Service perspective based production control system for smart job shop under industry 4.0

Industry 4.0 describes a smart job shop as follows: it can meet individual customer requirements even if the requirements are changed at the last minute; its production control system (PCS) can rapidly respond to unexpected disruptions in production, and smart workpieces in the smart job shop can communicate with workstations to tell them what to do next. Present PCSs issue production instruction (PI) to workstation in a relatively long period such as a day, a week, even a month. And the PI is usually at process level, which means it is not sufficient to maintain smooth production flow at the operational level. Therefore, the existing PCSs cannot meet the requirements of Industry 4.0. On account of this, this article proposes a smart workpiece enabled production instruction service system for smart job shop under Industry 4.0. The PI service system in smart job shop consists of three parts such as PI sets generation, PI sets execution and PI sets update. In PI sets generation, the PI is viewed as a service requirement from the smart workpiece for the workstation, and then a PI service model is established to integrate machining actions with different kinds of manufacturing resources, processing place and processing time. Based on that, a method of converting the Gantt chart to PI sets is presented. In PI sets execution, a PI service unit is proposed for real-time issuing PIs to the radio-frequency identification (RFID) tags of smart workpieces. In PI sets update, the update of PI sets including unexecuted processes PI sets and current processes PI sets is discussed in detail. Finally, a small-scale smart job shop is taken as an example to illustrate the feasibility of the PI service system.     

Intelligent workload balance control of the assembly process considering condition-based maintenance

Balancing the workloads of workstations is key to the efficiency of an assembly line. However, the initial balance can be broken by the changing processing abilities of machines because of machine degradation, and at some point, re-balancing of the line is inevitable. Nevertheless, the impacts of unexpected events on assembly line re-balancing are always ignored. With the advanced sensor technologies and Internet of Things, the machine degradation process can be monitored continuously, and condition-based maintenance can be implemented to improve the health state of each machine. With the technology of robotic process automation, workflows of the assembly process can be smoothed and workstations can work autonomously together. A higher level of process automation can be achieved. Real-time information of the processing abilities of machines will bring new opportunities for automated workload balance via adaptive decision-making. In this study, a fuzzy control system is developed to make real-time decisions to balance the workloads based on the processing abilities of workstations, given the policy of condition-based maintenance. Fuzzy controllers are used to decide whether to re-balance the assembly line and how to adjust the production rate of each workstation. The numerical experiments show that the buffer level of the assembly line with the proposed fuzzy control system is lower than that of the assembly line without any control system and the buffer level of the assembly line with another control system is the lowest. The demand can always be satisfied by assembly lines except the one with another control system since there is too much production loss sacrificed for the low buffer level. The sensitivity analysis of the control performance to the parameter settings is also conducted. Thus, the effectiveness of the proposed fuzzy control system is demonstrated, and intelligent automation can improve the performance of the assembly process by the fuzzy control system since real-time information of the assembly line can be used for adaptive decision-making.     

Resilience dynamics modeling and control for a reconfigurable electronic assembly line under spatio-temporal disruptions

This paper proposes a resilience dynamics modeling and control approach for a reconfigurable electronic assembly line under disruptions. A Digital Twin (DT) platform is developed as the basis for resilience analysis, and open reconfigurable architectures (ORAs) are introduced to support reconfiguration of the assembly line. The time-delays of disruptions are identified and used to characterize their spatio-temporal attributes. A systematic method based on max-plus algebra is proposed to model resilience dynamics under disruptions. The resilience control policy used in the DT platform is developed to minimize production losses, and it is tested on a smart phone assembly line, with its effectiveness validated by comparative analysis.     

Fuzzy activity time-based model predictive control of open-station assembly lines

The 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.     

Towards smart production planning and control; a conceptual framework linking planning environment characteristics with the need for smart production planning and control

Rapid advances in Industry 4.0 have the potential to transform production planning and control (PPC) through the emerging concept of smart PPC. This paper provides a visionary perspective by addressing the gap in research on how the characteristics of a company's planning environment impact on the need for, and potential benefit of, smart PPC. The paper posits that the potential of smart PPC to improve PPC performance increases with the complexity of the planning environment. A set of propositions is developed for how 12 product, market, and process variables impact on the need for smart PPC. These are operationalized into a conceptual framework that can be used as a tool by practitioners and academics to assess a company's need for smart PPC. A case study from the food sector illustrates the applicability of the framework and describes three potential applications for how four elements of smart PPC (real-time data management, dynamic production planning and re-planning, autonomous production control, and continuous learning) can be used to address key PPC challenges and open new opportunities for improving PPC. Future research should strengthen the validity and applicability of the proposed framework through additional cases across industrial sectors and carry out case studies, surveys, and structural equation modeling to investigate the specific relationship between planning environment characteristics, smart technologies, and the elements of smart PPC.     

