Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Gonzalo Mejía, Jordi PereiraIn this work, we focus on general multi-objective scheduling problems that can be modeled using a Petri net framework. Due to their generality, Petri nets are a useful abstraction that captures multiple characteristics of real-life processes.To provide a general solution procedure for the abstraction, we propose three alternative approaches using an indirect scheme to represent the solution: (1) a genetic algorithm that combines two objectives through a weighted fitness function, (2) a non dominated sorting genetic algorithm (NSGA-II) that explicitly addresses the multi-objective nature of the problem and (3) a multi-objective local search approach that simultaneously explores multiple candidate solutions. These algorithms are tested in an extensive computational experiment showing the applicability of this general framework to obtain quality solutions.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Chiara Cimini, Fabiana Pirola, Roberto Pinto, Sergio CavalieriAbstractIn recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Tao Li, Helen Lockett, Craig LawsonAbstractThe assembly line process planning connects product design and manufacturing through translating design information to assembly integration sequence. The assembly integration sequence defines the aircraft system components installation and test precedence of an assembly process. This activity is part of the complex systems integration and verification process from a systems engineering view. In this paper, the complexity of modern aircraft is defined by classifying aircraft system interactions in terms of energy flow, information data, control signals and physical connections. At the early conceptual design phase of assembly line planning, the priority task is to understand these product complexities, and generate the installation and test sequence that satisfies the designed system function and meet design requirements. This research proposes a novel method for initial assembly process planning that accounts for both physical and functional integrations. The method defines aircraft system interactions by using systems engineering concepts based on traceable RFLP (Requirement, Functional, Logical and Physical) models and generate the assembly integration sequence through a structured approach. The proposed method is implemented in an industrial software environment, and tested in a case study. The result shows the feasibility and potential benefits of the proposed method.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Shuo Huang, Jun Yang, Min XieAbstractJoint design of SPM (Statistical Process Monitoring) and maintenance strategies has evolved as a popular research topic in industrial engineering. Most existing works only consider location shifts of a process but neglect the effects of scale shifts. Besides, traditional SPM schemes are usually employed based on the single-sampling plan, which might be insensitive to detect quality shifts and perform uneconomically from the cost-saving perspective. To overcome the drawbacks mentioned above, this paper proposes a double-sampling SPM scheme for simultaneously monitoring of location and scale shifts, and then develop a more realistic and effective model for the joint design of SPM and maintenance strategies. First, we propose a new SPM scheme with a double-sampling plan for simultaneously monitoring location and scale shifts. The comparison results indicate that our proposed DS (double-sampling) scheme has a faster detection speed than the single-sampling one for different combination of shifts. Therefore, we combine our proposed SPM scheme with two widely used maintenance strategies: corrective maintenance and prevent maintenance, and then construct a new cost model under the constraints of sample size, operation time and ARL (average run length). A real case study is implemented to verify the effectiveness of the proposed method. Results demonstrate that the proposed model is more economical than the traditional method.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Xiaohui Chen, Youjun An, Zhiyao Zhang, Yinghe LiAbstractWith the development of intelligent manufacturing, production scheduling and preventive maintenance are widely applied in industry to enhance production efficiency and machine reliability. Therefore, according to the different processing states and the physical degradation phenomena of the machine, this paper proposes an accurate maintenance (AM) model based on reliability intervals, which have different maintenance activities in diverse intervals and overcome the shortcoming of the single reliability threshold maintenance model used in the past. Combining the flexible job-shop scheduling problem (FJSP), an integrated multiobjective optimization model is established with production scheduling and accurate maintenance. To strengthen the ability of the evolutionary algorithm to solve the presented model/problem, we propose a novel genetic algorithm, named the approximate nondominated sorting genetic algorithm III (ANSGA-III), which is inspired by NSGA-III. To improve the performance of the Pareto dominance principle, the local search, the elite storage for the original algorithm, the approximate dominance principle, the variable neighborhood search, and the elite preservation strategy are proposed. Then, we employ a scheduling example to verify and evaluate the availability of the above three improved operations and the proposed algorithm. Next, we compare ANSGA-III against five recently proposed algorithms, representing the state-of-the-art on similar problems. Finally, we apply ANSGA-III to solve the integrated optimization model, and the results reveal that the machine can maintain higher availability and reliability when compared to other models in our experiments. Consequently, the superiority of the proposed model based on accurate maintenance of reliability intervals is demonstrated, and the optimal reliability threshold between the yellow and red areas is found to be 0.82.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Csaba Kardos, András Kovács, József VánczaAbstractThe importance of computer-aided process planning (CAPP) for assembly is widely recognized, as it holds the promise of efficient and automated construction of solutions for a complex, geometrically, technologically, and economically constrained planning problem. This complexity led to the introduction of decomposition approaches, separating the macro-level planning problem that oversees the complete assembly process from the various micro-level problems that look into the details of individual assembly operations. The paper introduces a constraint model for solving the macro-level assembly planning problem based on a generic feature-based representation of the product and the assembly operations involved. Special attention is given to capturing the feedback from micro-level planners expressed in the form of feasibility cuts, and hence, to the integration of the approach into a complete CAPP workflow. Results on three case studies from different industries are also presented to illustrate the practical applicability of the approach.