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To address the limitations of current grinding robots' path planning for complex curved surface grinding, a novel path planning technology and post-processing method based on NX and Tecnomatix were proposed. Firstly, the equal chord height error algorithm is used to discretize the machining path, and the posture of the path point is confirmed according to the workpiece surface and the grinding path orientation, so as to obtain the complete grinding path position and posture information. Then, according to the pose of the grinding points, the position and posture planning for grinding the complex surface workpiece is carried out using the customized development method with NX. Finally, the virtual robot workstation and actual grinding system is built for experiments on the generated path. The experimental results show that the robot offline path planning technology of combining the NX customized development method and digital twin simulation technology can not only meeting the needs of more refined path planning tasks but also presenting a complete the grinding process of robot workstation based on logic control event-driven. Thus, the overall planning of robot grinding process can be effectively carried out, and the processing effect of robot grinding can be predicted, improving the machining accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Digital twins have attracted more and more attention in the past few years. To put digital twins into practice, a large number of modeling approaches have been proposed, vast amounts of data have been collected, and their accuracy has been improving. However, current research has paid insufficient attention to the multi-scale features of the shop floor, which hinders the effective application of the digital twin shop floor. To address the problem of how to achieve effective multi-level and multi-dimensional fusion of digital twin models with production process data, this paper first proposes a structured data modeling framework for sorting out all the production process data collected in real-time; and then proposes a multi-level fusion framework for supporting the fusion of real-time data and twin models from the unit level to the system level. The method judges the parsed received data streams through the full-factor semanticization framework, and at the same time fuses the parsed data streams with the constructed full-factor twin model from multiple dimensions and layers, forming a twin model fusion method with real-time data streams as the blood and twin model as the skeleton. Finally, the micro-assembly-based production shop environment is selected as a case study to verify the correctness and feasibility of the proposed data grooming framework, data, and model fusion method. [ABSTRACT FROM AUTHOR]

In this work, the authors present a digital twin (DT) to analyse the manufacturing process of structural composites by using resin transfer moulding (RTM). During RTM, a dry textile preform is impregnated with a polymer resin injected in a closed mould. RTM is one of the most used production methods for high-performance structural composites. The DT is focused on detecting in-homogeneous resin flow produced by race-tracking channels that divert resin flow to the outlet gates of the mould, producing dry spots and lack of impregnation. The DT core contains two surrogate models based on encoder/decoder deep learning architectures, providing the fast/accurate response necessary for interrogation during manufacturing. The first surrogate acts as the disturbance detector, providing the on-the-fly representation of the fabric permeability with the only information gathered by a set of five pressure sensors distributed over the mould surface. The second offers real-time representation of a set of quantities of interests (QoI): namely, the flow progress and the pressure field inside the mould. Training of both surrogates was performed with synthetic data generated by high-fidelity multi-physics simulations of the flow progress in a porous preform by following Darcy's law. Errors in the pressure field predictions of the surrogates are lower than 1 % with consultation time <50 ms, enabling encapsulation in the digital twin. The DT performance was evaluated by comparing the response against a set of RTM experiments for different race-tracking scenarios. The two most relevant novelties of the work are the use of the concept of the instantiated DT, which provides information on the current state of the process from data provided by a network of distributed sensors, and that this DT has been trained exclusively with synthetic data from multiphysics simulations while evaluated against experimental data from injection tests. [ABSTRACT FROM AUTHOR]

This paper presents an in situ monitoring method for numerical controlled manufacturing of large conceptual prototype based on multi-view stitching fusion (MSF). With the extensive application of numerical controlled manufacturing, like additive and subtractive manufacturing (ASM), the forming size tends to increase. Nevertheless, limited by optical lens and affiliated machine vision parameters, it is difficult to avoid the existence of blind areas when detecting defects including scratch, crack, pore, and thermal deformation by machine vision. To overcome the obstacles, multi-view information, including visible and depth sequences, were captured during the manufacturing progresses to expand monitoring scope. The overlapped regions of various frames were confirmed by stitching fusion of multi-views with homography matrix. The flaws were detected by superposing anomalous regions of multi-views which were extracted via multi-dimension synthetical anomalous degree map. The finite element analysis (FEA) was conducted to extract susceptible regions and locally adjust threshold. Inspired from digital twin visualization of Boundary representation (B-Rep), the numerical controlled manufacturing process of large textureless conceptual prototype can be quantized and visualized synchronously. The numerical outcomes were evaluated and verified that the MSF was effective in distinguishing wide-range defect regions of in situ large parts, thereby generating feedbacks. The physical experiment was implemented on W12 engine upper block and thin-walled shell of engineering machinery with laser stereo lithography appearance (SLA). The physical experiment prove that it is of great significance for fabricating large-scale high reflective objects with low defect and high efficiency, further for prototype verification of conceptual design. [ABSTRACT FROM AUTHOR]

This study delves into the critical issue of Digital Twin (DT) technology standardization, focusing on introducing a novel approach called "correspondence measure." We draw upon extensive literature review and case study analyses to investigate existing standardization methods, identifying sector-specific and problem-specific strategies. We also underline the importance of interoperability, data privacy and security, real-time synchronization, and accuracy and fidelity in the standardization of DTs. Unveiling the intricacies of the "correspondence measure," we elucidate its potential in enhancing the standardization process by providing a standardized measure of the accuracy and reliability of a digital twin concerning its physical counterpart. Furthermore, we discuss the applications of our proposed method in various sectors such as manufacturing, healthcare, aerospace, maritime and shipping, and city management. Our findings suggest that the "correspondence measure" can significantly contribute to the existing standardization approaches by facilitating a better understanding of the DT's behavior, thereby fostering trust in these digital replications. This paper not only offers a theoretical contribution to the literature on DT standardization but also provides practical insights for the stakeholders involved in developing, implementing, and managing digital twins. [ABSTRACT FROM AUTHOR]

