An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

To solve the distributed hybrid flow shop scheduling problem(DHFS)in raw glass manufacturing systems,we investigated an improved hyperplane assisted evolutionary algorithm(IhpaEA).Two objectives are simultaneously considered,namely,the maximum completion time and the total energy consumptions.Firstly,each solution is encoded by a three-dimensional vector,i.e.,factory assignment,scheduling,and machine assignment.Subsequently,an efficient initialization strategy embeds two heuristics are developed,which can increase the diversity of the population.Then,to improve the global search abilities,a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions.Furthermore,a local search heuristic based on three parts encoding is embedded to enhance the searching performance.To enhance the local search abilities,the cooperation of the search operator is designed to obtain better non-dominated solutions.Finally,the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms.The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.

Holonic Manufacturing System:the Holonic Revolution

This work shows how to develop a methodology to support and integrate the concepts and projects of the Holonic Manufacturing System(HMS)with the other areas of the organization for full organizational management success,being a new entrepreneurial management,with support of this new technology in the reduction of costs and increased value added.HMS is in the process of being developed in the so-called"Consortium of the Rich Countries for the 21st Century",which involves governments,companies and universities from the first world countries,developing technology and knowledge related to the Holonic Manufacturing System(HMS).This new concept,under development by the above consortium,will allow the countries that hold this advancement to overcome the challenges of the globalized market and gain even more international competitiveness.

Optimal deadlock avoidance Petri net supervisors for automated manufacturing systems

Deadlock avoidance problems are investigated for automated manufacturing systems with flexible routings. Based on the Petri net models of the systems, this paper proposes, for the first time, the concept of perfect maximal resourcetransition circuits and their saturated states. The concept facilitates the development of system liveness characterization and deadlock avoidance Petri net supervisors. Deadlock is characterized as some perfect maximal resource-transition circuits reaching their saturated states. For a large class of manufacturing systems, which do not contain center resources, the optimal deadlock avoidance Petri net supervisors are presented. For a general manufacturing system, a method is proposed for reducing the system Petri net model so that the reduced model does not contain center resources and, hence, has optimal deadlock avoidance Petri net supervisor. The controlled reduced Petri net model can then be used as the liveness supervisor of the system.

Intelligent Manufacturing Systems in COVID‑19 Pandemic and Beyond:Framework and Impact Assessment

Pandemics like COVID-19 have created a spreading and ever-higher healthy threat to the humans in the manufacturing system which incurs severe disruptions and complex issues to industrial networks.The intelligent manufacturing(IM)systems are promising to create a safe working environment by using the automated manufacturing assets which are monitored by the networked sensors and controlled by the intelligent decision-making algorithms.The relief of the production disruption by IM technologies facilitates the reconnection of the good and service flows in the network,which mitigates the severity of industrial chain disruption.In this study,we create a novel intelligent manufacturing framework for the production recovery under the pandemic and build an assessment model to evaluate the impacts of the IM technologies on industrial networks.Considering the constraints of the IM resources,we formulate an optimization model to schedule the allocation of IM resources according to the mutual market demands and the severity of the pandemic.

Motion/Posture Modeling and Simulation Verification of Physically Handicapped in Manufacturing System Design

Non-obstacle design is critical to tailor physically handicapped workers in manufacturing system. Simultaneous consideration of variability in physically disabled users, machines and environment of the manufacturing system is extremely complex and generally requires modeling of physically handicapped interaction with the system. Most current modeling either concentrates on the task results or functional disability. The integration of physical constraints with task constraints is far more complex because of functional disability and its extended influence on adjacent body parts. A framework is proposed to integrate the two constraints and thus model the specific behavior of the physical handicapped in virtual environment generated by product specifications. Within the framework a simplified model of physical disabled body is constructed, and body motion is generated based on 3 levels of constraints（effecter constraints, kinematics constraints and physical constraints）. The kinematics and dynamic calculations are made and optimized based on the weighting manipulated by the kinematics constraints and dynamic constraints. With object transferring task as example, the model is validated in Jack 6.0. Modelled task motion elements except for squatting and overreaching well matched with captured motion elements. The proposed modeling method can model the complex behavior of the physically handicapped by integrating both task and physical disability constraints.