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Fangwei Ning, Yan Shi, Maolin Cai, Weiqing Xu, Xianzhi ZhangAbstractIn the era of the mass customisation, rapid and accurate estimation of the manufacturing cost of different parts can improve the competitiveness of a product. Owing to the ever-changing functions, complex structure, and unusual complex processing links of the parts, the regression-model cost estimation method has difficulty establishing a complex mapping relationship in manufacturing. As a newly emerging technology, deep-learning methods have the ability to learn complex mapping relationships and high-level data features from a large number of data automatically. In this paper, two-dimensional (2D) and three-dimensional (3D) convolutional neural network (CNN) training images and voxel data methods for a cost estimation of a manufacturing process are proposed. Furthermore, the effects of different voxel resolutions, fine-tuning methods, and data volumes of the training CNN are investigated. It was found that compared to 2D CNN, 3D CNN exhibits excellent performance regarding the regression problem of a cost estimation and achieves a high application value.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Amin Abedini, Wei Li, Fazleena Badurdeen, I.S. JawahirAbstractProduction scheduling involves operational level decision making at the shop floor that covers not only the manufacturing stage of the product life-cycle, but also the use stage of the processes. Triple bottom line (TBL) including economic, environmental, and social pillars has been introduced to holistically evaluate the performance of a production firm. Despite the substantial research in sustainable manufacturing, a holistic model that considers all three pillars of the TBL for sustainable production scheduling is virtually absent. This paper presents a metric-based model to systematically and holistically evaluate the sustainability of the production schedules. To this aim, we first perform an extensive literature review to identify the fundamental performance metrics in production scheduling. Second, we assess those metrics with respect to the TBL. Third, we show the inconsistencies among the fundamental performance metrics, and consequently among the objectives defined in the TBL. Finally, we propose a generic model for production scheduling for sustainability based on balancing the trade-offs among the inconsistent objectives. The efficiency and effectiveness of the proposed model is demonstrated using a comprehensive numerical study. The proposed model not only provides a sustainable schedule, but also results in better control over the fundamental performance metrics of the production scheduling.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): M. De Ryck, M. Versteyhe, F. DebrouwereAbstractAutomated guided vehicles (AGVs) form a large and important part of the logistic transport systems in today's industry. They are used on a large scale, especially in Europe, for over a decade. Current employed AGV systems and current systems offered by global manufacturers almost all operate under a form of centralized control: one central controller controls the whole fleet of AGVs. The authors do see a trend towards decentralized systems where AGVs make individual decisions favoring flexibility, robustness, and scalability of transportation. Promoted by the paradigm shift of Industry 4.0 and future requirements, more research is conducted towards the decentralization of AGV-systems in academia while global leading manufacturers start to take an active interest. That said, this implementation seems still in infancy. Currently, literature is dominated by central as well as by decentral control techniques and algorithms. For researchers in the field and for AGV developers, it is hard to find structure in the growing amount of algorithms for various types of applications. This paper is, to this purpose, meant to provide a good overview of all AGV-related control algorithms and techniques. Not only those that were used in the early stages of AGVs, but also the algorithms and techniques used in the most recent AGV-systems, as well as the algorithms and techniques with high potential.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Radhya Sahal, John G. Breslin, Muhammad Intizar AliAbstractIndustry 4.0 is considered to be the fourth industrial revolution introducing a new paradigm of digital, autonomous, and decentralized control for manufacturing systems. Two key objectives for Industry 4.0 applications are to guarantee maximum uptime throughout the production chain and to increase productivity while reducing production cost. As the data-driven economy evolves, enterprises have started to utilize big data techniques to achieve these objectives. Big data and IoT technologies are playing a pivotal role in building data-oriented applications such as predictive maintenance.In this paper, we use a systematic methodology to review the strengths and weaknesses of existing open-source technologies for big data and stream processing to establish their usage for Industry 4.0 use cases. We identified a set of requirements for the two selected use cases of predictive maintenance in the areas of rail transportation and wind energy. We conducted a breadth-first mapping of predictive maintenance use-case requirements to the capabilities of big data streaming technologies focusing on open-source tools. Based on our research, we propose some optimal combinations of open-source big data technologies for our selected use cases.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Amir Ghasemi, Radhia Azzouz, Georg Laipple, Kamil Erkan Kabak, Cathal HeaveyAbstractIn this paper, we advance the state of the art for capacity allocation and scheduling models in a semiconductor manufacturing front-end fab (SMFF). In SMFF, a photolithography process is typically considered as a bottleneck resource. Since SMFF operational planning is highly complex (re-entrant flows, high number of jobs, etc.), there is only limited research on assignment and scheduling models and their effectiveness in a photolitography toolset. We address this gap by: (1) proposing a new mixed integer linear programming (MILP) model for capacity allocation problem in a photolithography area (CAPPA) with maximum machine loads minimized, subject to machine process capability, machine dedication and maximum reticles sharing constraints, (2) solving the model using CPLEX and proofing its complexity, and (3) presenting an improved genetic algorithm (GA) named improved reference group GA (IRGGA) biased to solve CAPPA efficiently by improving the generation of the initial population. We further provide different experiments using real data sets extracted from a Bosch fab in Germany to analyze both proposed algorithm efficiency and solution sensitivity against changes in different conditional parameters.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Sandeep Kumar, Vikas Manjrekar, Vivek Singh, Bhupesh Kumar LadAbstractPresent paper envisages the need for an innovative operations planning system to handle the challenges and opportunities offered by next industrial revolution called Industry 4.0 or smart manufacturing. In specific, to embrace the increasing level of automation in manufacturing industries, the obligation of joint consideration of multiple operations functions is realized. On the other hand, quick response to dynamic conditions created by machine failures, change in demand, uncertainty in supply, etc., is important in captivating the advantages of the digitization in industries. Easing out the computational complexity, imposed by the integration of multiple functions, therefore, becomes an important aspect of next generation manufacturing planning systems. Consequently, in this paper, an agent-based approach is engineered around the opportunities offered by modern digital factory viz., intelligence at the shop-floor and ubiquity of wireless communications. While intelligence at shop-floor allows distributing the decision-making tasks to various functional agents, the communication among the agents makes it feasible to incite integrated view through the coordination agent. The approach is demonstrated for a representative industrial environment of an automotive plant. Further, comparison over conventional approaches, computational comparison, effect of degree of integration, and performance of the approach under dynamic conditions are investigated. Finally, the approach is comprehensively evaluated to analyze its robustness and implications in various manufacturing settings. This extensive investigation shows that the proposed operations planning system has capability to apprehend the benefits from next generation intelligent factory.