Showing papers in "Robotics and Computer-integrated Manufacturing in 2020"

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Abstract: This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.

System architecture for blockchain based transparency of supply chain social sustainability

Abstract: Social sustainability is a major concern in global supply chains for protecting workers from exploitation and for providing a safe working environment. Although there are stipulated standards to govern supply chain social sustainability, it is not uncommon to hear of businesses being reported for noncompliance issues. Even reputable firms such as Unilever have been criticized for production labor exploitation. Consumers now increasingly expect sellers to disclose information on social sustainability, but sellers are confronted with the challenge of traceability in their multi-tier global supply chains. Blockchain offers a promising future to achieve instant traceability in supply chain social sustainability. This study develops a system architecture that integrates the use of blockchain, internet-of-things (IoT) and big data analytics to allow sellers to monitor their supply chain social sustainability efficiently and effectively. System implementation cost and potential challenges are analyzed before the research is concluded.

A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin

Abstract: As a typical manufacturing equipment, CNC machine tool (CNCMT) is the mother machine of industry. Fault of CNCMT might cause the loss of precision and affect the production if troubleshooting is not timely. Therefore, the reliability of CNCMT has a big significance. Predictive maintenance is an effective method to avoid faults and casualties. Due to less consideration of the status variety and consistency of CNCMT in its life cycle, current methods cannot achieve accurate, timely and intelligent results. To realize reliable predictive maintenance of CNCMT, a hybrid approach driven by Digital Twin (DT) is studied. This approach is DT model-based and DT data-driven hybrid. With the proposed framework, a hybrid predictive maintenance algorithm based on DT model and DT data is researched. At last, a case study on cutting tool life prediction is conducted. The result shows that the proposed method is feasible and more accurate than single approach.

Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model

Abstract: Increasing individualization demands in products call for high flexibility in the manufacturing systems to adapt changes. This paper proposes a novel digital twin-driven approach for rapid reconfiguration of automated manufacturing systems. The digital twin comprises two parts, the semi-physical simulation that maps data of the system and provides input data to the second part, which is optimization. The results of the optimization part are fed back to the semi-physical simulation for verification. Open-architecture machine tool (OAMT) is defined and developed as a new class of machine tools comprising a fixed standard platform and various individualized modules that can be added and rapidly swapped. Engineers can flexibly reconfigure the manufacturing system for catering to process planning by integrating personalized modules into its OAMTs. Key enabling techniques, including how to twin cyber and physical system and how to quickly bi-level program the production capacity and functionality of manufacturing systems to adapt rapid changes of products, are detailed. A physical implementation is conducted to verify the effectiveness of the proposed approach to achieving improved system performance while minimizing the overheads of the reconfiguration process by automating and rapidly optimizing it.

Manufacturing big data ecosystem: A systematic literature review

Abstract: Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to real-time big data analytics and cybersecurity.

Robotic grinding of complex components: A step towards efficient and intelligent machining – challenges, solutions, and applications

Abstract: Robotic grinding is considered as an alternative towards the efficient and intelligent machining of complex components by virtue of its flexibility, intelligence and cost efficiency, particularly in comparison with the current mainstream manufacturing modes. The advances in robotic grinding during the past one to two decades present two extremes: one aims to solve the problem of precision machining of small-scale complex surfaces, the other emphasizes on the efficient machining of large-scale complex structures. To achieve efficient and intelligent grinding of these two different types of complex components, researchers have attempted to conquer key technologies and develop relevant machining system. The aim of this paper is to present a systematic, critical, and comprehensively review of all aspects of robotic grinding of complex components, especially focusing on three research objectives. For the first research objective, the problems and challenges arising out of robotic grinding of complex components are identified from three aspects of accuracy control, compliance control and cooperative control, and their impact on the machined workpiece geometrical accuracy, surface integrity and machining efficiency are also identified. For the second aim of this review, the relevant research work in the field of robotic grinding till the date are organized, and the various strategies and alternative solutions to overcome the challenges are provided. The research perspectives are concentrated primarily on the high-precision online measurement, grinding allowance control, constant contact force control, and surface integrity from robotic grinding, thereby potentially constructing the integration of “measurement – manipulation – machining” for the robotic grinding system. For the third objective, typical applications of this research work to implement successful robotic grinding of turbine blades and large-scale complex structures are discussed. Some research interests for future work to promote robotic grinding of complex components towards more intelligent and efficient in practical applications are also suggested.

