recognition degree are constructed to narrow the score deviation. Finally, we provide a case for a sewing machine to verify the superiority of the improved QICA. The results show that it is capable of obtaining satisfactory solutions and outperforms the quantum genetic algorithm and particle swarm optimizer.

To confront the great challenge of industrial big data, the software-defined industrial networks (SDINs) are introduced to dynamically coordinate these data flows among the heterogeneous and distributed computing resources. Deciding how to more efficiently schedule many homogeneous computing tasks, which extensively appear in SDIN, becomes of critical importance. To this end, this article first illustrates some related notations and assumptions of the homogeneous computing tasks and computing networks, from which a new targeted optimization model is formulated. Then, the model is significantly enhanced by reformulating all the nonlinear constraints and inventively establishing the symmetry-breaking constraints and computation time cuts. Furthermore, considering the computational complexity, the scheduling process with many homogeneous computing tasks is further viewed as two associated phases: computing nodes assignment and tasks sequencing. As a result, a novel bilevel decomposition algorithm is proposed using Lagrangian decomposition and a new form of Lagrangian relaxation. Finally, a real industrial scenario is chosen, and three comparison algorithms are used to demonstrate the preponderant performance of the proposed algorithm.

Quality prediction is committed to predicting the key quality-related variables to obtain real-time feedback information for process control. To achieve the robust and transferable quality prediction of products processed in complex and uncertain manufacturing processes, deep learning methods have been developed. However, the training process of deep learning methods requires a large amount of annotated data to avoid overfitting, and the labeling process of quality-related variables is often time-consuming and labor-intensive. Therefore, few-shot quality prediction in a multistage manufacturing process is formalized to address the lack of annotated data and deal with previously unseen tasks without an additional training process. In addition, a novel contrastive decoder generator (CDG) is proposed to enable few-shot quality prediction, which consists of a machine feature encoder, a contrastive stage, and task feature generator, and an instance-specific decoder generator. Experiments are conducted on a public quality prediction dataset collected from an actual production line. The CDG achieves state-of-the-art results on this dataset for few-shot quality prediction settings, which proves the effectiveness of the CDG. Additionally, detailed experiments are performed to evaluate the roles of different modules in the CDG.

Pose estimation is an essential technology for industrial robots to perform precise gripping and assembly. The state-of-the-art deep learning-based approach uses an indirect strategy, i.e., first finding local correspondence between the 2-D image and 3-D model, and then using the perspective-n-point and RANSAC methods to calculate the poses of ordinary objects. However, the metal parts in industry are reflective and textureless, making it difficult to identify distinguishable point features to establish 2-D–3-D correspondences. To address this problem, in this article, we propose a novel deep learning based two-stage method for pose estimation of reflective textureless metal parts, which accurately estimates the target pose using monocular red green blue (RGB) images. Since contours play an important role in both keypoints prediction and pose estimation stages, our method is named ContourPose. First, an additional contour decoder is adopted to implicitly constrain the keypoints prediction in the former stage, which improves the accuracy of the keypoints prediction. Then, the predicted contour of the previous stage is taken as geometric prior that is used to iteratively solve for the optimal pose. Experiments indicate that the proposed approach for reflective textureless metal parts has a significant improvement over the state-of-the-art approaches.

For a complex electromechanical product that is a cyber–physical system (CPS), its dynamic behaviors are embodied in the closed-loop control between the logic process in its cyber component and actual actuators/sensors in its physical component, and thus, a well-defined model of the control is important to create a digital twin that acts as much like the real machine as possible. This article proposes a Petri nets (PNs)-based modeling solution that employs hybrid PNs (HPNs) for physics and system of sequential systems with shared resources (S4R) nets for logic in building a hierarchical control model. We also present PNs technologies for implementing a smooth transition and bidirectional mapping from the virtual prototype to the real machine. These technologies involve a PNs integration of a reinforcement learning (RL) method for generating a workflow scheduling agent in design, an extension of PNs definitions that is compatible with the microcontroller for easy deployment in manufacturing, and an architecture of PNs execution recording for data-driven monitoring in service. A software kit is provided for the solution that includes an integrated development environment of PNs, tools for quickly building a virtual prototype, and a monitor server for remote data-driven monitoring. This solution is successfully applied in the development of a typical cyber–physical product case, namely, the chemiluminescence immunoassay (CLIA) analyzer.