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): M. De Ryck, M. Versteyhe, K. ShariatmadarAbstractThis paper proposes an advanced decentralized method where an Automated Guided Vehicle (AGV) can optimally insert charging stations into an already assigned optimal tour of task locations. In today's industrial AGV systems, advanced algorithms and techniques are used to control the whole fleet of AGVs robustly and efficiently. While in academia, much research is conducted towards every aspect of AGV control. However, resource management or battery management is still one aspect which is usually omitted in research. In current industrial AGV systems, AGVs operate until their resource level drops below a certain threshold. Subsequently, they head to a charging station to charge fully. This programmed behaviour may have a negative impact on the manufacturing systems performance. AGVs lose time charging at inconvenient moments while this time loss could be avoided. Using the approach, an AGV can choose independently when it will visit a charging station and how long it will charge there. A general constrained optimization algorithm will be used to solve the problem and the current industrial resource management will be used as a benchmark. We use a simple extension of the Traveling Salesman Problem (TSP) representation to model our approach. The paper follows a decentral approach which is in the interest of the authors. The result of the proposal is a compact and practical method which can be used in today's operative central or decentral controlled AGV systems.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Alejandro Vital-Soto, Ahmed Azab, Mohammed Fazle BakiAbstractThe flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem (JSP) in which operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled sequencing flexibility, is studied in this work, which considers precedence between the operations in the form of a directed acyclic graph instead of a sequential order. In this work, a mixed integer linear programming (MILP) formulation is presented to minimize weighted tardiness for the FJSP with sequencing flexibility. Due to the NP-hardness of the problem, a novel biomimicry hybrid bacterial foraging optimization algorithm (HBFOA) is developed, which is inspired by the behavior of E. coli bacteria in its search for food. The developed HBFOA search method is hybridized with simulated annealing (SA). Additionally, the algorithm has been enhanced by a local search method based on the manipulation of critical operations. Classical dispatching rules have been employed to create the initial swarm of HBFOA, and a new dispatching rule named minimum number of operations has been devised. The developed approach has been packaged in the form of a decision support system (DSS) developed on top of Microsoft Excel—a tool most small and mid-range enterprises (SME) use heavily for planning. A case study with local industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments using literature benchmarks are further used for validation. The results demonstrate that the HBFOA outperformed the classical dispatching rules and the best integer solution of MILP when minimizing the weighted tardiness and offered comparable results for the makespan instances.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Peter Weiderer, Ana Maria Tomé, Elmar W. LangAbstractThe paper introduces a novel approach for the extraction of physically meaningful thermal component time series during the manufacturing of casting parts. We treat their extraction as Blind Source Separation (BSS) problem by exploiting process-related prior knowledge. Our proposed method arranges temperature time series into a data matrix, which is then decomposed by Non-negative Matrix Factorization (NMF). The latter is guided by a knowledge-based strategy, which initializes the NMF component matrix with time curves designed according to basic physical processes. The temperature time series encompass exclusively non-negative data. Hence NMF lends itself a natural choice as it does not impose mathematical constraints that lack any immediate physical interpretation. We show how to extract components linked to physical phenomena that typically occur during production and cannot be monitored directly. We apply our method to real world data, collected in a foundry during the series production of casting parts for the automobile industry and demonstrate its efficiency.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Hyunsoo LeeAbstractContemporary manufacturing processes require faster real-time controls against dynamic and volatile production environments. While a corresponding simulation model is considered a prerequisite system for the real-time control of a contemporary manufacturing process, simulation modeling and relevant analysis have supported these real-time features comparatively less. These issues might cause procrastination of the simulation modeling, and result in wrong decisions and inaccurate controls. In order to overcome these issues, a new real-time simulation modeling and analysis system is proposed. The proposed system supports sketch-based simulation modeling. The simulation model is constructed using modelers’ sketches of predefined simulation symbols. The sketches are converted automatically into a corresponding stochastic queueing network using Self-organizing Map, a type of neural network. Then, the model is simulated and analyzed using the embedded stochastic queueing analyses. The effectiveness of the proposed system is proven with the modeling, simulation and analyses of several real-time manufacturing cases.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): R. Miehe, T. Bauernhansl, M. Beckett, C. Brecher, A. Demmer, W.-G. Drossel, P. Elfert, J. Full, A. Hellmich, J. Hinxlage, J. Horbelt, G. Jutz, S. Krieg, C. Maufroy, M. Noack, A. Sauer, U. Schließmann, P. Scholz, O. Schwarz, M. ten HompelAbstractThe German manufacturing industry is forced to evolve its processes, techniques, and organizations due to increasing global competition and progressive sustainability requirements. In this context, the soaring possibilities of bio- and information technology have recently let few authors develop the vision of a biological transformation of manufacturing, a concept that to date has been barely concrete to politicians, scientists, and managers. In this paper, we present results of the first systematic assessment of the biological transformation of the German manufacturing industry. We chose a combination of the Delphi method and scenario planning in order to assess key technologies, determine the status quo of Germany and provide a forecast of potential developments. Thereupon, we identify ten fields of action for setting the course for a sustainable industrial value creation. We conclude with a summary and recommendations for decision makers in politics, industries and research.