Industrial blockchain based framework for product lifecycle management in industry 4.0

Abstract: Product lifecycle management (PLM) aims to seamlessly manage all products and information and knowledge generated throughout the product lifecycle for achieving business competitiveness. Conventionally, PLM is implemented based on standalone and centralized systems provided by software vendors. The information of PLM is hardly to be integrated and shared among the cooperating parties. It is difficult to meet the requirements of the openness, interoperability and decentralization of the Industry 4.0 era. To address these challenges, this paper proposed an industrial blockchain-based PLM framework to facilitate the data exchange and service sharing in the product lifecycle. Firstly, we proposed the concept of industrial blockchain as the use of blockchain technology in the industry with the integration of IoT, M2M, and efficient consensus algorithms. It provided an open but secured information storage and exchange platform for the multiple stakeholders to achieve the openness, interoperability and decentralization in era of industry 4.0. Secondly, we proposed and developed customized blockchain information service to fulfill the connection between a single node with the blockchain network. As a middleware, it can not only process the multi-source and heterogeneous data from varied stages in the product lifecycle, but also broadcast the processed data to the blockchain network. Moreover, smart contract is used to automate the alert services in the product lifecycles. Finally, we illustrated the blockchain-based application between the cooperating partners in four emerging product lifecycle stages, including co-design and co-creation, quick and accurate tracking and tracing, proactive maintenance, and regulated recycling. A simulation experiment demonstrated the effectiveness and efficiency of the proposed framework. The results showed that the proposed framework is scalable and efficient, and hence it is feasible to be adopted in industry. With the successful development of the proposed platform, it is promising to provide an effective PLM for improving interoperability and cooperation between stakeholders in the entire product lifecycle.

AR-based interaction for human-robot collaborative manufacturing

Abstract: Industrial standards define safety requirements for Human-Robot Collaboration (HRC) in industrial manufacturing. The standards particularly require real-time monitoring and securing of the minimum protective distance between a robot and an operator. This paper proposes a depth-sensor based model for workspace monitoring and an interactive Augmented Reality (AR) User Interface (UI) for safe HRC. The AR UI is implemented on two different hardware: a projector-mirror setup and a wearable AR gear (HoloLens). The workspace model and UIs are evaluated in a realistic diesel engine assembly task. The AR-based interactive UIs provide 21–24% and 57–64% reduction in the task completion and robot idle time, respectively, as compared to a baseline without interaction and workspace sharing. However, user experience assessment reveal that HoloLens based AR is not yet suitable for industrial manufacturing while the projector-mirror setup shows clear improvements in safety and work ergonomics.

Web-based digital twin modeling and remote control of cyber-physical production systems

Abstract: The rapid development new generation of information technologies facilitate the emergence of cyber-physical production system (CPPS) which could pave a way to exploring new smart manufacturing solutions. Digital twin (DT) is the technical core for establishing CPPS in the context of industry 4.0. Developing an easy-to-deploy and simple-to-use DT-based CPPS is a critical research gap. In this paper, a systemic framework is proposed to provide guidelines for rapid system configuration and easy runtime of DT-based CPPS by integrating CPS, DT modeling technologies, event-driven distributed cooperation mechanisms, and web technologies. The concept of CPS node (CPSN) for manufacturing resources is established by integrating semantic information model, 3D geometric model and function modules. Various CPSNs are orchestrated as an autonomous CPPS using dynamic resource registration and binding technologies. To achieve easy runtime of DT-based CPPS, event-driven distributed cooperation among CPSNs and web-based remote control of CPPS are proposed respectively. Finally, to verify the feasibility of the proposed framework, a prototype of DT-based CPPS is implemented, based on which an exemplary case is conducted.