Collaborative tasks in multiple systems have attracted considerable attention in contemporary manufacturing environments. Collaborative robots are representative agents for various collaborative industrial tasks. This study focuses on contactless delivery by considering multiple agents. A contactless delivery operation must be performed with well-synchronized cooperation among the sender, a receiver, and flying dynamics of the deliverable. The most challenging task is to predict the catching point of a thrown package by considering both collaborative robots. Accurate catching-point prediction and relevant operations were achieved using the proposed physics-informed neural network-based hybrid multi-stream deep learning framework. Various cooperative environments were considered using multi-modal input data and manufacturing conditions. The proposed framework incorporates these constraints, and the prediction is guided by flying dynamics and trajectory data. The effectiveness of the proposed framework was demonstrated by implementing multi-agent and related manufacturing environments in a digital twin system. The effectiveness of the proposed framework was proven experimentally using analyses and comparisons with existing machine learning methods. [ABSTRACT FROM AUTHOR]

With the increasing competition of the manufacturing industry, it is essential for manufacturers to develop a new (or next)-generation product based on their prior product design, product user experience, historical performances, etc. The digital twin (DT) is believed to be a suitable technology to support product lifecycle management with excellent data capture and analysis capabilities and product family design capabilities. However, it remains a challenge to synthesize and incorporate the data and information captured from previous generational product development and stored on their individual digital twin instances (DTIs) into next-generation product design. To address this problem, this study proposes a new nominal digital twin (NDT) concept as a collective digital representation of the current generational product digital mockups (DMUs) and all individual DTIs of the built products in services for new-generational product development. NDT is first defined here as a prototypical and synthesized digital twin (or digital representation) of multiple individual digital twins corresponding to multiple physical products in use or previously used. By analyzing the data and information on their DTIs, NDT can enable the establishment and evolution of a more precise approximated model of many related family products used previously or currently in use in various application scenarios and environments in the physical world. This paper also demonstrates how a NDT model can be first established in the product design phase from various digital mockup models and enhanced later with a stochastic forest meta-model based on Bayesian optimization connected to DTIs. With this NDT model, collaborative exploration for optimal design solutions during new-generation product design and improvement can be performed on NDT through multi-objective optimization, which in turn can make new-generation product design easier and quicker. As a primary verification of the feasibility of our proposed approach, a case study has been carried out, and the results have well confirmed that the NDT-based new-generation product design approach is feasible. [ABSTRACT FROM AUTHOR]

Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models. [ABSTRACT FROM AUTHOR]

The smart shop floor is the basic unit to realize intelligent manufacturing. Digital twin (DT) technology is a virtual presentation of the physical world and provides model support for shop floor layout, production scheduling, and production simulation. However, the complex connection and interaction of multiple devices, materials, and information poses a great challenge for the design and production simulation of the smart shop floor, and it is a major difficulty in the application of DT technology to build a multi-scale DT model of shop floor resources to accurately simulate the manufacturing system and guide the actual production process. Inspired by this need, this paper presents a multi-scale, multi-level digital twin modeling method for production resources of shop floor. First, the DT model of production resources is formally represented as four parts: geometric model, physical model, behavioral model, and rule model. Second, for implementing the interaction of different production resources, the connection and interaction mechanism is proposed. Last, taking the DT model of a micro-assembly shop floor as an example, the feasibility of the proposed method is verified, and a prototype system is developed. The implementation of the micro-assembly shop floor application case provides engineers with guidance for building a reliable, reusable DT model of production resources. [ABSTRACT FROM AUTHOR]

The digital twin workshop is a new workshop operation paradigm that enables precise decision-making by fusing virtual and physical space. As a complex manufacturing system, the digital twin model of the workshop must integrate models from different domains in order to provide personalized services. The interoperability of multi-domain models and the dynamic update of parameters become obstacles. In this paper, a knowledge graph (KG)-based multi-domain model integration method for digital twin workshops is proposed. The multi-domain model integration architecture based on KG is consisted of model element, model ontology, model data, semantic integration, and network connection. Then, the KG of multi-domain model for design, manufacturing, and simulation is constructed through ontology modeling and knowledge extraction. On this basis, multi-domain model integration is realized through semantic inference and knowledge query. The model parameters are updated through file exchange during the dynamic simulation. Finally, multiple scenarios in the subassembly workshop for hull construction are used to verify the efficacy of the proposed method. During the assembly and welding of hull parts, the integration of the product model, equipment model, and simulation model is realized, which assists in meeting the service requirements of multiple business scenarios. [ABSTRACT FROM AUTHOR]

In this work, a soft sensor–based digital twin (DT) was developed to reduce the startup time in manufacturing plastic tubes and enable real-time product quality monitoring, i.e., the weight per unit length and the inner and outer diameters of the tube. An experimental campaign was conducted on a real tube extrusion line using three polyvinyl chloride (PVC) compounds and different process conditions, and machine learning regression algorithms were trained and tested to create the models of the extruder and the extrusion die the DT is based on. The characterization of the considered material, whose properties were given as input to the digital models, was carried out according to a procedure based only on the data collected by the production line. The DT was tested for the startup of the production of a single-layer tube and allowed to achieve the specified customer requirements (thickness and weight) in a few minutes. The proposed solution thus proved to be a valuable tool for reducing the setup time, thus increasing the efficiency of the process. [ABSTRACT FROM AUTHOR]

Compared with traditional CNC machines, robot milling has the advantages of low cost, high flexibility, and strong adaptability, providing a new solution for complex surface machining. However, robot machining trajectory planning in the real world is time-consuming and has safety risks. At the same time, how to achieve 3D visualization monitoring in the milling process effectively is also a challenging problem. Digital twin technology, with its characteristics of multi-dimension, high-fidelity, virtual-real fusion, and real-time interaction, provides an effective way to solve these problems. For this purpose, the paper designs and implements a robot milling motion simulation and visualization monitoring system based on the digital twin system framework. The system uses the Unity3D platform to construct the robot's digital twin body, designs a material removal algorithm based on mesh deformation, and establishes a milling motion simulation model. Through virtual-real mapping technology, the system establishes a bidirectional communication between virtual and physical entities and achieves the result mapping of the robot milling motion simulation and the visualization monitoring of the milling process. Finally, the motion simulation and real-time visualization monitoring of the milling process are tested, verifying the effectiveness and timeliness of the system. [ABSTRACT FROM AUTHOR]