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Hongyi Liu, Lihui WangAbstractCollaborative robot's lead-through is a key feature towards human–robot collaborative manufacturing. The lead-through feature can release human operators from debugging complex robot control codes. In a hazard manufacturing environment, human operators are not allowed to enter, but the lead-through feature is still desired in many circumstances. To target the problem, the authors introduce a remote human–robot collaboration system that follows the concept of cyber–physical systems. The introduced system can flexibly work in four different modes according to different scenarios. With the utilisation of a collaborative robot and an industrial robot, a remote robot control system and a model-driven display system is designed. The designed system is also implemented and tested in different scenarios. The final analysis indicates a great potential to adopt the developed system in hazard manufacturing environment.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Chen Zhao, Shichang Du, Yafei Deng, Guilong Li, Delin HuangAbstractGeometric specifications are important control objects of mechanical components in modern manufacturing. For instance, circularity and cylindricity are essential indicators of high-precision rotary parts. With an increase in the number of measurement points, traditional statistical process control (SPC) methods cannot be applied in many processes because the measurements are highly correlated. During the past two decades, several studies have focused on profile monitoring. A profile, which describes the relationship between independent and response variables, is suitable for large-scale, complex and high-dimensional data monitoring. However, the issue of spatial correlations in measurement points remains unsolved. Considering spatial correlations, this study focuses on circular and cylindrical profiles and proposes a new method combining a spatial correlation model with control charting. SPC methods are utilized to establish control charts and analyze the control processes. The results of simulation and case study indicate that the proposed method is feasible and effective in monitoring circular and cylindrical profiles and can be extended to other geometric specifications.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Tamas Ruppert, Gyula Dorgo, Janos AbonyiAbstractThe sequencing and line balancing of manual mixed-model assembly lines are challenging tasks due to the complexity and uncertainty of operator activities. The control of cycle time and the sequencing of production can mitigate the losses due to non-optimal line balancing in the case of open-station production where the operators can work ahead of schedule and try to reduce their backlog. The objective of this paper is to provide a cycle time control algorithm that can improve the efficiency of assembly lines in such situations based on a specially mixed sequencing strategy. To handle the uncertainty of activity times, a fuzzy model-based solution has been developed. As the production process is modular, the fuzzy sets represent the uncertainty of the elementary activity times related to the processing of the modules. The optimistic and pessimistic estimates of the completion of activity times extracted from the fuzzy model are incorporated into a model predictive control algorithm to ensure the constrained optimization of the cycle time. The applicability of the proposed method is demonstrated based on a wire-harness manufacturing process with a paced conveyor, but the proposed algorithm can handle continuous conveyors as well. The results confirm that the application of the proposed algorithm is widely applicable in cases where a production line of a supply chain is not well balanced and the activity times are uncertain.
Abstract: Publication date: January 2020Source: Journal of Manufacturing Systems, Volume 54Author(s): Longting Chen, Guanghua Xu, Sicong Zhang, Wenqiang Yan, Qingqiang WuAbstractIn the field of prognostic and health management of engineered systems, health indicator construction of bearings is one of the most significant and challenging problems. Many data-driven approaches centered on deep learning have been proposed recently in the context of smart manufacturing, where massive condition monitoring data could be collected. Among them, there are two representative methods, i.e., the convolution neural networks (CNN) based method and the recurrent neural networks (RNN) based method. However, there are some problems with them. The former has small receptive field size and cannot encode time-series information that is crucial for determining the bearings degradation degree, while the latter need hand-crafted features with prior knowledge of experts. Aimed at these problems, an intelligent and end-to-end health indicator construction approach is proposed. It combines structural advantages of previous two methods. It firstly converts the original input data into a series of local features that maintain chronological order in the convolution feature map. Then the sequential local features are elegantly connected by a recurrent neural network, which makes the extracted features in the recurrent layer contain global semantic information with time series. The bearing experiment under two different operating conditions demonstrates that the proposed method is reliable and effective in establishing bearing health indicator and characterizes the nonlinear degradation trend of bearings into approximately linear process over time. The experimental results also show that the proposed method achieves better results concerning trendability and monotonicity, compared with the CNN-based method and the RNN-based method.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Mukund Subramaniyan, Anders Skoogh, Azam Sheikh Muhammad, Jon Bokrantz, Ebru Turanoğlu BekarAbstractThroughput bottleneck analysis is important in prioritising production and maintenance measures in a production system. Due to system dynamics, bottlenecks shift between different production resources and across production runs. Therefore, it is important to predict where the bottlenecks will shift to and understand the root causes of predicted bottlenecks. Previous research efforts on bottlenecks are limited to only predicting the shifting location of throughput bottlenecks; they do not give any insights into root causes. Therefore, the aim of this paper is to propose a data-driven prognostic algorithm (using the active-period bottleneck analysis theory) to forecast the durations of individual active states of bottleneck machines from machine event-log data from the manufacturing execution system (MES). Forecasting the duration of active states helps explain the root causes of bottlenecks and enables the prescription of specific measures for them. It thus forms a machine-states-based prescriptive approach to bottleneck management. Data from real-world production systems is used to demonstrate the effectiveness of the proposed algorithm. The practical implications of these results are that shop-floor production and maintenance teams can be forewarned, before a production run, about bottleneck locations, root causes (in terms of machine states) and any prescribed measures, thus forming a prescriptive approach. This approach will enhance the understanding of bottleneck behaviour in production systems and allow data-driven decision making to manage bottlenecks proactively.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Tian Yu, Cheng Zhu, Qing Chang, Junfeng WangAbstractMaintenance management is critical in enabling a smooth production operation in a manufacturing system. Once one machine fails, a corrective maintenance (CM) is required to resume its normal operation. However, perfect CM is not always needed to restore the failed machine as good as new. A Multi-level or imperfect CM is a more realistic and economic way based on the need of production operational levels. In addition, maintenance resources such as maintenance staffs are limited in reality. Therefore, how to dispatch the limited maintenance workforce with an appropriate level of CM is critical since it directly impacts the overall system’s productivity, energy efficiency and cost. This paper aims at creating a real-time CM scheduling policy to reduce the overall maintenance and energy related cost for a stochastic serial production line. To accomplish the goal, the CM scheduling problem is formulated as an online task allocation (OTA) problem. The expected cost rate of the serial production line is introduced and used to define the payoff function of the OTA problem. A numerical case study is provided to evaluate the effectiveness of the OTA based maintenance policy by comparing with other heuristic policies.