Real-time supply chain-A blockchain architecture for project deliveries

Abstract: Real-time tracking and tracing are important in providing a unified view of global supply chains consisting of several parties. This paper illustrates the needs and requirements for managing supply chains in multi-company project environments by adopting various tracking and tracing technologies. This kind of tracking and tracing is especially needed within distributed architectures engaged in project-based businesses, where several vendors are involved in a single project. Such tracking and tracing data can be used extensively to generate key performance indicators, which can be used to measure and control supply chain processes. This paper also proposes a pilot system of a cloud-based portal for real-time tracking and tracing of logistics and supply chains. This portal is formed by the combination of RFID, IoT and blockchain technology into an integrated real-time view. RFID (Radio Frequency Identification) and IoT (Internet of Things) provides real-time information or data, while blockchain technology is used to provide a chain of immutable transactions. The architecture of the proposed portal system is connected to transport companies, tracking devices, consolidation points and suppliers. The pilot study also illustrates the benefits and advantages of such a portal system.

DT-II:Digital twin enhanced Industrial Internet reference framework towards smart manufacturing

Abstract: In this paper, the interplay and relationship between digital twin and Industrial Internet are discussed at first. The sensing/transmission network capability, which is one of the main characteristics of Industrial Internet, can be a carrier for providing digital twin with a means of data acquisition and transmission. Conversely, with the capability of high-fidelity virtual modeling and simulation computing/analysis, digital twin evolving from lifecycle management for a single product to application in production/manufacturing in the shop-floor/enterprise, can further greatly enhance the simulation computing and analysis of Industrial Internet. This paper proposes a digital twin enhanced Industrial Internet (DT-II) reference framework towards smart manufacturing. To further illustrate the reference framework, the implementation and operation mechanism of DT-II is discussed from three perspectives, including product lifecycle level, intra-enterprise level and inter-enterprise level. Finally, steam turbine is taken as an example to illustrate the application scenes from above three perspectives under the circumstance of DT-II. The differences between with and without DT-II for design and development of steam turbine are also presented.

A digital twin-driven approach for the assembly-commissioning of high precision products

Abstract: High precision products (HPPs) with multidisciplinary coupling are widely used in aerospace, marine, chemical and other fields. Since the internal structure of HPPs is complex and compact, the assembly process requires high precision and involves multidisciplinary coupling. Traditional assembly process of HPPs is based on manual experience, which results in low assembly efficiency and poor-quality consistency. Given the above problems, this research proposes a digital twin-driven assembly-commissioning approach for HPPs. Firstly, this paper introduces the theoretical framework of digital twin-driven assembly-commissioning. Secondly, we introduce the construction method of assembly-commissioning total factor information model based on digital twin technology; the fusion method of twin data and the interoperability method between digital twin models; in addition, the assembliability prediction and assembly-commissioning process optimization methods. Finally, a case study product is used to verify the effectiveness and feasibility of the proposed method.

A semi-supervised convolutional neural network-based method for steel surface defect recognition

Abstract: Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods focus on supervised learning, which relies on large-scale labeled samples. In some real-world cases, it is difficult to collect and label enough samples for model training, and this might impede the application of most current works. The semi-supervised learning, using both labeled and unlabeled samples for model training, can overcome this problem well. In this paper, a semi-supervised learning method using the convolutional neural network (CNN) is proposed for steel surface defect recognition. The proposed method requires fewer labeled samples, and the unlabeled data can be used to help training. And, the CNN is improved by Pseudo-Label. The experimental results on a benchmark dataset of steel surface defect recognition indicate that the proposed method can achieve good performances with limited labeled data, which achieves an accuracy of 90.7% with 17.53% improvement. Furthermore, the proposed method has been applied to a real-world case from a Chinese steel company, and obtains an accuracy of 86.72% which significantly better than the original method in this workshop.

