Simulation and graphical animation of advanced manufacturing systems

This paper presents a framework of intelligent manufacturing scheduling and control with specific applications to operations of rail-guided vehicle systems (RGVS). A RGVS control architecture is discussed with a focus on a simulated experiment in operations of the load/unload area of a real industrial flexible manufacturing system (FMS). In the operation stage of a material handling system (MHS), all shop floor data are subject to change as time goes. These data can be collected using a data acquisition device and stored in a dynamic database. The RGVS simulator used in this experimental study is designed to incorporate some possible situations representing existing material handling scenarios in order to evaluate alternative control policies. At the development stage of the controller, all possible combinations of most commonly encountered scenarios such as RGV failures, production schedule changes, machine breakdowns, and rush orders are to be simulated and corresponding results collected. The data are then structured into training data pairs to properly train an artificial neural network. The neural network, trained by using input/output data sets obtained from a number of simulation runs, will then provide control strategy recommendations. At the application stage, whenever an abnormal scenario occurs, a pre-processor will be activated to pre-screen and prepare an input vector for the trained neural network. If such an abnormal scenario falls outside the existing domain of data sets employed to train the neural network, as judged by the MHS supervisory controller, an off-line training module will be activated to eventually update the neural network. The recommended control strategies will be transmitted to the MHS control for real-time execution. If there is no further abnormal event detected, the dynamic data base (DDB) module simply continues to monitor the MHS activities. The proposed MHS control system combines the features of example based neural network technology and simulation modeling for true intelligent, on-line, pseudo real-time control. Not only will the system assure that feasible material handling control actions be taken, but also it will implement better control decisions through continuous learning from experiences captured as the operation time of the MHS accumulates.

An FMS is a typical real-time concurrent system composed of a number of computer-controlled machine tools, automated material handling and storage systems that operate as an integrated system under the control of host computer(s). The growing demand for higher performance and flexibility in these systems and the interlocking factors of concurrency, deadline-driven activities, and real-time decision making pose a significant challenge to FMS design, especially in terms of control and scheduling. A formal engineering approach that helps handle the complexity and dynamics of FMS modeling, design and analysis is needed. A real-time architectural specification (RAS) model and its application in the modeling of flexible manufacturing system (FMS) are presented. RAS combines mature operational and descriptive formal methods, in particular time Petri nets (TPN) and real-time computational tree logic (RTCTL), to form an integrated system model for architectural specification and analysis of real-time concurrent systems such as FMS. The contribution of RAS is twofold: First, it provides a formal system to systematically maintain a strong correlation between (real-time) requirements and design and to verify the conformance of the design to the requirements, which helps enhance traceability and thus to help us to achieve high assurance in design. Second, it offers better scalability in modeling and analysis, which provides an effective way to deal with complexity in the application of formal methods. These two features together make RAS a suitable model for the design of FMS.

SUMMARY The material handling system of a flexible manufacturing system (FMS)consists of load and unload mechanisms, transfer mechanisms, and internal storage facilities, each of which may be configured in many different ways. In addition, many options are available for dispatching work-parts between work-stations. The inherent flexibility of an FMS affords the opportunity for enhanced system efficiency, yet it also makes the analysis and design of the system extraordinarily complex and difficult. This paper develops a combined process interaction-discrete event simulation model to analyse the material handling and dispatching system of an FMS. The model is implemented in SLAM (Simulation Language for Alternative Modeling)The SLAM model is applied to an example FMS and is shown to be an effective tool for designing the material handling system and for selecting the appropriate work-part dispatching option. It provides greater flexibility in both modelling and output analysis than conventional process inte...

Flexible manufacturing systems (FMS) consists of a set of numerically controlled machines, material handling systems (MHS), information system and service centres all working towards a common production schedule goal being the achievement of economic product manufacture in short period of time[1]. An FMS is highly versatile production system capable of performing many different operations given the proper tooling, fixtures and materials. Another feature of this system is the short switching time between different operations assigned to its individual workstations. An FMS to function effectively must have an on-line control system capable of reacting quickly to any problem occurring in the system by providing decisions to deal with the problem in a short period of time. The FMS control system thus needs to receive accurate and reliable information from the shop floor. The operational control of an FMS in general involves accessing a large set of static and dynamic data, and requires complex control algorithms. This paper presents a control system developed for real-time operational control of an FMS. The system is organised hierarchically, each level responsible for making decisions according to the operational needs and the time criticality of the particular level. The overall aim of the control system is to optimise FMS performance while meeting the production requirements.

