Control Of Flexible Manufacturing Systems Research Articles

The state of the art in intelligent real-time FMS control: a comprehensive survey

Flexible manufacturing systems (FMS) have been developed with the hope that they would provide a means to tackle a threefold challenge — better quality, lower cost and shorter lead times — by integrating machine tools, robots, material handling and storage systems, and computers. Control of the integrated system presented a new set of problems as well as challenges, which have been receiving considerable attention from the academic community as well as from industrial system users. Intelligent control, which involves using computers to assist in decision making at various stages of the control process, has been advocated by many researchers as a possible avenue to reach a solution to these problems. This paper provides a review of the state of the art in intelligent control of FMS, in an attempt to supplement earlier general reviews via a more focused perspective. The principles of several techniques, namely simulation, knowledge based, example based, petri nets, and hybrid approaches are briefly introduced, and publications are reviewed, followed by discussions ontheir potential. Suggestions for further research and development are also enumerated.

An object-oriented model for FMS control

The flexible manufacturing system (FMS) is a distributed network of heterogeneous programmable manufacturing machinery, such as assembly lines and numerically controlled machines. Despite these interconnected, programmable hardware elements, the success of building a truly flexible manufacturing system has been limited so far, owing to the lack of flexibility in its control software layer. In integrating heterogeneous machinery, many existing FMS control software systems depend structurally on specific machinery and job-scheduling strategies, and thus it is difficult to incorporate new developments in FMS organization and operational requirements. In searching for an open architecture for the FMS control software system, this paper presents an object-oriented FMS data model. Among others, it represents each physical cluster of related machinery (called a flexible manufacturing cell) as an object. To facilitate the integration of heterogeneous physical cells, such cell objects share a common protocol of interacting with the main control process through inheritance from the abstract cell class. Other related physical and abstract entities in FMS are also modelled as objects, with their similarity and difference captured in inheritance hierarchies. To verify the proposed approach experimentally, a prototype FMS control software system named FREE (FMS Runtime Executive Environment) has been implemented on top of a commercial object-oriented database system.

Application of manufacturing message specification for flexible manufacturing system control

This paper describes the development of the internal communication in a Flexible Manufacturing System (FMS), using Manufacturing Message Specification (MMS) and based on a Manufacturing Automation Protocol (MAP) network. Because the machines, of which the FMS consists, had few possibilities for communication with external devices, effort had to be put into making machine-specific interfaces between the NC machines and the MAP network. The interfaces were implemented by developing applications that translate MMS messages into machine-specific data and vice versa. The results of the project show that MMS can be used for the control of manufacturing machines that were not designed for use in a network for flexible manufacturing. MMS is not yet widely used. Therefore this paper is meant to demonstrate that MMS can easily be put into practice using relatively simple and off-the-shelf equipment.

Intelligent Control of Manufacturing Systems Using Open Communication

Knowledge based or Intelligent Control of Flexible Manufacturing Systems (FMS) means a continuous or frequent observation and evaluation of the status and condition of the system performance, decision making based on the evaluation results and on pre-defined knowledge, and then the operation according to the decisions. This kind of process is applied if any kind of disturbance or irregularity happens, what is rather common in the case of highly sophisticated, complex systems (Smith, et al., 1992). Our paper deals with the control of flexible manufacturing systems using programs based on a real-time expert system environment taking into consideration the requirements of openness of up-to-date systems.

Composition of Conflict-Free Petri Net Models for Control of Flexible Manufacturing Systems

An approach using Petri nets to modelling and control of flexible manufacturing systems (FMS) is worked out in the paper. A class of Petri nets suitable for the modelling of FMS and for the design of their control is formulated. In the Petri net models not only conflicts due to mutual exclusions of the shared system resources but conflicts of branching operations are analyzed and solved, as well. Models of FMS suitable for system performance analysis and control design are built up of a set of elementary building blocks proposed in the paper. Analysis, modelling method and control design is developed on the background of a pilot experimental FMS developed at the Helsinki University of Technology.

Hierarchical Control Design and Verification with Adaptive Interpreted Petri Nets

In this contribution, logic controller design and verification methods are applied to Flexible Manufacturing Systems (FMS). Logic control design methods as well as corresponding verification methods are extended to meet the demand of high flexibility in FMS control By introduction of adaptive interpreted Petri nets for FMS control, a hierarchical controller approach results. The controller hierarchies correspond to the control levels in FMS. Methods for FMS controller verification and implementation are outlined.