Augmented reality-assisted robot programming system for industrial applications

Abstract: Robots are important in high-mix low-volume manufacturing because of their versatility and repeatability in performing manufacturing tasks. However, robots have not been widely used due to cumbersome programming effort and lack of operator skill. One significant factor prohibiting the widespread application of robots by small and medium enterprises (SMEs) is the high cost and necessary skill of programming and re-programming robots to perform diverse tasks. This paper discusses an Augmented Reality (AR) assisted robot programming system (ARRPS) that provides faster and more intuitive robot programming than conventional techniques. ARRPS is designed to allow users with little robot programming knowledge to program tasks for a serial robot. The system transforms the work cell of a serial industrial robot into an AR environment. With an AR user interface and a handheld pointer for interaction, users are free to move around the work cell to define 3D points and paths for the real robot to follow. Sensor data and algorithms are used for robot motion planning, collision detection and plan validation. The proposed approach enables fast and intuitive robotic path and task programming, and allows users to focus only on the definition of tasks. The implementation of this AR-assisted robot system is presented, and specific methods to enhance the performance of the users in carrying out robot programming using this system are highlighted.

Human-robot coexistence and interaction in open industrial cells

Abstract: Recent research results on human–robot interaction and collaborative robotics are leaving behind the traditional paradigm of robots living in a separated space inside safety cages, allowing humans and robot to work together for completing an increasing number of complex industrial tasks. In this context, safety of the human operator is a main concern. In this paper, we present a framework for ensuring human safety in a robotic cell that allows human–robot coexistence and dependable interaction. The framework is based on a layered control architecture that exploits an effective algorithm for online monitoring of relative human–robot distance using depth sensors. This method allows to modify in real time the robot behavior depending on the user position, without limiting the operative robot workspace in a too conservative way. In order to guarantee redundancy and diversity at the safety level, additional certified laser scanners monitor human–robot proximity in the cell and safe communication protocols and logical units are used for the smooth integration with an industrial software for safe low-level robot control. The implemented concept includes a smart human-machine interface to support in-process collaborative activities and for a contactless interaction with gesture recognition of operator commands. Coexistence and interaction are illustrated and tested in an industrial cell, in which a robot moves a tool that measures the quality of a polished metallic part while the operator performs a close evaluation of the same workpiece.

A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment

Abstract: Smart manufacturing, as an emerging manufacturing paradigm, leverages massive in-context data from manufacturing systems for intelligent decision makings. In such context, Cyber-Physical Systems (CPS) play a key role in digitizing manufacturing systems and integrating multiple systems together for collaborative works. Amongst different levels of smartness and connectedness of CPS, Digital Twin (DT), as an exact digital copy of a physical object or system including its properties and relationship with the environment, has a significant impact on realizing smart manufacturing. A DT constantly synchronizes with its physical system and provides real-time high-fidelity simulations of the system and offers ubiquitous control over the system. Despite its great advantages, few works have been discussed about DT reference models, let alone a generic manner to establish it for smart manufacturing. Aiming to fill the gap, this research introduces a generic CPS system architecture for DT establishment in smart manufacturing with a novel tri-model-based approach (i.e. digital model, computational model and graph-based model) for product-level DT development. The tri-model works concurrently to simulate real-world physical behaviour and characteristics of the digital model. To validate the proposed architecture and approach, a case study of an open source 3D printer DT establishment is further conducted. Conclusions and future works are also highlighted to provide insightful knowledge to both academia and industries at last.

A collaborative architecture of the industrial internet platform for manufacturing systems

Abstract: One of the most significant advances in the development of intelligent manufacturing is represented by the industrial Internet, which is combining the physical and cyber components in manufacturing systems together. Aiming to manage the interaction between the physical and cyber components, this paper proposes a collaborative architecture for industrial Internet platform (IIP) called industrial operation system (Ind-OS), which contains the industrial driver, digital thread and micro-services to provide a better cooperative enterprise information system (EIS) environment for manufacturing systems. The industrial driver in the edge layer is presented for each resource unit for communication, computation, control, identification, insight and interoperation, realizing a ``plug and play" fashion in IIP. The digital thread is designed to link all resource units and meta-data, which consists of a digital resource chain and an integrated information chain. In the application layer, all the businesses in EIS are decoupled into different micro-services, and a “Jigsaw Apps” is recombined to support the operation of manufacturing systems. A case study illustrates the effectiveness of the proposed Ind-OS, and the impacts caused by the Ind-OS are discussed.