Digital twin (DT) technology has become an effective means of industrial digital transformation due to its ability to realize virtual-real mapping, data fusion, etc. In recent years, the application of DT technology to the assembly process of complex mechanical products in multiple fields can effectively overcome the drawbacks of the current assembly method, which is a new strategy for achieving high-precision and high-efficiency intelligent assembly. However, there are few reviews of DT's intelligent assembly technology and application for complex mechanical products. Based on the composition of the DT assembly system, the paper systematically summarizes the critical technologies of DT assembly, including DT model representation of the assembly objects, DT model construction of the assembly process, and DT data collection/management. The application of the DT assembly model based on virtual and real fusion in online monitoring, quality prediction, process planning, real-time control, etc. is comprehensively reviewed. Finally, the challenges faced by DT intelligent assembly of complex mechanical products are described, and future research directions are predicted. [ABSTRACT FROM AUTHOR]

With the enhancement of environmental protection awareness, energy-efficient scheduling has attracted widespread attention. But little consideration is given to process flexibility in the production process. In fact, the integrated process planning and scheduling (IPPS) can further reduce energy consumption. Interference events often occur in production workshops, such as machine failures and new jobs arrival, which affect production operation. Therefore, a new energy-efficient IPPS (EIPPS) method based on digital twin (DT) is proposed in this paper. Firstly, the DT-based EIPPS framework of the intelligent workshop is introduced. Next, an event-based real-time mapping method is proposed to solve the problem of a low degree of virtual-real mapping. Interference events can also be quickly identified. Then, a dynamic EIPPS strategy is proposed to respond to interference events in time. In this strategy, game theory is used to reduce makespan and energy consumption. Finally, a prototype system is designed to verify the effectiveness of the proposed dynamic EIPPS strategy. [ABSTRACT FROM AUTHOR]

Product surface quality which significantly influences a product's wear resilience and fatigue strength is an important technical characteristic. A frequently used indicator of surface quality, precise mating surface fit, and fatigue life is average surface roughness. Unstable machining leads to an unacceptable surface roughness of the workpieces. Effective surface quality control is significant in improving machining efficiency and reducing costs. A method for controlling the quality of product surfaces based on an offline digital twin and artificial intelligence is developed. To reduce average surface roughness, genetic algorithms are used to optimize machining settings. Actual machining data is used to test and validate the digital twin developed. The simulated results agree with the machining trials results and the root means square error is found to be between 0.128 and 0.135 µm. The developed offline digital twin accurately predicts the surface finish and significantly improves manufacturing and production intelligence. [ABSTRACT FROM AUTHOR]

Aerospace, marine energy, and other high-end equipment have a higher and higher demand for performance indicators. The realization of high-performance manufacturing of equipment is particularly critical, and the assembly is the key link. The continuous expansion of application requirements and the continuous improvement of service performance have resulted in increased difficulty and complexity of assembly. Currently, it is a challenge to effectively realize the initial assembly performance of equipment and its long-term stability. The traditional way of assembly is driven by experience and geometric tolerances have certain randomness and uncertainty. These issues result in low assembly efficiency and accuracy and low one-time pass rate. In service, the service performance is inconsistent with the initial assembly performance, resulting in abnormal vibration or failure of equipment. Thus, performance-oriented digital twin (DT) assembly can overcome the shortcomings of traditional assembly under the high assembly performance (HAP) requirements of high-end equipment. The DT can potentially realize the HAP of high-end equipment using real-time mapping and control of physical entities in virtual space through virtual–real interaction, data fusion, iterative optimization, and other means. However, the performance-oriented DT assembly technology lacks systematic research. Thus, this paper expounds on the connotation of performance-oriented DT assembly technology based on the research status of high-end equipment assembly technology. This article presents a technical framework for performance-oriented DT assembly and discusses the realization approach and key technologies of performance-oriented DT assembly. Finally, its research direction and suggestions are given. The research will reveal the urgent engineering problems of performance-driven active and accurate assembly of high-end equipment and provide a reference for improving assembly performance, consistency, and efficiency. [ABSTRACT FROM AUTHOR]

The transformation of manufacturing industry to green manufacturing is one of the important tasks to achieve the carbon peaking and carbon neutrality goals, which needs to improve the use efficiency of unit carbon emission. In order to describe the processing state in real time and improve the accuracy of carbon emission prediction, a dynamic prediction and simulation model of carbon efficiency based on digital twin was proposed. First, the dynamic characteristics of carbon emission during hobbing process was analyzed, and three carbon efficiency targets were defined to assess carbon emissions from processing processes. Then, a dynamic prediction and simulation model of carbon emissions was constructed based on convolutional neural network and dynamic discrete event system specification. On this basis, the framework of the carbon efficiency digital twin (CEDT) of the hobbing process was built, and the dynamic prediction and simulation models were integrated into CEDT as virtual models. The application in hobbing process showed that the presented model has higher accuracy in carbon emission prediction. The root-mean-square error, mean absolute error, and mean absolute percentage error of the real-time power prediction were reduced by 43.98%, 34.55%, and 30.67% on average, compared with the traditional method. Meanwhile, the validity of CEDT was verified and the effect of dynamic parameters on carbon efficiency was discussed. [ABSTRACT FROM AUTHOR]