System approach to design generic software for real-time control of flexible manufacturing systems

Potentially, flexible manufacturing systems (FMSs) possess the ability to attain efficiency and versatility in small batches, variable mix production. However, the FMS potentiality is not yet fully used, because of the high cost of specific control software. Hence, a generic software, usable in an arbitrary FMS, producing an arbitrary part mix, appears as an important means to minimize programming and re-programming effort. In this context, modeling and control problem formalization emerge as a main issue. In this paper the authors refer to Zeigler's system-theoretic formalism for modeling the dynamics of a generic FMS at the job release and job how levels. The authors' discrete event dynamic system (DEDS) model establishes an abstract comprehensive framework for developing a generic control software at such levels. The model is able to represent both hardware/software components of the FMS and processing activity plans with a common "language". A modular structure underlies the description of both the shop-floor dynamics and control rules. So, the software architecture emerges as a direct consequence of the proposed approach and consists of three main parts: the system state knowledge base, the job release manager and the job flow manager.

A neural network model for on-line control of flexible manufacturing systems

This research investigates the potential of using a neural network approach in real-time control of flexible manufacturing systems. A hierarchical manufacturing controller, consisting of two neural network structures, is proposed. The first neural system participates in the feasibility analysis, and the other, at the lower level, in the process of dispatching and control. At the first level, a Sigma-Pi type of connection is used to translate work-in-process (WIP) move requests into directed arcs. Through a filter scheme, infeasible arcs are identified and eliminated from further consideration. At the second level, a modified Hopfield-Tank model is developed to determine the correct moves. Its goal is to deliver the right WIP to the right workstation, and process it at the right time. An example is used throughout the paper to illustrate the architecture developed. This two-phase control procedure provides adaptability, speed, and good solution quality which are important for real-time control of flexible ...

Towards Intelligent and Open Control of Manufacturing Systems

Knowledge based or Intelligent Control of Flexible Manufacturing Systems (FMS) means a continuous or frequent observation and evaluation of the status and condition of the system performance, decision making based on the evaluation results and on pre-defined knowledge, and then the operation according to the decisions. In the case of so-called normal operation, which runs according to the given schedules there is no special need for intelligence, neither for interactions to modify the operation, but the above given procedure may help (Smith, et al. 1992) if any kind of disturbance or irregularity happens, what is rather common in the case of highly sophisticated, complex systems. Our paper deals with the control of flexible manufacturing systems using programs based on a Realtime expert system environment taking into consideration the requirements of openness of up-to-date systems.

Supervision Based Control for Flexible Manufacturing Systems

Applying the supervision at the flexible manufacturing systems (FMSs) is not only a control method, but also a design one. The FMS is seen as a discrete event system which has to be controlled implementing some required constraints. The system is structured on two levels. The first level includes the physical equipments and some very simple software routines. It is considered to be modeled by controlled Petri nets. The second level is the supervisor which implEments the user’s desired constraints on the behavior of the first level.

Knowledge Based Control of Manufacturing Systems Using Open Communication

A rather simplified definition of Intelligent Control of Flexible Manufacturing Systems (FMS) means a continuous or frequent observation and evaluation of the status and condition of the system performance, decision making based on the evaluation results and on pre-defined knowledge, and then the operation according to the decisions. In the case of so-called normal operation, which runs according to the given schedules there is no special need for intelligence, neither for interactions to modify the operation, but the above given procedure may help (Smith, et al.,1992) if any kind of disturbance or irregularity happens, what is rather common in the case of highly sophisticated, complex systems. Our paper deals with the control of flexible manufacturing systems using programs based on a Real-time expert system environment taking into consideration the requirements of openness of up-to-date systems.

Dynamic control of flexible manufacturing systems

Flexible manufacturing systems (FMSs) are a relatively new technological and organisational approach to helping companies respond to real-time marketing conditions for their production. Under a proposal of the National Bureau of Standards the FMSs are subdivided into virtual manufacturing cells in a dynamic manner, on the basis of group technology. A method of dynamic optimisation for the design of manufacturing processes, capacity balancing and checking, and also production scheduling or rescheduling in virtual manufacturing cells is described. It can be used during real-time production control in FMSs. Bellman's method of dynamic programming is used for the optimisation of manufacturing process design and execution. The model of manufacturing processes optimisation has been built in accordance with assumptions of Bellman's method. This model consists of a synthesised network of alternative manufacturing processes, a description of its components and an appropriate formula of criterion function. It can be considered as a “production rule base” of an expert system.