Modelling the dynamics of industrial robots for milling operations

Abstract: Using industrial robots as machine tools is targeted by many industries for their lower cost and larger workspace. Nevertheless, performance of industrial robots is limited due to their mechanical structure involving rotational joints with a lower stiffness. As a consequence, vibration instabilities, known as chatter, are more likely to appear in industrial robots than in conventional machine tools. Commonly, chatter is avoided by using stability lobe diagrams to determine the stable combinations of axial depth of cut and spindle speed. Although the computation of stability lobes in conventional machine tools is a well-studied subject, developing them in robotic milling is challenging because of the lack of accurate multi-body dynamics models involving joint compliance able of predicting the posture-dependent dynamics of the robot. In this paper, two multi-body dynamics models of articulated industrial robots suitable for machining applications are presented. The link and rotor inertias along with the joint stiffness and damping parameters of the developed models are identified using a combination of multiple-input multiple-output identification approach, computer-aided design model of the robot, and experimental modal analysis. The performance of the developed models in predicting posture-dependent dynamics of a KUKA KR90 R3100 robotic arm is studied experimentally.

In-process tool condition forecasting based on a deep learning method

Abstract: It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing.

A proactive material handling method for CPS enabled shop-floor

Abstract: Cyber physical system (CPS) enables companies to keep high traceability and controllability in manufacturing for better quality and improved productivity. However, several challenges including excessively long waiting time and a serious waste of energy still exist on the shop-floor where limited buffer exists for each machine (e.g., shop-floor that manufactures large-size products). The production logistics tasks are released after work-in-processes (WIPs) are processed, and the machines will be occupied before trolleys arrival when using passive material handling strategy. To address this issue, a proactive material handling method for CPS enabled shop-floor (CPS-PMH) is proposed. Firstly, the manufacturing resources (machines and trolleys) are made smart by applying CPS technologies so that they are able to sense, act, interact and behave within a smart environment. Secondly, a shop-floor digital twin model is created, aiming to reflect their status just like real-life objects, and key production performance indicators can be analysed timely. Then, a time-weighted multiple linear regression method (TWMLR) is proposed to forecast the remaining processing time of WIPs. A proactive material handling model is designed to allocate smart trolleys optimally. Finally, a case study from Southern China is used to validate the proposed method and results show that the proposed CPS-PMH can largely reduce the total non-value-added energy consumption of manufacturing resources and optimize the routes of smart trolleys.

A soft gripper with variable stiffness inspired by pangolin scales, toothed pneumatic actuator and autonomous controller

Abstract: Aiming at combining compliant covering and rigid lifting to the object grasping, this paper presents the design principle of a variable stiffness soft gripper and carries out its structural design, gripper fabrication and controller development. The proposed soft finger is composed of a variable stiffness layer and a pneumatic driven layer. The variable stiffness layer is inspired by the pangolins whose scales are flexible in daily activities and become tough when being threatened by predators. A toothed pneumatic actuator is designed to supply power with increased stiffness. The three-finger soft gripper is fabricated by 3D printing and molding of super elastic material. The tests for verifying grasping capability and variable stiffness are implemented. Experimental results show that the gripper can grasp a large variety of objects and achieve enhanced stiffness. The stiffness of the gripper is more than twice higher than the pneumatic gripper without variable stiffness structure. Finally, the control system for autonomous grasping is developed. The control block is divided into the actuation layer, information processing layer and user interface layer. According to the grasping process, the feedback signals in the information processing layer are collected by sensors. A safe grasping assessment is added to the control scheme for changing the gripper stiffness autonomously, which differs from the traditional soft gripper controller. The proposed soft gripper has variable stiffness, enhanced pneumatic input, autonomous control system. Therefore, it has great potential to be applied in the unstructured environment for effective, adaptable and safe object grasping.