To achieve timeliness and accuracy when conducting a performance analysis of a complex product assembly process, we consider the performance analysis of an array antenna assembly process, and investigate the measurability of the assembly process' performance. In this study, a digital twin modeling method is proposed for the assembly performance analysis of an array antenna. This method integrates finite elements, mesh model simplification, and surrogate models. To achieve real-time prediction and rapid visualization of the assembly performance of the assembly process, a mesh simplification algorithm for a finite element mesh model based on edge collapse is proposed and a Gaussian process regression surrogate model for product assembly performance is constructed to complete the construction of a digital twin model for assembly performance analysis. Finally, the proposed method is verified by an example of the assembly performance analysis of a typical array antenna. Results show that the digital twin model proposed achieves a high response speed and fidelity in the analysis of assembly performance and provides a valuable reference to application of digital twin technology in product assembly processes. [ABSTRACT FROM AUTHOR]

With the rapid advent of new information technologies (Big Data analytics, cyber-physical systems, such as IoT, cloud computing and artificial intelligence), digital twins are being used more and more in smart manufacturing. Despite the fact that their use in industry has attracted the attention of many practitioners and researchers, there is still a need for an integrated and comprehensive digital twin framework for reconfigurable manufacturing systems. To close this research gap, we present evidence from a systematic literature review, including 76 papers from high-quality journals. This paper presents the current research trends on evaluation and the digital twin in reconfigurable manufacturing systems, highlighting application areas and key methodologies and tools. The originality of this paper lies in its proposal of interesting avenues for future research on the integration of the digital twin in the evaluation of RMS. The benefits of digital twins are multiple such as evaluation of current and future capabilities of an RMS during its life cycle, early discovery of system performance deficiencies and production optimization. The idea is to implement a digital twin that links the virtual and physical environments. Finally, important issues and emerging trends in the literature are highlighted to encourage researchers and practitioners to develop studies in this area that are strongly related to the Industry 4.0 environment. [ABSTRACT FROM AUTHOR]

Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model. [ABSTRACT FROM AUTHOR]

The industrialization of additive manufacturing strongly relies on the robustness of the process. In particular, the more complex Directed Energy Deposition (DED) processes offer great flexibility and higher build-up rates on the one hand, but on the other hand they pose challenges until a part can be printed with the desired properties. This paper adopts and compares different approaches, i.e. linear, sliding mode and model-based controller, for online process regulation of track height and width using a novel industrial sensor setup. Based on single track experiments, control parameters are determined and results of the closed loop analyzed. As a result, the linear controller shows the best performance and robustness. The performance for the track width can even be improved by a Linear Quadratic Gaussian (LQG) controller. Finally, the performance of the linear control strategy is tested with a complex motor cover part. Additionally, a digital twin representation shows the spatial representation of the process parameters and region of interests where thresholds or predefined process rules are violated. [ABSTRACT FROM AUTHOR]

With the development of intelligent factories, collaborative robotic arms are becoming the critical equipment for flexible production lines, and digital twin is the best way to realize intelligent and digitized collaborative robotic arm. This paper designs and develops a collaborative robotic arm monitoring system based on digital twin and proposes a six-dimensional system architecture. Key technologies such as equipment twin modeling for the assembly process, multi-source heterogeneous data acquisition, and digital twin–driven real-time monitoring are studied. The system is driven by twin models and real-time data, and based on the key physical characteristics of the collaborative robotic arm. The model and data are effectively integrated to realize real-time monitoring of the assembly process and provide support for intelligent decision-making. The effectiveness and feasibility of the system are verified by running the application on the UR5 and UR10 collaborative robotic arm assembly test benches. [ABSTRACT FROM AUTHOR]

To improve the accuracy of thermal characteristics analysis of motorized spindle, an online correction model of thermal boundary conditions is proposed based on BP neural network (BPNN), the experimental data and simulation results are used to construct the BPNN model to correct the thermal boundary conditions of motorized spindle. Based on the co-simulation of Ansys, Matlab, and LabVIEW, a digital twin system for thermal characteristics is built to precisely predict the temperature field and thermal deformation of a motorized spindle under varied operating conditions. The experimental results show that the prediction accuracy of temperature field is greater than 98%, and the prediction accuracy of thermal deformation is greater than 96%, which effectively improves the simulation accuracy and robustness of thermal characteristics, and provides the foundation for the error compensation and thermal optimization design. [ABSTRACT FROM AUTHOR]

The aim of this paper is to describe the methods used to adapt the robotic system as well as the design, simulation, digitization, and verification of the robotic workplace for intelligent welding of small-scale production. Small-scale production in small and medium-sized enterprises is characterized by a high level of type variability of products. It was a requirement to design and verify a robotic positioning and welding workplace with a high degree of ability to automatically adapt to processing of various objects. This paper deals with the design and verification of robotic smart systems that contribute to variability of a robotic workplace for intelligent welding of small-scale production such as positioning and holding of the to-be-welded parts by two synchronized robotic manipulators, robotic welding, robotic picking systems using 3D scanners, 2D laser scanner measurement of gap geometry, and quick-change system of robotic grippers with a force-torque sensor. Before testing the robotic manipulation and robotic welding of products of various sizes and shapes, the design of the workplace was verified using its digital twin. The robotic workplace for intelligent welding of small-scale production also includes tools for digitization. [ABSTRACT FROM AUTHOR]

Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing. [ABSTRACT FROM AUTHOR]

Tool wear detection based on machine vision is the core to realize intelligent and automated cutting processing. It is essential to solve the problem of low-efficiency and low-quality acquisition of tool wear image acquisition in the machine detection process. In this paper, the image acquisition method by using a 6-DOF (degree of freedom) serial robot with an industrial camera is developed. The digital twin unit of the detection system is set up, the UR10 robot kinematics model and acquisition trajectory planning method are proposed, and Unity3D software is used to arrange the digital twin environment of the detection system. The synchronous mapping function of the robot digital model and physical entity is given through Socket communication to realize complex tool edges image acquisition trajectory planning, accurate teaching of virtual scenes, digital monitoring of the detection process, and optimization of system models. The stability and efficiency of robot kinematics, trajectory planning, and interactive communication in the tool wear image detection environment are verified. The research results show that the proposed method can provide support for the digital monitoring and control technology of the tool wear on-machine detection process. [ABSTRACT FROM AUTHOR]