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Eeva Järvenpää, Niko Siltala, Otto Hylli, Minna LanzAbstractSmart manufacturing calls for rapidly responding production systems which help the manufacturing companies to operate efficiently in a highly dynamic environment. Currently, the system design and reconfiguration planning are manual processes which rely heavily on the designers’ expertise and tacit knowledge to find feasible system configuration solutions by comparing the characteristics of the product to the technical properties of the available resources. Rapid responsiveness requires new computer-aided intelligent design and planning solutions that would reduce the time and effort put into system design, both in brownfield and greenfield scenarios. This article describes the implementation of a capability matchmaking approach and software which automatizes the matchmaking between product requirements and resource capabilities. The interaction of the matchmaking system with external design and planning tools, through its web service interface, is explained and illustrated with a case example. The proposed matchmaking approach supports production system design and reconfiguration planning by providing automatic means for checking if the existing system already fulfils the new product requirements, and/or for finding alternative resources and resource combinations for specific product requirements from large search spaces, e.g. from global resource catalogues.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Tigist Fetene Adane, Maria Floriana Bianchi, Andreas Archenti, Mihai NicolescuAbstractMachining of parts by using dedicated production systems has been, and continues to be, a viable manufacturing method. There are situations, however, where this type of system is not feasible due to changes in product type, customer demand, work-piece material, or design specification. From a competitive manufacturing environment, production system selection is a crucial issue for all component manufacturing companies. Improper selections could negatively affect the overall performance of a manufacturing system, for instance the productivity, as well as the cost and quality of manufactured components. In this paper, the application of system dynamics modelling and simulation of a complex manufacturing process is presented as a potential tool to investigate and analyse the performance of manufacturing system in response to disturbances in the system’s inputs (e.g., volume of products). In order to investigate the model soundness, a case study applied to the manufacturing of an engine block will be examined. The model presented here has been developed based on current engine block production for the vehicle manufacturing industry. Such a model can assist manufacturing system selection–centered round the capacity to control machining system parameters –as a testable way to choose a machining strategy from pre-selected performance criteria. More specifically, the benefit of this research lies in the fact that it will enable companies to implement improved potential manufacturing system optimization that responds during unexpected demand fluctuations. In addition, it will help in understanding the complex interaction between the process and operational parameters of a manufacturing system and help identify those critical parameters, ones that can lead to an optimizing strategy in the manufacturing standards of engine block production.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Samir Ouaret, Jean-Pierre Kenné, Ali GharbiAbstractThis paper deals with the control of a hybrid manufacturing/remanufacturing system subject to random breakdowns and repairs. Given the heterogeneity of returned products, the remanufacturing machine deteriorates with time as a result of imperfect repairs, and needs to be replaced. The manufacturing machine receives homogeneous raw materials, and is not affected by this type of deterioration. The main objective of this paper is to find the production rates of both machines and the replacement rate of the remanufacturing machine that minimize the total cost, including production, inventory holding, backlog, repair and replacement costs, over an infinite planning horizon. A novel mathematical model is proposed for the underlying class of problems related to the history of breakdowns and repairs. This model is characterized by the extension of the state space, leading to a Markov decision model, allowing a derivation of the optimality conditions. Otherwise, such conditions are impossible to obtain. Optimality conditions in the form of Hamilton-Jacobi-Bellman (HJB) equations are thus developed. We show that despite the increased state space dimension, the problem remains tractable, and the solutions of HJB equations are obtained numerically. Finally, an illustrative example and a sensitivity analysis are provided to verify the robustness of the control policies obtained.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Wenyu Zhang, Jiepin Ding, Yan Wang, Shuai Zhang, Zhiying XiongAbstractScheduling multiple heterogeneous tasks in a manufacturing system to satisfy customized requirements becomes challenging, especially in uncertain manufacturing environment. In cloud manufacturing, a serious problem is to schedule multiple heterogeneous tasks to balance the benefit conflicts among customers, manufacturing enterprises, and manufacturing platform comprehensively. Therefore, this study formulates the multi-task scheduling problem mathematically as a new fuzzy mixed-integer linear programming (FMILP) model based on multi-perspective collaborative optimization and fuzzy set theory. To solve the FMILP model, an extended genetic algorithm (EGA) with the interval-valued intuitionistic fuzzy entropy weight (IVIFEW) method is proposed. The IVIFEW method is adapted to obtain the preference of QoS attributes and task priority. In addition, the basic genetic algorithm is improved by integrating a migration operator, local search, and restart strategy to maintain the diversity of population and enhance the exploitation ability. A suitable parameter combination of EGA is found in a series of experiments based on the Taguchi method. The experimental results demonstrate that the proposed EGA solves the FMILP model effectively, providing better optimal solutions compared with the baseline algorithms.