This paper describes the ways of using program tools for design and simulation of flexible manufacturing systems (FMS) for the purpose of teaching the undergraduate course Flexible Manufacturing Systems. The concept of the course is based on the synergism of theoretical knowledge and practical hands-on experience gained by studying numerous problem cases, using common tools for description of FMS (Petri nets, max-plus algebra and matrix modeling) and by working with more specialized program tools for FMS design and simulation. An emphasis is given to the description o the program tool FlexMan that enables elegant setting of an FMS configuration, supports automatic generation of a matrix-based FMS description and consequently, enables effective off-site design and simulation of the FMS by using virtual reality models and Internet-related technologies.

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.

Scheduling of part and tools without considering material handling devices may result into non-implementable results as the material handling system constraints affect the overall scheduling. In this paper, an attempt has been made for the simultaneous scheduling of part, tool and two automated guided vehicles (AGVs) in a flexible manufacturing system which has identical machines with no on-board tool magazine. All the tools are stored in a common tool magazine and are shared among different machines through a material handling system. Each tool type is single in number. The problem has two interrelated problems; one as loading and scheduling of part and tool and other as scheduling of AGVs. Two heuristics namely priority dispatching rules (PDRs) and modified genetic algorithm (MGA) with three parent crossover are used to find the optimal solution to the integrated scheduling problem. Both PDRs and MGA use an algorithm based on the concept of Giffler and Thompson procedure and a heuristic approach to generate the active feasible schedule. The performance of the two proposed algorithm is compared for the results of makespan. It was found that the MGA gives better results.

Flexible manufacturing system (FMS) was defined as highly automated group technology machine cell, consisting of a group of processing stations interconnected by an automated material handling and storage system, and controlled by an integrated computer system. FMS can produce parts or products are in the mid-volume, mid-variety production range. The layout system in FMS is an important criterion to design the FMS system to produce a part or product. This facility layout of an FMS involves the positioning of cells within given boundaries, so as to minimize the total projected travel time between cells. Defining the layout includes specifying the spatial coordinates of each cell, its orientation in either a horizontal or vertical position, and the location of its load or unloads point. There are many types of FMS layout such as In-line, loop ladder and robot centered cell layout. The research is concentrating on the design and optimization FMS layout. The final conclusion can be summarized that the objective to design and optimisation of FMS layout for this study is successful because the FMS In-line layout is the best layout based on effective time and cost using ARENA simulation software.

This paper reports on a development of feature-focused dynamic routing policy and its evaluation in a flexible manufacturing systems (FMS) simulation framework. The dynamic policy is based on an integrative methodology in which process planning system IMPlanner is integrated with FMS simulation module. IMPlanner’s rule-based system process selection system performs knowledge-intensive task of generating alternative processing options for each feature for parts in production plan. Generated alternative routings (process plan network) for each part are utilized in the FMS simulation module such that routing decision in FMS are made on periodic intervals by considering alternative processes for each feature and making decision based on the current system status and performance. The proposed framework has been evaluated in an experimental FMS simulation module, implemented in Arena, in which two performance criteria, machine utilization and WIP, were used to make routing decisions. The feature-focused approach is compared with traditional static decision-making, and its improved performance is demonstrated.

The paper deals with the problem of the development of 3D graphic representation and simulation of flexible manufacturing systems. A 3D graphic model of a plant can improve the work performed in different control processes during the system life cycle, such as commissioning, monitoring and maintenance. These aspects become more relevant dealing with complex systems. Furthermore, for flexible manufacturing systems, mechanisms to manage the system modularity in the development of the 3D graphic representations are needed. A key factor which is bringing visibility to 3D techniques is the technical improvement of personal computer allowing to have relatively cheap, high graphical performances. Consequently, during last few years, several 3D visualization tools appeared on the marked. Within the paper, two existing simulation tools, 3D create and day dream daemon, are analyzed and compared. Both, have successively been used for the development of a simulation environment for the validation of control functions of a real manufacturing system.