The class imbalance problem has a huge impact on the performance of diagnostic models. When it occurs, the minority samples are easily ignored by classification models. Besides, the distribution of class imbalanced data differs from the actual data distribution, which makes it difficult for classifiers to learn an accurate decision boundary. To tackle the above issues, this article proposes a novel imbalanced data classification method based on weakly supervised learning. First, Bagging algorithm is employed to sample majority data randomly to generate several relatively balanced subsets, which are then used to train several support vector machine (SVM) classifiers. Next, these trained SVM classifiers are adopted to predict the labels of those unlabeled data, and samples that are predicted as minority class are added to the original dataset to reduce the imbalance ratio. The critical idea of this article is to introduce real-world samples into the imbalanced dataset by virtue of weakly supervised learning. In addition, bidirectional gated recurrent units are used to construct a diagnostic model for fault diagnosis, and a new weighted cross-entropy function is proposed as the loss function to reduce the impact of noise. Besides, it also increases the model's attention to the original minority samples. Furthermore, experimental evaluations of the proposed method are conducted on two datasets, i.e., Prognostics and Health Management challenge 2008 and 2010 datasets, and the experimental results demonstrate the effectiveness and superiority of the proposed method.

For metal surface defect inspection, deep-learning-based methods have largely improved the inspection accuracy. However, insufficient data and the diversity of defects usually pose challenges for these methods. To solve these problems, traditional data augmentation methods often augment data by applying image-level geometric variations, usually without introducing new features of unknown defects, which yields limited improvements in defect inspection. Given such circumstances, in this article, a new data augmentation algorithm named Mask2Defect is proposed. Via prior knowledge-based data infusing, this method is able to generate defects with varied features. A large volume of defects with different shapes, severities, scales, rotation angles, spatial locations, and part numbers can be generated in a controllable manner. These generated defects will work as teacher samples to fine-tune the inspection model and automatically adapt it to a wider range of defects. To be specific, we first encode the prior knowledge into the teacher mask via the industrial prior knowledge encoder and render the defect details according to the mask with the mask-to-defect construction network. Then, the fake-to-real domain transformation GAN is used to transform the rendered samples from the fake domain into the real defect domain. Experiments reveal that the synthesized image quality of our method outperforms the state-of-the-art generative methods, and the performance of the inspection model in defect classification and localization has also been improved by fine-tuning with the generated samples.

Quality prediction, as the basis of quality control, is dedicated to predicting quality indices of the manufacturing process. In recent years, data-driven deep learning methods have received a lot of attention due to their accuracy, robustness, and convenience for the prediction of quality indices. However, the existing studies mainly focus on the quality prediction of a single machine, while ignoring dependency relationships among multiple machines in multistage manufacturing process. To tackle the above issues, a novel path enhanced bidirectional graph attention network (PGAT) is proposed in this article. PGAT models the dependencies among machines into directed graphs and introduces graph attention network to encode the dependencies. Nonetheless, it is difficult for graph neural networks to encode long-distance dependencies. Hence, dependency path information is introduced into the features of machines. Moreover, in order to solve the label noise problem that often occurs in actual industrial dataset, a masked loss function is devised. With its help, batch training with noisy labels can be achieved, which improves the training efficiency. Furthermore, experiments are conducted on a public quality prediction dataset collected from an actual production line. PGAT achieves the state-of-the-art results on this dataset, which confirms the effectiveness of PGAT. Additionally, the experimental results demonstrate the critical role of modeling dependency relationships among machines.