Abstract: Publication date: Available online 29 October 2019Source: Journal of Manufacturing SystemsAuthor(s): Qinglin Qi, Fei Tao, Tianliang Hu, Nabil Anwer, Ang Liu, Yongli Wei, Lihui Wang, A.Y.C. NeeAbstractDigital twin is revolutionizing industry. Fired by sensor updates and history data, the sophisticated models can mirror almost every facet of a product, process or service. In the future, everything in the physical world would be replicated in the digital space through digital twin technology. As a cutting-edge technology, digital twin has received a lot of attention. However, digital twin is far from realizing their potential, which is a complex system and long-drawn process. Researchers must model all the different parts of the objects or systems. Varied types of data needed to be collected and merged. Many researchers and participators in engineering are not clear which technologies and tools should be used. 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin. From the perspective of 5-dimension digital twin model, this paper tries to investigate and summarize the frequently-used enabling technologies and tools for digital twin to provide technologies and tools references for the applications of digital twin in the future.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Li Yi, Christopher Gläßner, Jan C. AurichAbstractAdditive Manufacturing (AM) is the umbrella term for manufacturing processes that add materials layer by layer to create parts. AM technologies show numerous potentials in terms of rapid prototyping, tooling and direct manufacturing of functional parts and imply revolutionary benefits for the manufacturing industry. Currently, many industrial areas are marching to a more comprehensive application of AM. Hence, the development of new tools, methods, and concepts for guiding companies to implement AM technologies requires more research attention. This paper introduces the results of a research project carried out by academic and industrial partners from the German commercial vehicle industry. The research project addressed four issues for a long-term application of AM technologies: identification of barriers for AM applications, cost estimation for AM application, design of hybrid additive-subtractive process chains, and quality management with AM.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Tianyi Wu, Xiaobing Ma, Li Yang, Yu ZhaoAbstractWait time due to the exhaustion of raw materials or lack of demand is common in manufacturing/production processes, which provides cost-effective maintenance opportunities. This paper designs a two-phase opportunistic maintenance framework based on defect information, which integrates properties of production waits into the decision-making process. At the first phase, a finite number of inspections are executed to reveal the defective state, followed by imperfect preventive repair. At the second phase, no maintenance action is taken until reaching a scheduled maintenance window (postponed maintenance) or arrivals of production waits (opportunistic maintenance). The integration of imperfect repair with postponed and opportunistic maintenance enables a sufficient utilization of remaining useful lifetime and a flexible resource allocation. Under the constraint of the steady-availability requirement, the optimal policy minimizing the system maintenance cost is obtained via the genetic algorithm. A case study from a steel convertor plant shows that our policy outperforms some classic maintenance policies.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Yuhang Yang, Yifang Zhang, Y. Dora Cai, Qiyue Lu, Seid Koric, Chenhui ShaoAbstractHigh-resolution spatiotemporal data is crucial for characterizing, modeling, and monitoring the space–time dynamics of complex systems in manufacturing. However, the acquisition of such data is generally expensive and time-consuming. Spatiotemporal interpolation aims to predict the values at unmeasured locations using measured data, and emerges as a promising solution to cost-effectively characterizing spatiotemporal processes. Since the interpolation performance is largely influenced by the available measurement data, an intelligent measurement strategy is an important prerequisite to the success of interpolation methods. In this paper, a hierarchical measurement strategy is developed to achieve a balance between interpolation precision and measurement cost in spatiotemporal interpolation. A hierarchical decision-making problem is formulated to determine the observation times and measurement locations at each observation. To expedite the solution search process, hierarchical genetic algorithm is adopted and implemented using high-performance computing. Moreover, a new form of the covariance function is developed using a Bessel additive periodic variogram to more accurately model the periodic spatial variations in spatiotemporal processes. Case studies using real-world data collected from ultrasonic metal welding are reported to demonstrate the effectiveness of the proposed method.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Johannes Fisel, Yannick Exner, Nicole Stricker, Gisela LanzaAbstractCurrent trends, such as customers' demand for individual products and shorter product life cycles, are addressed by companies through a greater variety of products and variants. With regard to the line balancing of flow assembly systems, however, adjustments are associated with high investments, which requires a new planning approach for assembly line balancing. Existing approaches do not consider the reallocation of assembly tasks or the dimensioning of system-inherent flexibility and changeability according to requirements. Furthermore, they neglect the uncertainty of the future market situation. The proposed approach aims at optimizing the line balancing of flow assembly systems, taking into account the potential need for adaptation in order to meet this uncertain planning environment. For this purpose, the exchange of occurring costs as well as flexibility and changeability of the system is focused. Based on scenarios, potential future compositions of the variant mix are investigated and the resulting implications for the assembly system are derived. By applying the approach, an adequate adaptable assembly line balancing is generated by performing a mixed integer linear optimization. Since the evaluation and identification of adequacy are subject to subjective factors, several potentially adequate solutions are generated, which differ in terms of costs, flexibility and changeability. The result of the presented approach is a front of pareto-optimal assembly line balancing configurations. In order to show its practical applicability, a use case in automotive assembly line balancing is presented.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Annamaria Gisario, Michele Kazarian, Filomeno Martina, Mehrshad MehrpouyaAbstractThe applications of Additive Manufacturing (AM) have been grown up rapidly in various industries in the past few decades. Among them, aerospace has been attracted more attention due to heavy investment of the principal aviation companies for developing the AM industrial applications. However, many studies have been going on to make it more versatile and safer technology and require making development in novel materials, technologies, process design, and cost efficiency. As a matter of fact, AM has a great potential to make a revolution in the global parts manufacturing and distribution while offering less complexity, lower cost, and energy consumption, and very highly customization. The current paper aims to review the last updates on AM technologies, material issues, post-processes, and design aspects, particularly in the aviation industry. Moreover, the AM process is investigated economically including various cost models, spare part digitalization and environmental consequences. This review would be helpfully applied in both academia and industry as well.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Woo-sung Kim, Kyungsu ParkAbstractInspired by flexible manufacturing systems with setups, in this study, we investigate the steady-state behavior of flow line models with deterministic service durations. In such systems, different types of products with different batch sizes are produced in a single production line, and a setup is required when switching from one type of product to another. In this study, we aim at analyzing the effect of different setups on a system’s performance metrics, i.e., the maximum production rate and delay distribution of a product experienced inside the system. The maximum production rate of a deterministic flow line is the reciprocal of its bottleneck process time; however, the different setups existing in a system complicate the analysis. Furthermore, setups are necessary in flexible manufacturing systems. The systems that are the subject of our model are steel pipe manufacturing process and semiconductor wafer fabrications. Additionally, we obtain and analyze a two-dimensional, discrete-time, time-homogeneous Markov chain model to derive the system’s performance measures.