Design and implementation methodology based on Petri net formalism of flexible manufacturing systems control

Abstract This paper presents an aided design methodology of flexible manufacturing system control with a view to industrial implementation. The approach considered through a modelling phase, the a validation phase by simulation and finally a distributed implementation phase. In the modelling phase, based on the Petri net formalism, we demonstrate the hierarchical aspect which separates the part flow control and the product resource control. In addition, we emphasize generic aspects which allow us to use an object-oriented approach. These aspects and the set of modelled objects are directly used in the simulation and production phases after an automatic translation in an implementation language (ADA in our case). In consequence, the distributed location for those two phases is well facilitated from the viewpoint of the approach used.

Hierarchical and dynamic production control in flexible manufacturing systems

Job shop control involves directing the flow of products through the manufacturing system. Centralized and decentralized approaches have been proposed. This paper introduces first a mix approach in the organization of real time control level in flexible manufacturing systems: a hierarchical organization permits coordination of distributed decision centres and heterarchical consideration confers a partial autonomy to sub-level decision centres. Second, corrective actions against unexpected events are introduced to maintain as far as possible the forecasted scheduling.

Scheduling and control of flexible manufacturing systems: a critical review

Abstract Flexible manufacturing systems (FMS) are distinguished by the use of computer control in place of the hard automation usually found in transfer lines. The high investment required for an FMS and the potential of FMS as a strategic competitive tool make it attractive to engage in research in this area. This paper presents a review of literature concerning the operations aspect of FMS. Articles emphasizing many methodological perspectives are critically reviewed. The review is carried out from multiple viewpoints. Future research directions are suggested.

A Hierarchical Model for Control of Flexible Manufacturing Systems

Flexible Manufacturing Systems (FMSs) are usually composed of general purpose machines with automatic tool changing capability and integrated material handling. The complexity of FMSs requires sophisticated control. In this paper we present a four-level control hierarchy and outline computationally feasible control algorithms for each level. The top level is concerned with the choice of part types and volumes to be assigned to the FMS over the next several months. The second level plans daily or shift production. Production levels are set and tools are allocated to machines so as to minimize holding and shortage costs. Various FMS environments are presented. The third level determines process routes for each part type in order to minimize material handling. Additional tools are loaded on machines when possible to maximize alternate routeing. Routes are then assigned to parts to minimize workload assignment, and these are used by level four for actual routeing, sequencing and material handling path control. The level three model is formulated as a linear program, and heuristics are used for level four. An example is provided to illustrate the completeness of the decision hierarchy and the relationships between levels.

A Hierarchical Model for Control of Flexible Manufacturing Systems

Flexible Manufacturing Systems (FMSs) are usually composed of general purpose machines with automatic tool changing capability and integrated material handling. The complexity of FMSs requires sophisticated control. In this paper we present a four-level control hierarchy and outline computationally feasible control algorithms for each level. The top level is concerned with the choice of part types and volumes to be assigned to the FMS over the next several months. The second level plans daily or shift production. Production levels are set and tools are allocated to machines so as to minimize holding and shortage costs. Various FMS environments are presented. The third level determines process routes for each part type in order to minimize material handling. Additional tools are loaded on machines when possible to maximize alternate routeing. Routes are then assigned to parts to minimize workload assignment, and these are used by level four for actual routeing, sequencing and material handling path control. The level three model is formulated as a linear program, and heuristics are used for level four. An example is provided to illustrate the completeness of the decision hierarchy and the relationships between levels.

A Hierarchical Model for Control of Flexible Manufacturing Systems

A Hierarchical Model for Control of Flexible Manufacturing Systems

Application of artificial intelligence to problems in advanced manufacturing systems

Flexible manufacturing systems (FMS) are used worldwide for the production of parts in CIM systems. The design and operation of an FMS can be achieved only by the teamwork of several experts using different computer programs. Because of the complexity and diversity of the problems to be solved, traditional programming can be used only if combined with artificial intelligence techniques, such as expert and knowledge-based systems. This paper deals with the application of expert systems to assist in quality control and the simulation and control of FMSs. Well known traditional simulation (Cinema/SIMAN) and networking (MAP) packages are combined with high performance expert systems (ALL-EX, G2) to provide acceptable solutions. This paper reports on recent work where rapid prototype rograms are under test, with the ultimate goal of providing industrially applicable programs.

Dynamic Operations Control in Flexible Manufacturing Systems (FMS)

The problem of combining efficiency and flexibility is the basic challenge faced by flexible manufacturing systems in operating state. A dynamic method for operations control in FMS with job-shop production is proposed using graph theory. Appropriate corrective actions are developed to react to perturbation occuring during production with respect to product due-date. Different states of the manufacturing system are qualified and heuristic programming is introduced to maintain production not far away from outline estimation.