IoT-enabled smart appliances under industry 4.0: A case study

Manufacturers expect the extra value of Industry 4.0 as the world is experiencing digital transformation. Studies have proved the potential of the Internet of Things (IoT) for reducing cost, improving efficiency, quality, and achieving data-oriented predictive maintenance services. Collecting a wide range of real-time data from products and the environment requires smart sensors, reliable communications, and seamless integration. IoT, as a critical Industry 4.0 enabler emerges smart home appliances for higher customer satisfaction, energy efficiency, personalisation, and advanced Big data analytics. However, established factories with limited resources are facing challenges to change the longstanding production lines and meet customer’s requirements. This study aims to fulfil the gaps by transforming conventional home appliances to IoT-enabled smart systems with the ability to integrate into a smart home system. An industry-led case study demonstrates how to turn conventional appliances to smart products and systems (SPS) by utilising the state-of-the-art Industry 4.0 technologies.     

A function-oriented biologically analogical approach for constructing the design concept of smart product in Industry 4.0

The development of the Industry 4.0 paradigm and the advancement of information technology have aroused new consumer requirements for smart products that are capable of context awareness and autonomous control. Nature holds huge potential for inspiring innovative design concepts that can meet the ever-growing need for smart products since biology perceive and interact with their living environment for survival. However, to date, very few studies have explored the application of natural wisdom in building innovative design concepts for smart products. This paper proposes a function-oriented design approach for smart products, by analogizing to biological prototypes. To do so, a unified functional representation, based on the Function–behavior–structure (FBS) ontology, is proposed to abstract biological prototypes, followed by a fuzzy triangular numbers-based algorithm designed to locate appropriate biological prototypes as analogical sources for smart product development. Moreover, functional innovative strategies and a hybrid design process are formulated to develop design concepts of smart products, by integrating several existing engineering design methods. Finally, an illustrative design case of a smart natural resource collecting system is used to demonstrate the workability of the proposed method.     

A real-time decision-making of maintenance using fuzzy agent

In many manufacturing processes, real-time information can be obtained from process control computers and other monitoring devices. However, production control problems are frequently accompanied by certain and uncertain conditions. Problems with uncertainty conditions generally include difficulty in identifying an optimal solution in real-time using conventional mathematical approaches. This study presents a fuzzy logic approach for decision-making of maintenance. Some linguistic variables and rules-of-thumb are used to form the fuzzy logic models, based on the domain experts’ experiences in production line and maintenance department. The historical production data are used to train and tune the fuzzy models. The tuned fuzzy models are then embedded into an internet-based and event-oriented information system as fuzzy agent. The production controller can easily make suitable production control decisions based on the inference results of fuzzy agents to satisfy the quick response requirement.     

A method of NC machine tools intelligent monitoring system in smart factories

The construction of effectual connection to bridge the gap between physical machine tools and upper software applications is one of the inherent requirements for smart factories. The difficulties in this issue lies in the lack of effective and appropriate means for real-time data acquisition, storage and processing in monitoring and the post workflows. The rapid advancements in Internet of things (IoT) and information technology have made it possible for the realization of this scheme, which have become an important module of the concepts such as “Industry 4.0”, etc. In this paper, a framework of bi-directional data and control flows between various machine tools and upper-level software system is proposed, within which several key stumbling blocks are presented, and corresponding solutions are subsequently deeply investigated and analyzed. Through monitoring manufacturing big data, potential essential information are extracted, providing useful guides for practical production and enterprise decision-making. Based on the integrated model, an NC machine tool intelligent monitoring and data processing system in smart factories is developed. Typical machine tools, such as Siemens series, are the main objects for investigation. The system validates the concept and performs well in the complex manufacturing environment, which will be a beneficial attempt and gain its value in smart factories.     