The numerical control machine tool is an important part of manufacturing. The speed and accuracy of the machine tool simulation and the monitoring of machine physical model movement, model collision, and operation health are the most common bottlenecks. The previous studies on these problems are discrete and poor in real time. In this paper, combined with the digital twin (DT) technology, a perception-monitor-feedback system architecture is constructed. Based on the improved Gilbert–Johnson–Keerthi (GJK) distance algorithm, the collision information of tool and machine tool can be better detected during the simulation and monitoring of machine tool movement, and the more real workpiece shape can be displayed. Based on the model synchronous motion driven by the production perception data and its potential information extraction, the tool wear online monitoring is carried out. The tool wear online monitoring is performed based on the production perception data–driven model synchronous movement and extraction of its potential information. Finally, the usability and efficiency of the system are verified by an example of a typical shaft part processed by a numerical control machine tool. [ABSTRACT FROM AUTHOR]

A digital twin is a "live" virtual replica of a sensorised component, product, process, human, or system. It accurately copies the entity being modelled by capturing information in real time, or near real time, from the entity, through embedded sensors and the Internet-of-Things. Many applications of digital twins in the manufacturing industry have been investigated. This article focuses on, and contributes to, the development of product digital twins to reduce the impact of quantity, quality, and demand uncertainties in remanufacturing. Starting from issues specific to remanufacturing, the article derives the functional requirements for a product digital twin for remanufacturing and proposes a Unified Modelling Language (UML) model of a generic asset to be remanufactured. The model is used in an example which highlights the need to translate existing knowledge and data into an integrated system to realise a product digital twin, capable of supporting remanufacturing process planning. [ABSTRACT FROM AUTHOR]

This work proposes the development of a tool focused on monitoring simulation-based digital twins (DTs). Although it has been a popular approach in recent years, adopting DT simulation models for decision support in production systems implies some challenges, emphasizing the need for techniques to ensure the accreditation of the models over time. In this case, since the literature does not present alternatives for evaluating DTs models during their operation, this work proposes a tool based on the K-near neighbors (K-NN) classifier and the p control chart. In this way, it is possible to compare the behavior of the physical and virtual environments, monitoring the DT to guarantee its accreditation to support decisions. To assess the applicability of the proposed approach, we applied it to two real study objects with different characteristics, where the tool was able to evaluate the DTs simulation models. As a result, we observed that the proposed tool acted as a supplement to the DTs in order to make them more robust and reliable. Moreover, it was possible to monitor DTs with several evaluation variables in a simple way and with a friendly interface. [ABSTRACT FROM AUTHOR]

Thin-walled parts are widely used in the aerospace industry, where the milling deformation of the parts caused by their thin-walled draping and extremely large size ratio characteristics as well as high material removal rate has been the most common quality issue that greatly affects the assembly performance and operation safety of aerospace equipment. Hence, effective management of milling deformation for a thin-walled part will significantly improve its quality, which, however, is still made difficult by the lack of a real-time deformation perception, optimization, and control method. To bridge this gap, a novel online optimal control method of milling deformation for thin-walled parts is proposed by incorporating digital twins into the milling process of thin-walled parts. To this end, a reference framework of the milling process digital twin (MPDT) for thin-walled parts are designed, where the autonomous operation logic of MPDT for online optimal control of milling deformation is further clarified. On that basis, three key enabling technologies of MPDT are introduced from the perspective of multidimensional high-fidelity MPDT modeling, knowledge-driven low-latency milling deformation simulation, and online optimal control of milling deformation, which provide an insight into the industrial implementation of MPDT. Eventually, a MPDT prototype system is implemented, where its application and evaluation results demonstrate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The aeronautic manufacturing industry has been seeking to enhance competitiveness and product quality by applying the Industry 4.0's technologies. Particularly, on the roadmap of the digital twin era, a way to achieve a reduction in manufacturing time and thus production cost is to obtain prediction models of the main elementary assembly operations and functions within aircraft manufacturing process, such as the clamping force applied by the temporary fasteners on the aircraft's structural parts. Besides being a mandatory operation, it affects multiple tasks along the product's assembly lifecycle. This work focuses on the role of the clamping force in the assembly process, establishing its functional model by means of an experimental approach based upon resources used on a real shop floor of a major aircraft manufacturer. To evince the main requirements that the clamping force tools can achieve, this work employs the Taguchi Design method, design of experiments, and process capability analysis. The model resulted from the aforementioned methods and tools allows the assembly behavior prediction and thus the control of the manufacturing process, ultimately yielding a better geometry quality. [ABSTRACT FROM AUTHOR]

Digital twin is a technology that integrates multi-physical, multi-scale, and multi-disciplinary attributes. At the same time, it has the characteristics of real-time synchronization, faithful mapping, and high fidelity. It can realize the interaction and integration of physical world and information world. In recent years, digital twin technology has attracted the attention of academic and business circles, especially its application. Based on this background, this paper summarizes the concept and evolution of digital twin, studies the application tools and platforms of digital twin, and discusses the relationship between digital twin and related technologies. Then we focus on the application scenarios of digital twin, and summarize the new trends and requirements of digital twin application. The purpose of this paper is to provide reference for the practice of digital twin concept and technology in related fields in the future. [ABSTRACT FROM AUTHOR]