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.

A major obstacle in digital projector technology is that images projected onto nonideal surfaces with large depth variances can easily become blurred. In this article, present a method to overcome projection defocus for projection surfaces that inevitably have complex shapes and large depth variances. The proposed method has two main advantages over traditional methods. First, an edge-intensification-based defocus compensation algorithm is proposed to manipulate the input image to compensate for its projection defocus before blurring occurs. Unlike previous time-consuming compensation algorithms, the proposed algorithm has very high efficiency, as it is noniterative and open loop. Second, a sinusoidal-projection-based estimation method is proposed to reduce kernel estimation errors on complex surface types. Unlike previous methods limited to specific surface types, the proposed method can provide consistently good kernel estimation results even for discontinuous and textured (nonpure white) projection surfaces. Hence, the proposed method can be applied to a wider range of applicable surfaces. These two contributions are demonstrated through extensive experiments and compared with the state-of-the-art methods.

Successful product development relies on the enterprise system (ES) to manage product lifecycle data and support decision-making in various levels. Since the amount of data generated in the industrial Internet of Things is increasing dramatically, new paradigms of the ES are required to realize the distributed intelligence. Recent progress in edge computing has enabled advances in decentralized decision support systems. Although the product design is a key stage of new product development, none of the recent studies in edge-based ES have shed light on this stage. Therefore, an edge-based ES framework for design scheme evaluation is proposed in this article. It not only accomplishes most of the decision-making tasks within the edge mesh of an enterprise, but also solves the information island of multiple decision makers from different geographical regions. Moreover, a multigroup decision-making algorithm is proposed to enable collaborative design scheme evaluation. The evaluation processes of designers, experts, and customers are analyzed systematically in this article. For one advantage, the trapezium cloud model is applied to convert the qualitative evaluation information given by designers and experts into qualitative values. It decreases the cognitive discrepancy and solves the information distortion. For another, EEG data are utilized to explore the implicit psychological states of customers during the product operation. All the evaluation results of multiple groups of decision makers are integrated by the fuzzy measure and Choquet integral to determine the optimal design scheme. A case study is conducted to illustrate the feasibility of the method proposed in this article.

Disassembly sequencing is important for remanufacturing and recycling used or discarded products. AND/OR graphs (AOGs) have been applied to describe practical disassembly problems by using “AND” and “OR” nodes. An AOG-based disassembly sequence planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While precedence and “AND” relationship issues can be addressed, OR (exclusive OR) relations are not well addressed by the existing heuristic methods. Thus, an ineffective result may be obtained in practice. A conflict matrix is introduced to cope with the exclusive OR relation in an AOG graph. By using it together with precedence and succession matrices in the existing work, this work proposes an effective triple-phase adjustment method to produce feasible disassembly sequences based on an AOG graph. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with the traditional economical criterion leads to a novel dual-objective optimization model such that disassembly profit is maximized and disassembly energy consumption is minimized. An improved artificial bee colony algorithm is developed to effectively generate a set of Pareto solutions for this dual-objective disassembly optimization problem. This methodology is employed to practical disassembly processes of two products to verify its feasibility and effectiveness. The results show that it is capable of rapidly generating satisfactory Pareto results and outperforms a well-known genetic algorithm.

Disassembly planning aims to perform the optimal disassembly sequence given a used or obsolete product in terms of cost and environmental impact. This paper presents a new multiobjective programming model for the target disassembly sequencing. It proposes an improved multiobjective ant colony algorithm to derive optimal target disassembly sequences. This work also establishes some indices on disassembly scheme evaluation and a fuzzy integral method to evaluate the obtained disassembly scheme. A CNC machine tool example is given to illustrate the proposed models and the effectiveness of the proposed algorithm. Both theoretical and simulation results demonstrate that the proposed approach can perform the quantitative analysis of a disassembly process effectively. Such results can help decision makers select the best plans and sequences when executing a disassembly process of a product.

