Pipe manufacturing plays a pivotal role in ship construction, thus improving the production efficiency of the ship pipe shops contributes to accelerating ship building. Multi-variety and small-batch pipes require setup time for job switching and the multi-production lines environment leads to difficulties in formulating a production plan. Therefore, the flexible job shop group scheduling problem with multi-production lines (FJGSP-MPL) is studied, which mainly considers arranging various pipes constrained by pipe families in multiple different production lines. The mathematical model of FJSGP-MPL is established, and an improved memetic algorithm (IMA) based on opposition-based learning (OBL) and adaptive local search is proposed. Experimental results indicate that the introduced IMA algorithm has superior overall effectiveness in solving the problem studied in this paper.

As an important part of intelligent manufacturing, robotic mixed-model assembly line needs to cost-effectively adjust its configuration to meet the dynamical manufacturing requirements. However, the existing reconfiguration method always considers the configurations of equipment in cyber space and ignores the interconnections between the physical and cyber spaces. Digital twin provides alternative ways to realize the interconnections between the physical and cyber spaces. In this paper, digital twin model of robotic mixed-model assembly line and the interconnections between physical and cyber spaces are realized by means of digital twin model. In addition, the mathematical model for minimizing the reconfiguration cost and load balancing is built, then the virtual reconfiguration problem is proposed. Afterwards, adaptive neighborhood search Bees algorithm, of which the adaptive neighborhood search strategy is included, is utilized to solve the proposed problem. Finally, the effectiveness of the proposed method is verified and the results show adaptive neighborhood search Bees algorithm generates better solutions compared with the other optimization algorithms.Note to Practitioners—The existing reconfiguration method is always realized in the cyber space and rarely integrates the physical industrial robot. Motivated by this, this paper combines the physical industrial robot with digital twin model of robotic mixed-model assembly line, and uses adaptive neighborhood search Bees algorithm to solve the reconfiguration problem. Based on the digital twin model of robotic mixed-model assembly line, the optimal solution of virtual reconfiguration method could be obtained considering the dynamic manufacturing requirements. Afterwards, the optimal solution can be sent to the physical robotic assembly line through the interconnection between the physical and cyber spaces. In addition, experiments under different cases show that the proposed method could generate the optimal solution when the manufacturing requirements change. In the future, the physical manufacturing process based on robotic mixed-model assembly line will be studied to make the proposed method more applicable.

Disassembly is an inevitable process of recycling end-of-life products and robotic disassembly sequence planning could improve disassembly efficiency. However, the missing condition of component is uncertain and it could not be pre-known before execution of disassembly process. The optimal solution should be dynamically generated according to the recognized condition during disassembly process. In this article, digital twin is utilized to solve robotic disassembly sequence dynamic planning under uncertain missing condition. First, the framework of the proposed method is studied and digital twin of robotic disassembly process is established. Afterwards, deepQ-learning network is utilized to solve the proposed problem. Finally, case studies are carried out to verify the effectiveness of proposed method. The results show the converged deepQ-learning network model could dynamically find the optimal solutions after the missing condition of component is recognized during disassembly process using less running time, compared with the other meta-heuristics algorithms.

In automated assembly tasks, ellipse detection is usually applied in the vision-based pose estimation of circular workpieces. However, the existing ellipse detection methods cannot effectively solve severe visual occlusion in cluttered environments. To address this problem, this paper proposes a Generative Adversarial Networks (GAN)-supported ellipse detection method against the occlusion condition. The trained GAN network can restore the occluded image of workpieces, so that the elliptical features can be detected robustly. In the experiments with different degrees of occlusion, the ellipse detection rate is above 90%, which shows better performance than other existing methods.

With the development of smart manufacturing, robotic assembly technology in automatic production line has greatly improved production efficiency and product quality. In assembly process, dynamic disturbances such as advance delivery and changes in processing time may occur, which requires rescheduling of robotic assembly line (RAL). Traditional scheduling methods are not sufficient to meet the real-time and adaptive requirements. Due to digital twin’s characteristics of virtual reality interaction, real-time mapping and iterative optimization, the digital twin-based assembly planning (DTAP) model is established and the task rescheduling strategy for RAL is proposed. The virtual RAL is the real reflection of the physical RAL. When the RAL is dynamically disturbed, based on the virtual entity of RAL and the adaptive discrete bees algorithm (ADBA), the assembly task assignment is optimized and fed back to the physical RAL. Mapping and interaction between the physical and virtual RAL achieves the precise scheduling and execution of assembly tasks and improves the assembly efficiency. Finally, the case study based on bearing fitting is implemented to verify the effectiveness of the proposed method.