Remanufacturing is the process to restore the functionality of high-value end-of-life (EOL) products, which is considered a substantial link in reverse logistics systems for value recovery. However, due to the uncertainty of the reverse material flow, the planning of a remanufacturing reverse logistics system is complex. Furthermore, the increasing adoption of disruptive technologies in Industry 4.0/5.0, e.g., the Internet of things (IoT), smart robots, cloud-based digital twins, and additive manufacturing, has shown great potential for a smart paradigm transition of remanufacturing reverse logistics operations. In this paper, a new mixed-integer program is modeled for supporting several tactical decisions in remanufacturing reverse logistics, i.e., remanufacturing setups, production planning and inventory levels, core acquisition and transportation, and remanufacturing line balancing and utilization. The model is further extended by incorporating utilization-dependent nonlinear idle time cost constraints and stochastic takt time to accommodate different real-world scenarios. Through a set of numerical experiments, the influences of different demand patterns and idle time constraints are revealed. The potential impacts of disruptive technology adoption in remanufacturing reverse logistics are also discussed from managerial perspectives, which may help remanufacturing companies with a smart and smooth transition in the Industry 4.0/5.0 era. [ABSTRACT FROM AUTHOR]

The formation of undesirable microstructures and defects hinders the widespread use of additive manufacturing, e.g., the formation of columnar grains in Ti–6Al–4 V leads to undesirable anisotropic mechanical properties. Here, we investigate the application of a novel synchronized circular laser array in the powder bed fusion technique to alter the microstructure of printed parts toward the preferable equiaxed grains. This feat is not achievable with the single laser powder bed fusion technique for Ti–6Al–4 V alloy. The temporal temperature distributions for different process parameters (laser power, scanning speed, and internal distance between lasers in the array) were obtained by an anisotropic heat transfer model, and the Hunt criterion was employed to construct the solidification map. The results revealed that a degree of overlap between lasers is recommended to form a coherent melt pool, avoid degeneracy in surface quality, and maintain adequate resolution for all processing windows. However, laser overlap is not required for low scanning speed and high power scenarios. Finally, microstructure prediction shows that 45% of printed track includes equiaxed grains at a high power regime (500 W). However, the volume fraction of the equiaxed microstructure is reduced by decreasing laser power. [ABSTRACT FROM AUTHOR]

Given recent advancements in technology and recognizing the evolution of smart manufacturing, the implementation of digital twins for factories and processes is becoming more common and more useful. Additionally, expansion in connectivity, growth in data storage, and the implementation of the industrial internet of things (IIoT) allow for greater opportunities not only with digital twins but with closed loop analytics. Discrete event simulation (DES) has been used to create digital twins and, in some instances, fitted with live connections to closely monitor factory operations. However, the benefits of a connected digital twin are not easily quantified. Therefore, a test bed demonstration factory was used, which implements smart technologies, to evaluate the effectiveness of a closed-loop digital twin in identifying and reacting to trends in production. This involves a digital twin of a factory process using DES. Although traditional DES is typically modeled using historical data, a DES system was developed which made use of live data to improve predictions. This model had live data updated directly to the DES model without user interaction, creating an adaptive and dynamic model. It was found that this DES with live data typically provided more accurate predictions of future performance and unforeseen near future problems when compared to the predictions of a traditional DES using only historic data, resulting in smarter decisions and implementation of more timely solutions. [ABSTRACT FROM AUTHOR]

A vibration monitoring method of strip rolling mill based on the digital twin model was proposed which could effectively reduce the structure complexity and test cost of the traditional monitoring system. Firstly, the coupling vibration mechanism model of the dynamic characteristics of roll system and bearings of four-high mill was established. Secondly, the digital twin model of strip rolling mill vibration was built based on the mechanism model. At the same time, the traditional monitoring test of the rolling mill was carried out to verify the accuracy of the digital twin model. Through analyzing the experimental results, it could be found that the roll system is vibrating intensely with the increase of rolling force obvious fluctuation. Finally, based on the above research results, the corresponding measures were put forward to suppress the rolling mill vibration. In conclusion, the rolling mill vibration monitoring theory based on the digital twin model provided the technical support and theoretical basis for improving equipment operation stability and reducing testing costs. [ABSTRACT FROM AUTHOR]

As a highly secure and reliable system, aviation faces challenges including digital transformation, high production operation costs, low maintenance efficiency, and intensive technology. One of the most beneficial technologies is the digital twin (DT), which can solve the above issues. This paper investigates aviation industry DT from government authorities, industry, and academia. Firstly, it surveys the development of the definition of DT and compares its concepts with the Internet of Things, cyber-physical systems, and digital thread etc. Next, it shows the research timeline of aviation DT in the past 10 years by authorities such as Air Force Research Laboratory and National Aeronautics and Space Administration. Then, this paper reviews the state-of-the-art research status of DT in the whole lifecycle of the aviation system and concludes that aviation DT is the most widely used in manufacturing and maintenance, and should pay attention to the application of UAV DT. Finally, it summarizes that data fusion, high-fidelity modeling, integration with New IT, and human–machine interaction are the key technologies of aviation DT. The future possible research directions could be digital process twin risk control, interactive DT, and DT data learning. [ABSTRACT FROM AUTHOR]

The potential benefits of Industry 4.0 have led to an increased interest in smart manufacturing. To facilitate the self-diagnosis and adaptive ability in smart milling system, a digital twin–driven intelligent algorithm for monitoring in-process milling parameters is proposed here. The algorithm can extract the radial width of cut, axial depth of cut, cutter runout parameters, and cutting constants in the end milling process at the same time only by using force sensor. It is an important breakthrough in this paper to converge two different force models to realize cyber-physical fusion for identifying milling parameters in the milling process. By using the convolution force model, digital twin technology can extract the approximate solution of milling parameters in the machining process in advance, so as to narrow the range of solution. Furthermore, the subsequent artificial intelligence algorithm can find the accurate solution of the current milling parameters in a short calculation time by cyber-physical fusion with the numerical force model considering cutter runout effect. Milling experiments are carried out to validate the proposed algorithm. It is shown that due to the complementary advantages of the convolution force model and numerical force model, the algorithm proposed in this paper can give consider to the identification accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