A flexible manufacturing system (FMS) is an integrated system of numerically-controlled machine tools and an automated material handling system under the supervision and control of an on-line host computer (Draper, 1984). An FMS can simultaneously process a number of different part-types of medium batch sizes in a random manner. FMS is fast becoming an appropriate production method for mid-volume batch manufacturing. This is due to the fact that FMS has dual advantages over conventional production methods, especially the job shop production system. The advantages are, namely, higher production efficiency and greater flexibility with regard to part-type variety and production.

Flexible Manufacturing Systems (FMS) provide capabilities to accommodate flexibility requirements in production. Such system due to the levels of flexibility are complex to reconfigure optimally. Simulation is one of the tools that provide a platform for supporting the FMS reconfiguration without breaking optimal production set-up. For the simulation to be successful it is important to appropriately design the simulated environment as well as provide quality data inputs. As FMS are usually driven by production line controllers (PLC), the behaviour of elements in the system can be difficult to be fully understood. The quality of data fed in to the simulation affects the quality of outputs which in consequence translates to the trust that the simulation is reliable source of analysis. This paper presents the approach of data collection from the shop floor aiding FMS simulation in scenarios where it is difficult to understand the system behaviour. Also, case study of a manual handling robot in the FMS is presented to demonstrate obtaining quality data from the production shop floor.

A flexible manufacturing system (FMS) is a highly automated, complex system. Simulation is a well-proven method to design or analyze an FMS. Deployment of a radio frequency identification (RFID) system in FMS produces large volumes of RFID data streams, which provide valuable information to improve the operation of FMS. Different frameworks are presented in this paper regarding the use of RFID data streams in an FMS simulation. Simulations are performed with AutoMod software. Related technical details are also presented. The paper’s structure is complied as the following steps: step 1, the introduction of AMHS, FMS and AutoMod; step 2, FMS simulation using AutoMod software; step 3, simulation frameworks driven by RFID data streams; step 4, conclusion.

Software developers often meet the problem of creating new components of an application that someone probably has already produced. Without having effective reuse tools usually it is more natural to create new components from scratch than to seek for useful elements in other programmes and/or systems.In the field of simulation of Flexible Manufacturing Systems (FMS) this issue often occurs, when different systems with some similar features have to be managed. FMSs are highly automated production systems with minimal human control. FMS usually consist Flexible Manufacturing Cells (FMCs). An FMC is considered as a group of one or more machine tools, robots, adjacent mechanical elements, tool and fixture supporting workstations and an internal transport system.The components of different FMSs and FMCs are the same type of machine tools, transfer equipment, etc. In the relevant aspects they usually differ from each other only in their amounts and working parameters. This fact itself breeds the idea of reuse of FMS and FMC simulation model elements.Simulation can be used for analysing the behaviour of an existing or would-be system. It is useful for calculating utilisation statistics, finding bottlenecks, pointing out scheduling errors, and even for creating manufacturing schedules. Simulation is a very effective technique for dynamic analysis, but it is short of optimising ability. A satisfactory solution can be obtained through modelling and simulating alternative approaches with iteration.There are many concepts, object classes, and lines of source code created, that could be reused in the new applications. Usage of reuse methodology and practice will reduce our efforts in developing new systems.In this paper the reuse of the components of simulation models in a hybrid simulation and scheduling system for new simulation applications will be introduced. This simulation-scheduler system has been implemented as a combination of a traditional simulation language (SIMAN/Cinema) and an intelligent expert environment (G2). The design methodology used for model development is based on the object-oriented Rational Rose CASE tool and on a use-case model.

Material handling in manufacturing systems is becoming easier as the automated machine technology is improved. Nowadays, most of the research aims at increasing the flexibility and improving the performance of the automatic guided vehicle (AGV). In this paper, designed and made AGV in the Industrial Control Laboratory in Royce Lab at the University of Manchester Institute of Science and Technology will be presented. For controlling the navigation of the AGV, a newly developed controller integrated fuzzy logic with programmable logic controller will be used. By using integrated fuzzy logic controller with programmable logic controller (IFLPLC), the flexibility of AGV will be increased and we achieved great advantages, which can be used in future. Since this AGV uses programmable logic controller and fuzzy logic controllers together, then it will be useful for factories which implement flexible manufacturing system (FMS). Online maintenance and sending the commands to other machines from AGV and so on are the advantages that can be used in FMS. The aim of this paper is to propose the practical example of IFLPLC. The designed AGV is able to reach the target. Further researches are required for collision avoidance and selecting the best way in FMS