Blockchain-enabled logistics finance execution platform for capital-constrained E-commerce retail

Abstract: As one of the most prevailing retail channels, E-commerce has nowadays facilitated retailers to sell goods to customers worldwide and tremendously increased the supply chain efficiency by removing most intermediate links. The broaden business scope and accelerated goods circulation, nevertheless, have generally led to capital shortages for retailers, especially small and medium enterprises (SMEs). Given the SMEs’ difficulty in acquiring capital from financial institutions such as banks, logistics finance (LF) has emerged as an alternative, which is the combination of logistics and financial service. However, the frequent order fulfillment and diversity of pledges in E-commerce hinder SMEs in meeting LF's financing requirements. Furthermore, the current LF relies more on large and reputable third-party logistics (3PLs) to alleviate financing risks, which in turn raises the entry threshold for other 3PLs. Hence, this paper has proposed a blockchain-enabled logistics finance execution platform (BcLFEP) as an integrated solution to facilitate LF for E-commerce retail. A cross-layered architecture is proposed to organize and manage involved resources, workflows and decisions based on the object-oriented methodology (OOM). A hybrid finite state machine-based smart contract (HFSM-SC) is designed to associate and coordinate with all kinds of agents for LF operations throughout its lifecycle. Moreover, blockchain is integrated with agent technology to construct a blockchain-enabled multi-agent system (BcMAS), providing a trusted runtime environment to more autonomously and efficiently execute smart contract. Finally, a case study is conducted to implement BcLFEP-enabled dynamic pledge management for verification and evaluation.

Deep learning-based smart task assistance in wearable augmented reality

Abstract: Wearable augmented reality (AR) smart glasses have been utilized in various applications such as training, maintenance, and collaboration. However, most previous research on wearable AR technology did not effectively supported situation-aware task assistance because of AR marker-based static visualization and registration. In this study, a smart and user-centric task assistance method is proposed, which combines deep learning-based object detection and instance segmentation with wearable AR technology to provide more effective visual guidance with less cognitive load. In particular, instance segmentation using the Mask R-CNN and markerless AR are combined to overlay the 3D spatial mapping of an actual object onto its surrounding real environment. In addition, 3D spatial information with instance segmentation is used to provide 3D task guidance and navigation, which helps the user to more easily identify and understand physical objects while moving around in the physical environment. Furthermore, 2.5D or 3D replicas support the 3D annotation and collaboration between different workers without predefined 3D models. Therefore, the user can perform more realistic manufacturing tasks in dynamic environments. To verify the usability and usefulness of the proposed method, we performed quantitative and qualitative analyses by conducting two user studies: 1) matching a virtual object to a real object in a real environment, and 2) performing a realistic task, that is, the maintenance and inspection of a 3D printer. We also implemented several viable applications supporting task assistance using the proposed deep learning-based task assistance in wearable AR.

Digital twin-enabled Graduation Intelligent Manufacturing System for fixed-position assembly islands