Real-time information driven intelligent navigation method of assembly station in unpaced lines

This paper considers the assembly station as a breakthrough to improve the real-time information driven control and optimization of assembly process in unpaced asynchronous line. By adopting automatic identification technologies, the overall architecture of the real-time intelligent navigation of assembly station (INoAS) is put forward. Under this architecture, three core services, namely the real-time assembly operating guidance service (OGS), collaborative production service (CPS) among assembly stations and real-time queuing service (RQS) of the jobs at each station, are designed to provide optimal and dynamical navigation for assembly activities for each station. Then, the disturbances and exceptions could be timely captured by installing the INoAS at each station, and the operating guidance, collaborative production information sharing and real-time queuing could be easily achieved. The presented architecture and services of INoAS will facilitate the real-time information driven process monitor and control between the line and stations.     

A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins

Nowadays, one important challenge in cyber-physical production systems is updating dynamic production schedules through an automated decision-making performed while the production is running. The condition of the manufacturing equipment may in fact lead to schedule unfeasibility or inefficiency, thus requiring responsiveness to preserve productivity and reduce the operational costs. In order to address current limitations of traditional scheduling methods, this work proposes a new framework that exploits the aggregation of several digital twins, representing different physical assets and their autonomous decision-making, together with a global digital twin, in order to perform production scheduling optimization when it is needed. The decision-making process is supported on a fuzzy inference system using the state or conditions of different assets and the production rate of the whole system. The condition of the assets is predicted by the condition-based monitoring modules in the local digital twins of the workstations, whereas the production rate is evaluated and assured by the global digital twin of the shop floor. This paper presents a framework for decentralized and integrated decision-making for re-scheduling of a cyber-physical production system, and the validation and proof-of-concept of the proposed method in an Industry 4.0 pilot line of assembly process. The experimental results demonstrate that the proposed framework is capable to detect changes in the manufacturing process and to make appropriate decisions for re-scheduling the process.     

Optimisation of mixed-model assembly line balancing problem under uncertain demand

Assembly lines play a crucial role in determining the profitability of a company. Market conditions have increased the importance of mixed-model assembly lines. Variations in the demand are frequent in real industrial environments and often leads to failure of the mixed-model assembly line balancing scheme. Decision makers have to take into account this uncertainty. In an assembly line balancing problem, there is a massive amount of research in the literature assuming deterministic environment, and many other works consider uncertain task times. This research utilises the uncertainty theory to model uncertain demand and introduces complexity theory to measure the uncertainty of assembly lines. Scenario probability and triangular fuzzy number are used to describe the uncertain demand. The station complexity was measured based on information entropy and fuzzy entropy to assist in balancing systems with robust performances, considering the influence of multi-model products in the station on the assembly line. Taking minimum station complexity, minimum workload difference within station, maximum productivity as objective functions, a new optimization model for mixed-model assembly line balancing under uncertain demand was established. Then an improved genetic algorithm was applied to solve the model. Finally, the effectiveness of the model was verified by several instances of mixed-model assembly line for automobile engine.     

Assembly line balancing under uncertainty: Robust optimization models and exact solution method

This research deals with line balancing under uncertainty and presents two robust optimization models. Interval uncertainty for operation times was assumed. The methods proposed generate line designs that are protected against this type of disruptions. A decomposition based algorithm was developed and combined with enhancement strategies to solve optimally large scale instances. The efficiency of this algorithm was tested and the experimental results were presented. The theoretical contribution of this paper lies in the novel models proposed and the decomposition based exact algorithm developed. Moreover, it is of practical interest since the production rate of the assembly lines designed with our algorithm will be more reliable as uncertainty is incorporated. Furthermore, this is a pioneering work on robust assembly line balancing and should serve as the basis for a decision support system on this subject.     

Modelling and control of an assembly/disassembly mechatronics line served by mobile robot with manipulator

The aim of this paper is to reverse an assembly line using a mobile platform equipped with a manipulator. By reversibility we mean that the line is able to perform disassembly. For this purpose, an assembly/disassembly line balancing (A/DLB) and a synchronised hybrid Petri nets (SHPN) model will be used to model and control an assembly/disassembly mechatronics line (A/DML), with a fixed number of workstations, served by a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM). The SHPN model is a hybrid type, where A/DML is the discrete part, and WMR with RM is the continuous part. Moreover, the model operates in synchronised mode with signals from sensors. Disassembly starts after the assembly process and after the assembled piece fails the quality test, in order to recover the parts. The WMR with RM is used only during disassembly, to transport the parts from the disassembling locations to the storage locations. Using these models and a LabView platform, a real-time control structure has been designed and implemented, allowing automated assembly and disassembly, where the latter is assisted by a mobile platform equipped with a manipulator.     