Product assembly simulation is considered as one of the key technologies in the process of complex product design and manufacturing. Virtual assembly realizes the assembly process design, verification, and optimization of complex products in the virtual environment, which plays an active and effective role in improving the assembly quality and efficiency of complex products. In recent years, augmented reality (AR) and digital twin (DT) technology have brought new opportunities and challenges to the digital assembly of complex products owing to their characteristics of virtual reality fusion and interactive control. This paper expounds the concept and connotation of AR, enumerates a typical AR assembly system structure, analyzes the key technologies and applications of AR in digital assembly, and notes that DT technology is the future development trend of intelligent assembly research.

Currently, human motion analysis using three‐dimensional (3D) data creates closer awareness in computer vision with the introduction of cost‐effective Kinect or other depth cameras. This study attempts to segment a continuous 3D skeletal sequence into several disjointed sub‐sequences, each of which is corresponding to a complete action. To address this issue, the authors propose a supervised time‐series segmentation algorithm. A bidirectional propagation search scheme is employed to reach a solution. Specifically, a human skeleton is formulated as a point in multidimensional space, and a motion trajectory is further represented as a sequence. Each training action sequence serves as an atom in a dictionary, which is adopted by an l 2 ‐ regularised collaborative representation classifier. Based on the fact that the reconstruction error of the collaborative representation measures the similarity between a test sub‐sequence and training sequences, they utilise its variation over time to capture action transition. Cut point detection and sub‐sequence recognition are simultaneously achieved. Experiments on the authors’ recorded 3D skeletal sequences demonstrate that the proposed algorithm outperforms existing human motion segmentation techniques. Their algorithm is capable of extending to segment various dimensional sequences. This extensibility is validated by synthetic signal segmentation experiments.

Assembly variation analysis of parts that have flexible curved surfaces is much more difficult than that of solid bodies, because of structural deformations in the assembly process. Most of the current variation analysis methods either neglect the relationships among feature points on part surfaces or regard the distribution of all feature points as the same. In this study, the problem of flexible curved surface assembly is simplified to the matching of side lines. A methodology based on Bézier curves is proposed to represent the side lines of surfaces. It solves the variation analysis problem of flexible curved surface assembly when considering surface continuity through the relations between control points and data points. The deviations of feature points on side lines are obtained through control point distribution and are then regarded as inputs in commercial finite element analysis software to calculate the final product deformations. Finally, the proposed method is illustrated in two cases of antenna surface assembly.

Actively pushing design knowledge to designers in the design process, what we call ‘knowledge push’, can help improve the efficiency and quality of intelligent product design. A knowledge push technology usually includes matching of related knowledge and proper pushing of matching results. Existing approaches on knowledge matching commonly have a lack of intelligence. Also, the pushing of matching results is less personalized. In this paper, we propose a knowledge push technology based on applicable probability matching and multidimensional context driving. By building a training sample set, including knowledge description vectors, case feature vectors, and the mapping Boolean matrix, two probability values, application and non-application, were calculated via a Bayesian theorem to describe the matching degree between knowledge and content. The push results were defined by the comparison between two probability values. The hierarchical design content models were built to filter the knowledge in push results. The rules of personalized knowledge push were sorted by multidimensional contexts, which include design knowledge, design context, design content, and the designer. A knowledge push system based on intellectualized design of CNC machine tools was used to confirm the feasibility of the proposed technology in engineering applications.