This paper develops an approach to improve a CNC machine's tool performance and slow down its degradation rate automatically in the Pre-Failure stage. A Deep Reinforcement Learning (DRL) agent is developed to optimize the machining process performance online during the Pre-Failure interval of the tool's life. The Pre-Failure agent that is presented in the proposed approach tunes the feed rate according to the optimal policy that is learned in order to slow down the tool's degradation rate, while maintaining an acceptable Material Removal Rate (MRR) level. The machine learning techniques and pattern recognitions are implemented to monitor and detect the tool's potential failure level. The proposed mechanism is applied to a CNC machine when turning Titanium Metal Matrix Composites (TiMMC). A CNC machine Digital Twin (DT) is developed to emulate the physical machine in the digital environment. It is validated with the physical machine's measurements. The proposed pre-failure mechanism is a model-free approach, which can be implemented in any machining process with fewer online computational efforts. It also validated on a wide range of cutting speeds, up to 15,000 RPM. Deployment of the proposed machine learning approach for the particular case study improves the tool's Time to Failure (T2F) by 40% and the MRR by 6%, on average, compared to the classical approach. [ABSTRACT FROM AUTHOR]

Digital Twin technology has been widely applied in various industry domains. Modern industrial systems are highly complex consisting of multiple interrelated systems, subsystems and components. During the lifecycle of an industrial system, multiple digital twin models might be created related to different domains and lifecycle phases. The integration of these relevant models is crucial for creating higher-level intelligent systems. The Cognitive Digital Twin (CDT) concept has been proposed to address this challenge by empowering digital twins with augmented semantic capabilities. It aims at identifying the dynamics and interrelationships of virtual models, thus to enhance complexity management capability and to support decision-making during the entire system lifecycle. This paper aims to explore the CDT concept and its core elements following a systems engineering approach. A conceptual architecture is designed according to the ISO 42010 standard to support CDT development; and an application framework enabled by knowledge graph is provided to guide the CDT applications. In addition, an enabling tool-chain is proposed corresponding to the framework to facilitate the implementation of CDT. Finally, a case study is conducted, based on simulation experiments as a proof-of-concept. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor periodicity of workshop material distribution, difficult prediction of station material demand time node, and redundant distribution route, this paper proposes a model construction method of material distribution system based on digital twin. Build a material distribution control mode based on digital twin, and establish a full-cycle material distribution mechanism on this basis to comprehensively optimize the distribution cycle from the material preparation stage, dynamic replenishment stage, and data transmission stage of adjacent distribution cycles. Build the digital twin model of material distribution system, establish the material demand time node dynamic prediction operation mechanism based on LSTM, accurately predict the station material demand time node, establish the material distribution route optimization model with the lowest total cost, and optimize the AGV route. Finally, it is applied to the asynchronous line workshop of A enterprise to verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data, Internet of Things, and other technologies, digital twin has gained great attention and become a current research topic. Using digital twin technology, the digital twin model can be constructed in the cyber space that is fully equivalent to the physical entity. It is always consistent with the physical entity in the operation process, which greatly improves the dynamic perception and prediction ability of the real world. After the development in recent years, digital twin has gradually changed from the initial concept discussion to the study of model framework and implementation method. However, because the research objects in different industries have great differences in their own composition, service conditions, and application scenarios, they have personalized characteristics in modeling strategies and usage methods. Therefore, based on different industries, this paper reviews the current articles on digital twins and distinguishes the focus of digital twin modeling research; subsequently, the relevant supporting techniques and methods are summarized according to their different importance for digital twin modeling. Based on the review in this paper, future researchers can conduct targeted research on digital twin technology in term of the characteristics of the objects in their industry. [ABSTRACT FROM AUTHOR]

Digital twin (DT) is a key enabling technology to realize cyber-physical system (CPS), which can truly perceive, map, and predict the operating state of physical entity. Through analyzing the generality of the testing process of large and complex equipment components (LCEC), a five-dimensional framework of DT-based test process management (DTTPM) is proposed, which comprises physical layer, network layer, data layer, model layer, and service layer. In order to realize the visualization and enhance the controllability, security, and information transparency of LCEC in the testing process, three key technologies are elaborated in detail as follows: (1) the construction of the twin semantic model of the testing process in model layer, (2) the synchronization method of twin model and physical entity based on real-time data, (3) and human–computer interaction–based visual monitoring of authenticity and safety coordination. Through the case of the vibration test for the crowned blade of a steam turbine by blade tip-timing measurement, the feasibility and flexibility of DTTPM are demonstrated. [ABSTRACT FROM AUTHOR]

Manufacturing organisations must compete with each other while adapting to the ever-changing conditions by building and strengthening their chains of competencies to survive. Therefore, companies are challenged to reform and reconstruct their product, process, and system models as well as to define new goals conforming to evolving complex and dynamic environments. Recent advancements in technologies such as modelling and simulation (M&S), digital twin (DT) and virtual reality (VR) promise new ways for remodelling organisations' resources, processes, and architectures. Moreover, comprehensive concepts like DT-based virtual factory (VF) exploit the potential for utilising such technological concepts in the application domain by enabling the integration of various tools, methods, and processes. There are a variety of empirical studies focusing either on the distinct use of technologies, methods and processes or very generic concepts and approaches. However, studies focusing on both conceptual and practical aspects for such comprehensive and integrated solutions to handle co-evolution in the complex manufacturing domain are limited for defining, designing and utilising novel technologies. In this paper, therefore, we attempt to close this gap by (1) framing and discussing the conceptual and theoretical foundations of DT-based VF, (2) introducing and discussing the extension of the DT-based VF to virtual enterprise and (3) generalising and interpreting the prescriptive knowledge discovered during the previous VF demonstrations performed at Vestas Wind Systems A/S. Systems and complexity theories, concepts of business cycles and competence-based strategic management are discussed to frame descriptive knowledge as a language for depicting the internal and external nature of complex manufacturing enterprise operations. Furthermore, design principles of the DT-based VF concept are examined based on framed concepts and theories as well as its potential implications and deviations into different application contexts to provide managerial guidelines for utilising such a concept. [ABSTRACT FROM AUTHOR]