纯滞后过程模糊预测控制研究

针对复杂工业过程控制,提出一种基于T—S模糊模型的预测控制方法,从参考轨迹和可测的过程变量提取特征信息,并利用最优控制理论,构成了具有模糊模型的纯滞后预测控制系统。经过跟踪调节和定值干扰调节实验仿真,仿真结果表明基于T—S模糊模型的预测控制方法的有效性和可行性,系统的跟踪效果良好,调节品质优于单纯的线性调节器。     

基于遗传的装配线平衡系统研究

给出了用于求解装配线平衡的遗传算法。在此基础上,分析了装配线平衡系统的功能和工作机理。并采用面向对象语言开发了装配线平衡系统。最后将此系统用于某装配线的平衡,并依据平衡结果进行仿真,证明该算法效果较好。利用该系统可以有效地解决装配线平衡问题,大大降低成本,为提高装配线的生产效率和改进装配线提供了技术依据。     

RFID-enabled real-time wireless manufacturing for adaptive assembly planning and control

Wireless Manufacturing (WM) is emerging as a next-generation advanced manufacturing technology (AMT). WM relies substantially on wireless devices (e.g. RFID—Radio Frequency Identification or Auto ID—Automatic Identification, and on wireless information/communication networks (e.g. Wi-Fi and Bluetooth), for the collection and synchronization of manufacturing data. This paper proposes a WM framework where RFID devices are deployed to workstations, critical tools, key components, and containers of WIP (Work In Progress) materials to turn them into so-called smart objects. The study is based on a simplified product assembly line. Smart objects are tracked and traced and shop-floor disturbances are detected and fed back to decision makers on a real-time basis. Such real-time visibility closes the loop of adaptive assembly planning and control. A facility called assembly line explorer is provided for the line manager to oversee the status of the entire assembly line, and a workstation explorer facility for operators to monitor the status of their operations at corresponding workstations. These facilities improve the effectiveness of managerial decisions and operational efficiency.     

射频识别在多品种小批量生产管理中的应用研究

针对多品种小批量生产环境下,生产过程难以实现精准管理的问题,采用射频识别标签标识车间重要生产对象,关键应用管控点部署交互式作业控制终端,自动、实时、准确、详细地获取车间物理环境的信息。在此基础上,构建相应的生产管理系统的体系结构和集成化运行模式。研究多源异构实时生产信息融合技术和基于组件技术的可重构生产管理系统实现方法。该系统已成功应用于重庆某企业摩托车装配线,取得了良好的应用效果。     

Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services

As the keystones of the personalized manufacturing, the Industrial Internet of Things (IIoT) consolidated with 3D printing pave the path for the era of Industry 4.0 and smart manufacturing. By resembling the age of craft manufacturing, Industry 4.0 expedites the alteration from mass production to mass customization. When distributed 3D printers (3DPs) are shared and collaborated in the IIoT, a promising dynamic, globalized, economical, and time-effective manufacturing environment for customized products will appear. However, the optimum allocation and scheduling of the personalized 3D printing tasks (3DPTs) in the IIoT in a manner that respects the customized attributes submitted for each model while satisfying not only the real-time requirements but also the workload balancing between the distributed 3DPs is an inevitable research challenge that needs further in-depth investigations. Therefore, to address this issue, this paper proposes a real-time green-aware multi-task scheduling architecture for personalized 3DPTs in the IIoT. The proposed architecture is divided into two interconnected folds, namely, allocation and scheduling. A robust online allocation algorithm is proposed to generate the optimal allocation for the 3DPTs. This allocation algorithm takes into consideration meeting precisely the customized user-defined attributes for each submitted 3DPT in the IIoT as well as balancing the workload between the distributed 3DPs simultaneously with improving their energy efficiency. Moreover, meeting the predefined deadline for each submitted 3DPT is among the main objectives of the proposed architecture. Consequently, an adaptive real-time multi-task priority-based scheduling (ARMPS) algorithm has been developed. The built ARMPS algorithm respects both the dynamicity and the real-time requirements of the submitted 3DPTs. A set of performance evaluation tests has been performed to thoroughly investigate the robustness of the proposed algorithm. Simulation results proved the robustness and scalability of the proposed architecture that surpasses its counterpart state-of-the-art architectures, especially in high-load environments.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.