With the development of communication and control technology, intelligent transportation systems (ITS) have received increasing attention from both industry and academia. However, plenty of studies providing different formulations for ITS depend on Master Control Center and require a high level of hardware configuration. The systematized technologies for distributed architectures are still not explored in detail. In this paper, we proposed a novel distributed cyber–physical system for connected and automated vehicles, and related methodologies are illustrated. Every vehicle in this system is modeled as a double-integrator and supposed to travel along a desired trajectory for maintaining a rigid formation geometry. The desired trajectory is generated by reference leading vehicles using information from multiple sources, while ordinary following vehicles use velocity and position information from their nearest neighbors and sensor information from on-board sensors to correct their own performance. Information graphs are used to illustrate the interaction topology between connected and automated vehicles. Edge computing technology is used to analyze and process information, such that the risk of privacy leaks can be greatly reduced. The performance scaling laws for the network with a one-dimensional information graph are generalized to networks withD-dimensional information graphs, and the results of the experiments show that the performance of the connected and automated vehicles matches very well with analytic predictions. Some design guidelines and open questions are provided for the future study.

This paper studies the linear quadratic Gaussian (LQG) control problem for wireless networked control systems in which control inputs are randomly dropped without the packet acknowledgment The packet acknowledgment is based on a signal scheme between the actuator and the estimator, which make the estimator know whether control packets are dropped or not. For such systems, the calculation of the optimal LQG controller has been shown to be computationally prohibitive and impractical, and sub-optimal solutions are usually obtained under an optimality criteria which is different from that for the optimal LQG problem. An approximate optimal LQG controller is obtained by introducing two approximations in computing the optimal estimation and control. Illustrative examples are provided to verify effectiveness of the proposed design scheme.

The aim of this paper is to develop a fuzzy group programming decision-making method to effectively assess manufacturing cloud. Evidential reasoning approach is applied to aggregate group fuzzy information source provided by organizing group of manufacturing cloud and an agreement result of collaborative decision then can be concluded. Multiple constraints and multiple objective optimization models are constructed for maximization of consumer satisfaction degree, minimization of manufacturing cost and manufacturing service implementation difficulty. Strength pareto evolutionary algorithm is adopted to acquire pareto solutions set. Fuzzy optimal selection method is employed to choose the most feasible solution from pareto solutions set. Finally, a living example related to the manufacturing work of air separation equipment is given to validate the approach we proposed.

The integration of virtual reality and computer-aided design technologies is a revolution in the history of design. Virtual environments provide more information and feedback to designers than traditional desktop systems. Such information and feedback include immersive and stereoscopic visual information, haptic or tactile feedback, and auditory feedback. The multimodal information is in general rather intuitive and inspiring to designers, resulting in globally enhanced insights, creativity and productivity. Moreover, multimodal virtual reality technology excels at the visualization of complex scene and information, which is particularly difficult in desktop systems. In this talk, we present our recent research achievements on: (1) online synthesis of multi-channel visual signals, and the construction of multimodal virtual environments; (2) fusion of graphic, image and video signals in augmented reality environments; (3) synthesis of visual and haptic signals in virtual assembly applications; (4) virtual design and simulation in multimodal virtual environments, including surface deformation based on hand gesture interface, virtual assembly with haptic interactions, virtual maintenance in augmented reality environments; (5) demonstrations and examples.

Virtual assembly and tolerance analysis are effective tools to avoid assembly conflicts between components designed by different CAD teams during collaborative product design. This paper develops a virtual assembly and tolerance analysis system supporting collaborative product design and puts forward a component model expression method based on assembly ports as well as giving approaches to interactive assembly and tolerance analysis in virtual environment, which include recognition of assembly relation based on assembly ports, partial disassembly in assembly procedure, assembly movement navigation, recording of assembly procedure and tolerance information visualization. Besides, the paper also realizes the reusing of virtual assembly procedure to support modification and refinement of components' structure in collaborative product design.

The paper proposes a semantics knowledge modeling approach for product assembly, which is suitable for virtual reality interaction. Assembly semantics knowledge is used to express design conception, constrain the relationship between parts, and encapsulate design knowledge in semantics knowledge modeling. Using assembly semantics, the designer is able to express design intention in an engineer's language, which is natural and convenient for designer. In addition, the intelligence of the system can be promoted through the inference of implicit knowledge of semantics. These methods are implemented in the development of VIRDAS (Virtual Reality Design and Assembly System) and several assembly examples are also given.