Additive manufacturing is often used in rapid prototyping and manufacturing, allowing the creation of lighter, more complex designs that are difficult or too expensive to build using traditional manufacturing methods. This work considers the implementation of a novel digital twin ecosystem that can be used for testing, process monitoring, and remote management of an additive manufacturing–fused deposition modeling machine in a simulated virtual environment. The digital twin ecosystem is comprised of two approaches. One approach is data-driven by an open-source 3D printer web controller application that is used to capture its status and key parameters. The other approach is data-driven by externally mounted sensors to approximate the actual behavior of the 3D printer and achieve accurate synchronization between the physical and virtual 3D printers. We evaluate the sensor-data-driven approach against the web controller approach, which is considered to be the ground truth. We achieve near-real-time synchronization between the physical machine and its digital counterpart and have validated the digital twin in terms of position, temperature, and run duration. Our digital twin ecosystem is cost-efficient, reliable, replicable, and hence can be utilized to provide legacy equipment with digital twin capabilities, collect historical data, and generate analytics. [ABSTRACT FROM AUTHOR]

Faced with the global crisis of COVID-19 and the strong increase in customer demands, competition is becoming more intense between companies, on the one hand, and supply chains on the other. This competition has led to the development of new strategies to manage demand and increase market share. Among these strategies are the growing interest in sustainable manufacturing and the need for customizable products that create an increasingly complex manufacturing environment. Sustainable manufacturing and the need for customizable products create an environment of increased competition and constant change. Indeed, companies are trying to establish more flexible and agile manufacturing systems through several systems of reconfiguration. Reconfiguration contributes to an extension of the manufacturing system's life cycle by modifying its physical, organizational and IT characteristics according to the changing market conditions. Due to the rapid development of new information technology (such as IoT, Big Data analytics, cyber-physical systems, cloud computing and artificial intelligence), digital twins have become intensively used in smart manufacturing. This paper proposes a digital twin design and simulation model for reconfigurable manufacturing systems (RMSs). [ABSTRACT FROM AUTHOR]

In the abrasive grinding hardening (AGH) of metal materials, the microstructure of the surface changes under the action of the intense grinding heat and forms the evolution layer covering the grinding surface with unique mechanical properties. The effective control of microstructure in the layer has great practical significance for obtaining high-performance surfaces. However, the formation mechanism of evolution layer is not well revealed and few quantitative investigations on microstructure phase content and morphology, which hinders the development of advanced grinding technology and surface performance characterization methods. A novel digital twin model is proposed; the spatial–temporal distribution of grinding heat and the evolution of microstructure along the surface depth direction have been reproduced from the microscale. The results reveal the dynamic effect of grinding parameters on phase content and morphology of the evolution layer. Furthermore, the quantitative relationship between microstructure and surface macroscopic hardening characteristics has been explored, and a novel method is proposed to predict the surface hardness. This research enhances the understanding of the formation mechanisms of grinding hardened surface and is significant to realize effective control and accurate prediction of surface performance. [ABSTRACT FROM AUTHOR]

Bill of material (BOM), as the core data in the complete life cycle management of complex products, is the primary data that supports the deep integration of intelligent and information technology of complex products. They are aiming at the characteristics of high complexity, solid dynamics, and many uncertain factors in the BOM reconstruction process of complex products. The digital twin technology is introduced into the BOM reconstruction process, and the BOM consistency reconstruction mechanism of complex products is constructed. The sequence process of BOM data flow is analyzed. The focus is on the reconstruction process of engineering BOM (EBOM), process BOM (PBOM), manufacturing BOM (MBOM), and maintenance BOM (WBOM) based on digital twin. The rationality of the constructed BOM is verified by establishing a virtual model, which promotes continuous optimization and improvement of the BOM. This paper also discusses the key technologies of data dynamic perception, digital threading, simulation modeling decoupling, and coupling in the process of BOM reconstruction. Finally, the feasibility of the proposed method is verified by combining the BOM reconstruction process of an enterprise bogie and the construction of the bogie twin system, which provides a reference for improving the accuracy, integrity, and consistency of the BOM reconstruction process of complex products. [ABSTRACT FROM AUTHOR]

The success of machining process automation hinges primarily on the effectiveness of the monitoring and adaptive control systems. A new digital twin–based process monitoring method and system in small batch machining is presented, and the cutting model is integrated into the monitoring method to improve the diagnosis accuracy. Model-based and signal-based monitoring indicators are developed, and indicators of the residual force component for the tool wear monitoring and energy ratio for the chatter detection are introduced to the digital twin–based monitoring. The critical monitoring algorithm is verified in two cases: tool wear monitoring and chatter detection. The results show that the method proposed can accurately evaluate the percentages of the residual useful life of the tool and the chatter. Moreover, a new process monitoring system in small batch machining is developed by integrating the advanced algorithm. This study can provide a critical reference for intelligent machining monitoring and control in industry. [ABSTRACT FROM AUTHOR]

The change of size, surface roughness, residual stress, and so on profoundly influence the final machining quality of complex mechanical products. Digital twin machining technology can ensure machining quality by observing the machining process in real time. However, the current digital twin systems mainly adopt the display method of virtual-real separation. It leads to transmitting the useful processing information to the on-site technicians ineffectively, limiting the digital twin system to help field processing. The monitoring technology on the machining process by augmented reality based on the digital twin machining system is proposed to deal with this problem. Firstly, the augmented reality dynamic multi-view is constructed based on multi-source heterogeneous data. Secondly, the augmented reality is integrated into the real-time monitoring of the intermediate process of complex products to promote cooperation among the operators and the digital twin machining system. It can avoid irreparable errors when the finished product is nearly completed. Finally, the effectiveness and feasibility of the proposed method will be verified by a monitoring application case. [ABSTRACT FROM AUTHOR]