Remanufacturing provides an alternative way to realize natural resources saving and environment protection. Disassembly, as a key step in remanufacturing, has been attracted much attention in recent years. To make up for the deficiency of manual disassembly which is low efficiency and high cost, industry robots have great advantages in handling large volume and repeatable disassembly activities. Besides, proper disassembly sequence planning helps to improve the disassembly efficiency. In this paper, the framework of robotic disassembly sequence planning using deep reinforcement learning (DRL) is proposed to solve robotic disassembly sequence planning (RDSP) problem. Considering the smoothness of starting and stopping in robotic movement, dynamic moving speed model is built for moving time in disassembly. Firstly, a disassembly precedence matrix (DPM) is constructed according to the structure of disassembly products. After that, RDSP is modeled as Markov decision process and the state, action and reward of the agent in DRL environment are designed. The deep reinforcement learning network model is trained to obtain the optimal disassembly sequence in RDSP. Finally, case study based on double coupling shaft with 21 components proves that the DRL algorithm used in this paper can obtain a disassembly sequence for better performance compared with other two meta-heuristic methods.

While self-attention has been successfully applied in a variety of natural language processing and computer vision tasks, its application in Monte Carlo (MC) image denoising has not yet been well explored. This paper presents a self-attention based MC denoising deep learning network based on the fact that self-attention is essentially non-local means filtering in the embedding space which makes it inherently very suitable for the denoising task. Particularly, we modify the standard self-attention mechanism to an auxiliary feature guided self-attention that considers the by-products (e.g., auxiliary feature buffers) of the MC rendering process. As a critical prerequisite to fully exploit the performance of self-attention, we design a multi-scale feature extraction stage, which provides a rich set of raw features for the later self-attention module. As self-attention poses a high computational complexity, we describe several ways that accelerate it. Ablation experiments validate the necessity and effectiveness of the above design choices. Comparison experiments show that the proposed self-attention based MC denoising method outperforms the current state-of-the-art methods.

Remanufacturing has enormous economic and environmental benefits in terms of resource conservation and environmental protection. Disassembly, as an essential step in remanufacturing, is always manually executed, it has the disadvantages of high labor intensive, time consuming and low efficiency while robotic disassembly can cover the shortages of manual disassembly. During the robotic disassembly process, considering the structure and movement characteristics of the industrial robot, the industrial robot need to perform collision avoidance movements considering the obstacle caused by the End-of-Life (EoL) product. The moving time considering the robotic collision avoidance trajectory is a non-negligible part of total disassembly time. In this paper, robotic disassembly sequence planning (RDSP) considering robotic collision avoidance trajectory is proposed. This method is used to obtain the collision avoidance trajectory and the moving time between different disassembly points by the robotic collision avoidance model established in this paper. Afterwards, an optimized discrete bee algorithm (ODBA) is used to generate the optimal disassembly sequence to minimize the total disassembly time. Finally, case studies based on a gear pump verify the effectiveness of proposed methods.

The industrial cloud robotics (ICR) has the characteristics of intelligence, reliability, and scalability. In the smart manufacturing environment, ICR can be encapsulated as services through virtualization and servilization technology, enabling the rapid matching of personalized manufacturing capabilities and services for end users. However, the manufacturing resources are physically isolated and the physical workshop environment is vulnerable to dynamic disturbances, which reduces manufacturing system performance. In this context, taking the cycle time into consideration, the manufacturing service scheduling model for ICR is established and the digital twin (DT) enhanced scheduling optimization mechanism is proposed. When disturbances occur, the digital twin platform interacts with the cloud layer and physical workshop to analyze multi-source data in order to monitor the manufacturing environment in real time and optimize the production efficiency. Meanwhile, the manufacturing service scheduling based on an improved discrete differential evolution (IDDE) algorithm is proposed, in which the adaptive mutation and crossover operator and double mutation strategies are applied to converge to the optimal scheduling sequence. Finally, the case study is implemented to verify the proposed mechanism shows better performance compared with the existing optimization algorithms.