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Adolfo Muñoz, Xavier Mahiques, J. Ernesto Solanes, Ana Martí, Luis Gracia, Josep TorneroAbstractIn recent years, the quality control of car body surfaces production lines have been put in the context of Industry 4.0. The emergence of automatic defect detection systems have helped to standardize the brand quality and gather information about all quality control tasks performed by workers. However, current worker interfaces used to indicate the location and other characteristics of the defects found by these systems have overcome the ergonomics of workers and increased their stress at work. This paper presents a novel mixed reality-based user interface for quality control inspection which is more intuitive, in order to improve the ergonomics of workers, reduce their stress at work and improve the productivity of current quality control production lines. An experimental prototype is shown in the paper in order to demonstrate the benefits of the proposed interface. In addition, the paper shows the results of several usability tests that compare the proposed mixed reality-based user interface with current interfaces used in important factories such as Mercedes-Benz, analyzing the benefits and drawbacks of each interface.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Chen Zheng, Xiansheng Qin, Benoît Eynard, Jing Bai, Jing Li, Yicha ZhangAbstractContinuously growing pressures due to the shorter development lead-time and the uncertainty of increasingly complex market require a high flexibility from production companies and their manufacturing systems. Comparing with large enterprises, small and medium-sized enterprises (SMEs) which lack concentrated market power and powerful original equipment manufacturers (OEMs) have difficulty and risk to develop flexible and customised robotic manufacturing systems. Nowadays, although various design methods for robotic manufacturing systems have been made to fix this problem, however, there still exist two main challenges: flexibility in design and high degree of customisation.To deal with the existing challenges, this paper proposes an SME-oriented design approach based on a configuration design paradigm. This method can offer decision support to designers to form flexible architecture for robotic manufacturing systems and, at the same time, give more interactive configuration freedom to customers so as to achieve a high productivity and flexibility for customisation but with less risk and cost of product development. To validate the proposed method, two design cases of robotic manufacturing systems are presented for demonstration.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Manoj Babu, Pasquale Franciosa, Dariusz CeglarekAbstractIn-line dimensional inspection of free form surfaces using robotic 3D-optical scanners provide an opportunity to reduce the mean-time-to-detection of product quality defects and has thus emerged as a critical enabler in Industry 4.0 to achieve near-zero defects. However, the time needed to inspect large industrial size sheet metal parts by 3D-optical scanners frequently exceeds the production cycle time (CT), consequently, limiting the application of in-line measurement systems for high production volume manufacturing processes such as those used in the automotive industry. This paper addresses the aforementioned challenge by developing the Spatio-Temporal Adaptive Sampling (STAS) methodology which has the capability for (i) estimation of whole part deviations based on partial measurement of a free form surface; and, (ii) adaptive selection of the next region to be measured in order to satisfy pre-defined measurement criterion. This is achieved by first, modelling spatio-temporal correlations in the high dimensional Cloud-of-Points measurement data by using a dimension reduced space-time Kalman filter; then, dynamically updating the model parameters during the inspection process by incorporating partial measurement data to predict entire part deviations and adaptively choose the next critical region of the part to be measured.The developed STAS methodology enhances the current free form surface inspection models, which are mostly based on spatial analysis; into spatio-temporal model, which uses (i) the spatial analysis to model part deformation; and, (ii) temporal analysis to model autoregressive behaviour of the manufacturing process for prediction of next part deviations. This provides capability to predict the whole part deviation based on partial measurement information and consequently reduces measurement cycle time. The industrial case study using a robotic 3D-optical scanner for the measurement of an automotive door inner part demonstrates the STAS methodology, which resulted in (i) a 3 Sigma error of prediction of whole part deviations within 0.27 mm based on measurement of 33% of the part surface; and, (ii) a corresponding CT reduction of 42.2% from 510.5 s required by current best practice to measure the whole part to 295.18 s required to partially measure the part.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Fengtian Chang, Guanghui Zhou, Chao Zhang, Zhongdong Xiao, Chuang WangAbstractThe development of product-service-system (PSS) urges the emergence of service-oriented maintenance mode (SOMA), which leads to the shift of maintenance manners from traditional high-cost in-house maintenance to the performance-based proactive service offerings for complex multi-component systems. In this case, a novel and adaptive maintenance grouping strategy that considers service responses, service interactions and dynamic prediction requirements is desired to implement the predictive maintenance planning. Thus, this paper presents a service-oriented dynamic multi-level predictive maintenance grouping strategy. It involves component level, dynamic grouping level and system level to fulfill the individual optimization and grouping optimization. Firstly, the individual service time based on real-time remaining useful life (RUL) distribution information is optimized and obtained with the minimum average cost. Secondly, considering the existing economic and resource dependence in predictive and corrective services, this paper dynamically groups the predicted optimal individual services from rolling planning horizon at each calculated service planning time. The penalty costs and grouping service costs are both constructed. Then, a modified k-means method is designed to find out the optimal short-term grouping execution strategy from medium-term prediction information where the average cost savings and relative availability improvement degree are regarded as the optimization objectives. Finally, some numerical examples with three predictive grouping scenarios show that our grouping strategy could provide a feasible and effective long-term dynamic maintenance planning for proactive OEM or PSS providers. It is adapted to the SOMA on complex multi-component systems.