Abstract: The layout of fixed-position assembly islands is widely used in the heavy equipment industry, where the product remains at one assembly island for its entire assembly period, while required workers, equipment, and materials are moved to the island according to the assembly plan. Such layout is not only suitable for producing bulky or fragile products, but also offers considerable flexibility and competitive operational efficiency for products with medium variety and volumes. However, due to inherent complexity of the product, sophisticated assembly operations heavily rely on skilled operators, and the complexity and uncertainty are high and amplified by such massive manual interventions as well as the unique routing patterns of the fixed-position assembly process. Aiming at reducing the complexity and uncertainty, this paper introduces a digital twin-enabled Graduation Intelligent Manufacturing System (DT-GiMS) for fixed-position assembly islands. Inspired by the success of graduation ceremony, an assembly system-Graduation Manufacturing System (GMS) is proposed for fixed-position assembly islands, in which job tickets, setup tickets, operation tickets, and logistics tickets are designed to organize the production activities. Following the concept of digital twin, unified digital representations with appropriate sets of information are created at object level, product level, and system level, respectively. Through Internet of Things (IoT), smart gateway, Web 3D and industrial wearable technologies, vital information including identity, status, geometric model, and production process can be captured and mapped in physical space, and converged and synchronized with their digital representations in twin (cloud) space on a real-time basis. The overall framework of DT-GiMS is presented with physical layer, digital layer, and service layer. Real-time convergence and synchronization among them ensure that right resources are allocated and utilized to the right activities at the right time with enhanced visibility. Considering customer demand and production capacity constraints, real-time ticket pool management mechanisms are proposed to manage production activities in a near-optimal way under DT-GiMS. With the support of cloud-based services provided in service layer in DT-GiMS, managers could easily make production decisions, and onsite operators could efficiently complete their daily tasks with nearly error-free operations with enhanced visibility. A demonstrative case is carried out to verify the effectiveness of the proposed concept and approach.

Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing

Abstract: Remanufacturing helps to improve the resource utilization rate and reduce the manufacturing cost. Disassembly is a key step of remanufacturing and is always finished by either manual labor or robots. Manual disassembly has low efficiency and high labor cost while robotic disassembly is not flexible enough to handle complex disassembly tasks. Therefore, human-robot collaboration for disassembly (HRCD) is proposed to flexibly and efficiently finish the disassembly process in remanufacturing. Before the execution of the disassembly process, disassembly sequence planning (DSP), which is to find the optimal disassembly sequence, helps to improve the disassembly efficiency. In this paper, DSP for human-robot collaboration (HRC) is solved by the modified discrete Bees algorithm based on Pareto (MDBA-Pareto). Firstly, the disassembly model is built to generate feasible disassembly sequences. Then, the disassembly tasks are classified according to the disassembly difficulty. Afterward, the solutions of DSP for HRC are generated and evaluated. To minimize the disassembly time, disassembly cost and disassembly difficulty, MDBA-Pareto is proposed to search the optimal solutions. Based on a simplified computer case, case studies are conducted to verify the proposed method. The results show the proposed method can solve DSP for HRC in remanufacturing and outperforms the other three optimization algorithms in solution quality.

A robust weld seam recognition method under heavy noise based on structured-light vision

Abstract: Structured-light vision systems are widely used in robotic welding. The key to improving the robotic visual servo performance and weld quality is the weld seam recognition accuracy. Common detection algorithms are likely to be disturbed by the noise of spatter and arc during the welding process. In this paper, a weld seam recognition algorithm is proposed based on structured light vision to overcome this challenge. The core of this method is fully utilizing information of previous frames to process the current frame, which can make weld seam extraction both more robust and effective. The algorithm can be divided into three steps: initial laser center line recognition, online laser center line detection, and weld feature extraction. A Laplacian of Gaussian filter is used for recognizing the laser center line in the first frame. Afterwards, an algorithm based on the NURBS-snake model detects the laser center line online in a dynamic region of interest (abbreviated ROI). The center line obtained from first step is set as the initial contour of the NURBS-snake model. Using the line obtained from the previous step, feature points are determined by segmentation and straight-line fitting, while the position of the weld seam can be calculated according to the feature points. The accuracy, efficiency and robustness of the recognition algorithm are verified by experiments.

A novel robotic grasp detection method based on region proposal networks

Abstract: Grasp detection based on deep learning is an important method for robots to accurately perceive unstructured environments. However, the deep learning method widely used in general object detection is not suitable for robotic grasp detection. Multi-stage network is often designed to meet the requirements of grasp posture, but they increase computation complexity. This paper proposes a single-stage robotic grasp detection method by using region proposal networks. The proposed method generates multiple oriented reference anchors firstly. The grasp rectangles are then regressed and classified based on these anchors. A new matching strategy for oriented anchors is proposed based on the rotation angles and center positions of the anchors. The well-known Cornell grasp dataset and Jacquard dataset are used to test the performance of the proposed method. Experimental results show that the proposed method can achieve higher grasp detection accuracy compared with other methods in the literature.