In smart manufacturing, the introduction of mobile industrial robot has facilitated efficient and flexible production. Mobile industrial robot has the capability of following human operators to coordinate with them to complete complicated operations. In this context, following target person robustly is a significant prerequisite for mobile robot offering assistance. However, the mobile robot with limited field of view may lose the dynamic target during following, which easily leads to following failure. In this paper, therefore, an anticipative human following approach is adopted. This method is appropriate for omnidirectional mobile industrial robot equipped with a visual sensor with limited perception range. We propose a novel Interacting Multiple Model-based adaptive trajectory prediction algorithm. The algorithm integrates two physics-based motion models and adaptively adjusts model parameters for increasing accuracy of prediction. Based on the prediction, mobile robot plans its path and configuration during following in advance to avoid obstacles and achieve robust following. Comparison results demonstrate that the proposed approach can predict human trajectory more accurately, and reduce the deviation between the human and the center of view of the robot.

Recently, tremendous utilization of wireless networks has led to sever scarcity of radio spectrum resources. TV White Spaces (TVWSs) as novel bands enabling Cognitive Radio (CR) technology to improve spectrum resources utilization, have attracted significant standardisation efforts such as IEEE 802.11af, IEEE 802.16h and 802.22. As these heterogeneous networks may operate on the same channels of TVWSs, network coexistence problem cannot be avoided and is particularly challenging given the heterogeneous MAC/PHY layer protocols and operation parameters (e.g., tx power) employed in coexisting networks. In this paper, we develop a coexistence mechanism called ecological Species Competition based HEterogeneous networks coexistence MEchanism (SCHEME). Inspired by ecology based species competition model, SCHEME uses an ecological spectrum allocation method to assign available spectrums. Through both theoretical and simulation analysis, we demonstrate that SCHEME can achieve stable and fair spectrum allocation among coexisting networks.

Faults in mechanical equipment could cause breakdown of time-critical production systems, which is very expensive in terms of production losses and re-commissioning costs. In cloud manufacturing, the scattered distribution of mechanical equipment and fault diagnosis resources, such as experts and specialist instruments, etc., could hinder the development of fault diagnosis systems. The idea of resource servitisation, aimed at resource sharing and collaboration, will lead fault diagnosis systems toward integration, low cost and high efficiency. This paper focuses on the servitisation of fault diagnosis for mechanical equipment in cloud manufacturing. A new service-oriented fault diagnosis system framework for mechanical equipment is proposed, together with a new servitisation method of fault diagnosis for mechanical equipment. Moreover, enabling technologies, e.g. XML, Web Services Definition Language (WSDL), Axis2, are also analysed. Finally, a prototype system is presented that demonstrates the feasibility and effectiveness of the developed architecture and servitisation method in a cloud manufacturing environment.

Cyber-physical system (CPS) is a multi-dimensional complex system in which physical world operations are monitored and controlled using the communication and computing components, and they interact with each other in order to achieve a global optimization of such system operation. Dynamic response to the emergencies in a public environment not only requires the perception capability that obtains such useful information, but also needs the optimum operation methods to handling them. With the tight integration of communication, computing and control, a CPS can be used to address the aforementioned goals. In this paper, a framework of the CPS for public environment perception and emergency handling is developed, and its requirements to each component in the framework are analyzed in detail. Furthermore, a task allocation mechanism and a hybrid path planning approach are also presented for a set of robots in the CPS to handle uncertain emergencies collaboratively. Finally, we demonstrate the system using a case study in which mobile robots are equipped with multiple sensors to handle fires with different cases in a scenario of public environment, and the simulation results show the effectiveness of the proposed approaches and the feasibility of such system.

The popularity of networks in manufacturing is continuously growing, and the quality of service (QoS) has become one of the most intriguing aspects for both the theory and practice of manufacturing networks. The concept of QoS for manufacturing networks is discussed, together with the classification and modeling of the QoS issues. Then, this paper presents a QoS-aware service framework integrating the QoS mechanisms from both the MGrid technology and communication networks, so that provides an integrated QoS guarantee for manufacturing systems under network environment. In order to evaluate the efficacy of the framework, a prototype implementation have been performed and analyzed. The analysis results demonstrate that the framework, QASF-MNet, is able to effectively satisfy the various performance requirements posted by the applications of manufacturing networks.