Social Sensors (S^2ensors): A Kind of Hardware-Software-Integrated Mediators for Social Manufacturing Systems Under Mass Individualization

Currently, little work has been devoted to the mediators and tools for multi-role production interactions in the mass individualization environment. This paper proposes a kind of hardware-software-integrated mediators called social sensors （S2ensors） to facilitate the production interactions among customers, manufacturers, and other stakeholders in the social manufacturing systems （SMS）. The concept, classification, operational logics, and for- malization of S2ensors are clarified. S2ensors collect sub- jective data from physical sensors and objective data from sensory input in mobile Apps, merge them into meaningful information for decision-making, and finally feed the decisions back for reaction and execution. Then, an S2en- sors-Cloud platform is discussed to integrate different S2- ensors to work for SMSs in an autonomous way. A demonstrative case is studied by developing a prototype system and the results show that S2ensors and S2ensors- Cloud platform can assist multi-role stakeholders interact and collaborate for the production tasks. It reveals the mediator-enabled mechanisms and methods for production interactions among stakeholders in SMS.

Configuration Selection for Reconfigurable Manufacturing Systems by Means of Characteristic State Space

The configuration selection for reconfigurable manufacturing systems（RMS） have been tackled in a number of studies by using analytical or simulation models. The simulation models are usually based on fewer assumptions than the analytical models and therefore are more wildly used in modeling complex RMS. But in the absence of an efficient gradient analysis method of the objective function, it is time-consuming in solving large-scale problems by using a simulation model coupled with a meta-heuristics algorithm. In this paper, a new approach by means of characteristic state space is presented to improve the efficiency of the configuration selection for an RMS. First, a characteristic state equation is set up to represent the input and the output resources of each basic activity in an RMS. A production process model in terms of matrix equations is established by iterating the equations of basic activities according to the resource flows. This model introduces the production process into a characteristic state space for further analysis. Second, the properties of the characteristic state space are presented. On the basis of these properties, the configuration selection in an RMS is considered as a path-planning problem, and the gradient of the objective function is computed. Modified simulated annealing（SA） is also presented, in which neighborhood generation is guided by the gradient to accelerate convergence and reduce the run time of the optimization procedure. Finally, several case studies on the configuration selection for some actual reconfigurable assembly job-shops are presented and compared to the classical SA. The comparison shows relatively positive results. This study provides a more efficient configuration selection approach by using the gradient of the objective function and presents the relevant theories on which it is based.

Layout Planning Technology of Cellular Manufacturing System Based on Process Interconnection Analysis

With sustaining change of production mode,layout planning is no longer a thing built once for all.Cellular layout(CL) is becoming a hotspot in the research field of manufacturing system layout.Traditional researches on layout planning are mainly concentrating on aspects of layout arithmetic,style and evaluation,etc.Relatively seldom efforts are paid to CL and its specific problems as cell formation(CF),equipment sharing and CL analysis.Through problem analyzing of layout in cellular manufacturing system(CMS),research approach of cell formation,interactive layout and layout analysis threaded with process interconnection relationship(PIR) is proposed.Typical key technologies in CL like CF technology based on similarity analysis of part processes,interactive visual layout technology,layout evaluation technology founded on PIR analysis and algorithm of cell equipment sharing are put forward.Against the background of one enterprise which encounters problems of low utility of key equipments and disperse material logistic,an example of four-cell layout is given.The CL adjustment and analysis results show that equipment with high level of sharing degree should be disposed around the boundary of its main cell,and be near to other sharing cells as possible; process route should be centralized by all means,so equipment adjustment is to be implemented along direction that route intersection can be decreased; giving consideration to the existence of discrete cell,logistic route and its density should be centralized to cells formed.The proposed research can help improve equipment utility and material logistic efficiency of CL,and can be popularized to other application availably.

Scheduling optimization of task allocation in integrated manufacturing system based on task decomposition

How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.

STOCHASTIC OBJECT-ORIENTED PETRI NETS (SOPNS) AND ITS APPLICATION IN MODELING OF MANUFACTURING SYSTEM RELIABILITY

Object-oriented Petri nets (OPNs) is extended into stochastic object-oriented Petri nets (SOPNs) by associating the OPN of an object with stochastic transitions and introducing stochastic places. The stochastic transition of the SOPNs of a production resources can be used to model its reliability, while the SOPN of a production resource can describe its performance with reliability considered. The SOPN model of a case production system is built to illustrate the relationship between the system's performances and the failures of individual production resources.

A Reconfigurable Manufacturing System for In-site Machining Component with Large Scale

To cope with various unpredictable changes in large scale parts,the concept of reconfigurable manufacturing system (RMS) for machining these components is presented.Considering with large-size space measurement and the fixed-free manufacture mode,an automatically localizing machining method for large scale part is studied in this paper,and the architecture of the RMS for machining large scale parts is proposed.According to the method and structure,the automatically localizing model is established.The theoretical analysis and simulation examples demonstrate the feasibility and validity of the proposed method,and the results indicate that the method is suitable and effective for machining large scale components in significant scientific projects.