An adaptive trajectory planning algorithm for robotic belt grinding of blade leading and trailing edges based on material removal profile model

Abstract: Robotic belt grinding of the leading and trailing edges of complex blades is considered to be a challenging task, since the microscopic material removal mechanism is complicated due to the flexible contact state accompanied with greatly varying curvature that finally affects the machined profile accuracy. The resulting poor accuracy of blade edges, to a great extent, is attributed to the trajectory planning method which less considers the dynamics. In this paper, an iso-scallop height algorithm based on the material removal profile (MRP) model is developed to plan the tool paths by taking into consideration the elastic deformation at contact wheel-workpiece interface. An improved constant chord-height error method considering the influence of elastic deformation is then proposed to adaptively plan the grinding points according to the curvature change characteristics of the free-form surface. Based on these two steps, a MRP model based adaptive trajectory planning algorithm is constructed to enhance the profile accuracy facing the robotic belt grinding operation. Simulation and experimental results demonstrate the effectiveness of the proposed trajectory planning algorithm for the robotic belt grinding of blades from the perspectives of surface roughness, profile accuracy and processing efficiency. Particularly this technology serves to solve the problem of over-cutting at the blade leading and trailing edges.

Collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem using improved discrete Bees algorithm in remanufacturing

Abstract: Remanufacturing helps to reduce manufacturing cost and environmental pollution by reusing end-of-life products. Disassembly is an inevitable process of remanufacturing and it is always finished by manual labor which is high cost and low efficiency while robotic disassembly helps to cover these shortages. Before the execution of disassembly, well-designed disassembly sequence and disassembly line balancing solution help to improve disassembly efficiency. However, most of the research used for disassembly sequence planning and disassembly line balancing problem is only applicable to manual disassembly. Also, disassembly sequence planning and disassembly line balancing problem are separately studied. In this paper, an improved discrete Bees algorithm is developed to solve the collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem. Robotic workstation assignment method is used to generate robotic disassembly line solutions based on feasible disassembly solutions obtained by the space interference matrices. Optimization objectives of the collaborative optimization problem are described, and the analytic network process is used to assign suitable weights to different indicators. With the help of variable neighborhood search, an improved discrete Bees algorithm is developed to find the optimal solution. Finally, based on a gear pump and a camera, case studies are used to verify the effectiveness of the proposed method. The results under different cycle time of robotic disassembly line are analyzed. Under the best cycle time, the performance of the improved discrete Bees algorithm under different populations and iterations are analyzed and compared with the other three optimization algorithms. The results under different assessment methods and scenarios are also analyzed.

A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing

Abstract: Assembly stations are important hubs that connect massive material, information, human labor, etc. The fixed-position assembly systems for complex products may deal with hundreds of thousands of processes, making them vulnerable to manufacturing exceptions. Many scheduling problems were described and solved in the past decades, however, the gap between theoretical models and industrial practices still exist. To achieve a practical method for the dynamic scheduling in case of exceptions while reducing the impact brought by the exceptions, an Intelligent Collaborative Mechanism (ICM) was proposed where negotiations on resource configuration may happen among tasks (i.e. assembly processes). The intercommunication among resources was guaranteed by the data-driven ICM framework. The Petri-net-based workflow analysis and the constraint matrix can pick out the tasks that are currently not bound by other ones. The dynamic priority of the processes was defined and obtained using grey relational analysis. The matching strategy among the selected tasks and operators can provide a scheduling plan that is close to the initial plan, so the assembly systems may remain effective even when exceptions occur. The proposed models were analyzed in a case scenario, where the impact brought by exceptions can decrease by 44.3% in terms of the operators’ utilization rate, and by 60.26% in terms of the assembly time. This research has provided a practical strategy to improve the flexibility and effectiveness of assembly systems for complex products.