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Chao Guan, Zeqiang Zhang, Silu Liu, Juhua GongAbstractThe novel multi-workshop facility layout problem presented in this paper involves the placement of a group of departments into several workshops; it deals with the distribution of departments over workshops and their optimal exact coordinates without overlapping. In considering practical situations, the internal material handling flows and external transport flows are taken into account in the problem. In this study, the problem is first formulated as a mixed integer linear programming model with three objectives: minimization of overall material handling costs, minimization of number of workshops, and maximization of utilization ratio of workshop floor. Thereafter, the proposed multi-objective particle swarm optimization algorithm with an innovative discrete framework and incorporated with a two-stage approach is employed to search for feasible solutions locally and globally. Finally, several benchmark instances derived from literature that satisfy our case requirements are employed to evaluate the performance of the proposed method; highly preferable results are typically achieved.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Bugra Alkan, Robert HarrisonAbstractHighly diverse factors including technological advancements, uncertain global market and mass personalisation are believed to be main causes of ever-growing complexity of manufacturing systems. Although complex systems may be needed to achieve global manufacturing requirements, complexity affects on various factors, such as: system development effort and cost, ease of re-configuration, level of skill required across the system life-cycle (e.g. design, operate and maintain). This article aims to develop a scientifically valid and industrially applicable complexity assessment approach to support early life-cycle phases of component-based automation systems against unwanted implications of structural system complexity. The presented approach defines component-based automation system as a constellation of basic components which can be represented in various design domains, such as: mechanical, electrical, pneumatic, control, etc. Accordingly, structural complexity is expressed as the combination of both inherent complexity of system entities and topological complexity resulting from the integration of elements of such constellations in a multi-layered network. The proposed approach is used to specify and implement a complexity assessment module which can be integrated into a series of virtual system design software solutions, in order to add complexity assessment as part of the design support and validation tools used by manufacturing engineers. Consequently, the proposed approach is integrated into the vueOne virtual engineering tool, wherein virtual automation system design data can be streamlined and used as input to the theoretical complexity model. In the developed tool, only mechanical and logical design domains are considered due to the limited data availability in early design phase. Inherent complexity of both mechanical and logical system entities and their interactions are expressed as a function of domain-specific design elements, and topological complexity is defined as the graph energy of the corresponding design connectivity matrix. Furthermore, the values of mathematical model parameters are determined based on an optimisation study, where subjective opinions of system/control engineers regarding the effort/difficulty associated with the development of thirty different component-based automation system designs are correlated with the corresponding complexity model outputs to minimise the prediction errors. The proposed approach is also demonstrated on a modular production system consisting of four sub-modules. The study shows that the approach can help designers/managers to better identify root causes of structural system complexity, and provides a systemic approach to compare alternate system designs during early system planning phase.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Lukas Baier, Julian Frommherz, Elmar Nöth, Toni Donhauser, Peter Schuderer, Jörg FrankeAbstractFast identification of failure root causes is a major task in minimizing rejects in manufacturing. Due to increasing complexity of products and supply chains many interdependencies affect the final product quality. As knowledge about every possible interdependence is hardly held by individuals, data analysis strategies are required to evaluate captured information. Hence, we propose a root cause identification method for determining the main influencing factors on end products failing end of line tests. Additionally, graphical representation of calculation results in spectrograms similar to audio signals facilitates human interpretation. The evaluation of the proposed method on the basis of a use case proves the applicability in a real scenario. The method is able to identify the root cause for rejects within short periods of time. In this specific case it shortened the analysis time by a factor of about 50. In the future, it empowers smart production systems to automatically identify failure root causes and to take countermeasures like adjusting process parameters.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Xiao Zhang, Benjamin Lies, Hao Lyu, Hantang QinAbstractElectrohydrodynamic inkjet (e-jet) printing is a technique which utilizes electrical forces to generate droplets in micro/nano scale using conductive inks. Currently, there is no procedure in place to measure the printed patterns without taking the sample away from the printer setup. Removal of the substrate from the printing stage during the e-jet printing process prevents any additional work from being performed on the sample. We investigated the application of scalar diffraction for the in-situ measurement and digital reconstruction of opaque material printed on transparent substrates. Measurement and characterization of the printed material can be achieved in-situ to alter printing condition in process for quality assurance. In order to accomplish the sample reconstruction, a digital recording of a scalar diffraction pattern in the image plane was employed in this paper with a magnification of 5× with the help of a combination of lenses. The reconstructed images were then compared to images captured by an offline high-resolution microscope. The results indicated a submicron accuracy of the feature radii and the locations of feature centers. In addition to the quantitative measurements, this method also allows for the operator to view the overall form of the printed patterns. Our findings demonstrate an effective approach for in-situ monitoring of e-jet printing and printed patterns, which could pave the way for the industrial application in printing testing field.
Abstract: Publication date: Available online 26 August 2019Source: Journal of Manufacturing SystemsAuthor(s): Qianqian Dong, Raed Kontar, Min Li, Gang Xu, Jinwu XuAbstractStatistical monitoring of advanced production processes is becoming increasingly challenging due to the large number of key performance variables that characterize a process. These variables often are non-Gaussian, highly correlated and exhibit non-linear dependencies. Traditional multivariate monitoring methods handle non-Gaussian data through combining both independent component analysis (ICA) and support vector data description (SVDD). However, redundant independent components not only increase the modeling complexity of SVDD but also reduce the accuracy of the monitoring. In this article, we solve the above problems by introducing a Laplacian based weighting score to adjust the ICA-SVDD procedure. The key aspect of our model is that independent components are automictically selected using a Laplacian algorithm, which are inputted to a SVDD model to determine the control limits. The advantageous features of the proposed method are demonstrated through a numerical study as well as a case study which concerns an application to a hot rolling process for monitoring steel production processes.
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