Deadlock prevention for flexible manufacturing system

Deadlock must be prevented via the shop controller during the flexible manufacturing system （FMS） performing. Various models have been tried for the analysis and design of shop controller. Petri net is suitable to describe the dynamic behavior of the discrete event system, such as concurrency, conflict and deadlock, however, the verification of the .system behavior needs structure analysis with complex theoretical proof method. Temporal logic model checking has important advantages over traditional theorem prover. It is flatly automatic and can produce possible counter-example which is particularly important in finding subtle error in complex transition systems. In this paper, a new method for the deadlock prevention based on Petri net and Temporal Logic model checking is presented. The specification in the Temporal Logic is expressed according to some result of structure analysis of the Petri net. The model checking is employed to execute the formal verification, which will conduct an exhaustive exploration of all possible behaviors. Finally, an example is presented to demonstrate how the method works.

DESIGN OF RECONFIGURABLE MANUFACTURING SYSTEMS WITH STRONGLY COUPLED NATURE

Today＇s manufacturing cnvironmem forces manufacturing companies to make as many product variations as possible at affordable costs within a short time. Mass customisation is one of most important technologies for companies to achieve their objectives. Efforts to mass customisation should be made on two aspects： （1） To modularize products and make them as less differences as possible; （2） To design manufacturing resources and make them provide as many processes variations as possible. This paper reports our recent work on aspect （2）, i.e. how to design a reconfignrable manufacturing system （RMS） so that it can be competent to accomplish various processes optimally; Reconfignrable robot system （RRS） is taken as an example. RMS design involves architecture design and configuration design, and configuration design is further divided in design analysis and design synthesis. Axiomatic design theory （ADT） is applied to architecture design, the features and issues of RRS configuration design are discussed, automatic modelling method is developed for design analysis, and concurrent design methodology is presented for design synthesis.

Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

This paper presents a new,bi-criteria mixed_integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system.The objective of this model is to minimize the makespan and intercell movements simultaneously,while considering sequence-dependent cell setup times.In the cellular manufacturing systems design and planning,three main steps must be considered,namely cell formation(i.e,piece families and machine grouping),inter and intra-cell layouts,and scheduling issue.Due to the fact that the cellular manufacturing systems problem is NP-Hard,a genetic algorithm as an efficient meta-heuristic method is proposed to solve such a hard problem.Finally,a number of test problems are solved to show the efficiency of the proposed genetic algorithm and the related computational results are compared with the results obtained by the use of an optimization tool.

Load Allocation Research in a Manufacturing System

Based on queuing theory, a nonlinear optimization model is proposed in this paper, which has the service load as its objective function and includes three inequality constraints of Work In Progress （WIP）. A novel transformation of optimization variables is also devised and the constraints are properly combined so as to make this model into a convex one from which the Lagrangian function and the Karurh Kuhn Tucker （KKT） conditions are derived. The interior-point method for convex optimization is presented here as a computationaUy efficient tool. Finally, this model is evaluated on a real example, from which such conclusions are reached that the optimum result can ensure the full utilization of machines and the least amount of WIP in manufacturing systems; the interior-point method needs fewer iterations with significant computational savings and it is possible to make nonlinear and complicated optimization problems convexified so as to obtain the optimum.

Robust Deadlock Avoidance Policy for Automated Manufacturing System With Multiple Unreliable Resources

This work studies the robust deadlock control of automated manufacturing systems with multiple unreliable resources. Our goal is to ensure the continuous production of the jobs that only require reliable resources. To reach this goal, we propose a new modified Banker's algorithm(MBA) to ensure that all resources required by these jobs can be freed. Moreover,a Petri net based deadlock avoidance policy(DAP) is introduced to ensure that all jobs remaining in the system after executing the new MBA can complete their processing smoothly when their required unreliable resources are operational. The new MBA together with the DAP forms a new DAP that is robust to the failures of unreliable resources. Owing to the high permissiveness of the new MBA and the optimality of the DAP, it is tested to be more permissive than state-of-the-art control policies.

EDM COLLABORATIVE MANUFACTURING SYSTEM BASED ON MULTI-AGENT TECHNIQUES

A framework for building EDM collaborative manufacturing system usingmulti-agent technology to support organizations characterized by physically distributed,enterprise-wide, heterogeneous intelligent manufacturing system over Internet is proposed. Expertsystem theory is introduced. Design, manufacturing and technological knowledge are shared usingartificial intelligence and web techniques by EDM-CADagent, EDM-CAMagent and EDM-CAPPagent. Systemstructure, design process, network conditions, realization methods and other key techniques arediscussed. Instances are also introduced to testify feasibility.

Fog-IBDIS:Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems

Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data.