Configure and Parameters' Optimization for Sheet Metal Flexible Manufacturing System Based on Simulation

Flexibility of flexible manufacturing systems (FMSs) has been considered as an effective tool to compete in the present manufacturing environment. Enormous research efforts have been made to harness the benefits of flexibility through superior control strategies. While modeling flexibility and control strategies, researchers have mostly assumed an information system that can provide real-time control. Literature qualitatively reports that the real-time control can be highly capital intensive and difficult to achieve. This paper focuses on FMS operating under review-period (RP)-based control and presents a combined study of routing flexibility (RF), control strategies, and information system under Taguchi's method using simulation. RP-based control for FMS has been compared with real-time control. This paper contributes an approach for the decision maker to study the performance of an FMS operating under RP control and to identify the periodicity (time interval) of RP that will not deteriorate its performance in comparison to real-time control. It also helps the decision maker to reach a tradeoff between RP-based control and real-time control. The results show that RP-based control can be effectively implemented on an FMS having lower RF level. RP-based control can outperform real-time control with a superior control strategy and smaller RP size. The results under Taguchi's method suggest that the RF and control strategy should have maximum relative percentage contributions in FMS performance, whereas contribution of the RP (information system) should be minimum. Increasing the relative percentage contribution of the information system may deteriorate the performance of FMS. The information system is needed as a catalyst to facilitate the contributions of other factors in improving the FMS performance and not its own contribution. © 2007 IEEE.

Research has found that while flexibility is purported to be a prime advantage of flexible manufacturing systems (FMS), it has not yet become a major competitive priority for American and European manufacturers. Moreover, many installed systems either are not very flexible or do not use the available flexibility to the best advantages. We suspect that inappropriate application of the conventional production management concepts to FMS is one of the major factors that hinders current FMSs from capturing their key advantage: flexibility. This paper takes a first step to examine why the conventional job shop loading and fixed routeing concepts fail to capture the flexibility of FMS. Once problems are identified we suggest ways to exploit the FMS flexibility. We further conduct an experiment to investigate FMS performance under different manufacturing policies and operating conditions. The results indicate that the performance of an FMS greatly diminishes when the conventional job shop loading and fixed routeing concepts are applied. This is because the inherent flexibility of the FMS is not fully utilized. Moreover, the operating conditions such as tooling duplication levels and operation processing time variation could also significantly affect the FMS performance.

Discrete event simulation (DES) is a well-recognized technique in evaluating the performance of flexible manufacturing systems (FMS). FMSs lend themselves to such a method because of the inherent complex element interactions, the queueing principles involved and the changes of state that occur within them. In particular, to attempt to rely on intuition, experience or static optimization alone can lead only to a limited FMS design solution. DES allows the analyst to recognize the dynamics of FMS while also allowing him to interact with the system model in such a way that changes in, say, layout, system design and operation strategies (scheduling, routing, etc.) may be assessed. Hence, “what if?” scenarios are orchestrated [1]. The analyst not only is able to make changes to the simulation rules but also is capable of altering data records and displays.

Flexible manufacturing systems (FMS): Current diffusion and main advantages

There are generally ten (10) common forms of flexibilities involved in Flexible Manufacturing System (FMS), and material handling flexibility is one of them. Implementing an FMS with many Automated Guided Vehicles (AGV) allows the system to increase productivity by choosing the appropriate AGV to handle transportation for processing parts between different workstations. In this paper, we study the effect of increasing the number of AGVs in the system on the performances of Flexible Manufacturing System. The performances considered are Productivity, Makespan, Waiting for Process Time and Waiting for Transport Time.

This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.

A new FMS simulator with object-oriented-programming techniques

With the advance of research, design and operation of flexible manufacturing systems (FMS) which are designed to produce a variety of different part types with high machine utilization, short lead times and little work-in-progress inventory, new requirements like high modeling efficiency, high model validity and credibility and effectiveness and correct analysis of results are defined. Simulation is an efficient tool to verify design concepts, to select machinery, to evaluate alternative configurations and to test system control strategies of an FMS, etc. This paper presents a useful tool for simulating the behavior of random FMSs. This simulator is used for real time alternative routing selection based on a group of metaheuristics principles which include in particular simulated annealing (SA), genetic algorithm (GA), taboo search (TS), ant colony algorithms (ACO), particle swarm optimization (PSO) and electromagnetism like method (EM). This software is also can used for performance and sensitivity analysis of theses techniques jugged by the production rate, machines and material handling utilization rate, the cycle time and the work in process.

In this paper, we use Petri nets to analyse flexible manufacturing systems (FMS) to achieve the following objectives. The first objective is to derive a theorem for checking the buffer overflows in an FMS. The second objective is to schedule the earliest starting times for a sequence of operations in an FMS by the timed Petri net. The FMS may involve many part types, machines, pallets and so on. The third objective is to detect the deadlock phenomenon by a corrected algorithm of the P-invariant theory for Petri nets. This method will enable us to know whether there exists any deadlock in an FMS. The algorithm is implemented in the Turbo Pascal language. The fourth objective is to search for the optimal scheduling rule in an FMS. The search procedure is also implemented in the Turbo Pascal language¨ The optimal machine scheduling rule can result in a better throughput.

The optimal controller synthesis problem for discrete event control of a flexible manufacturing system (FMS) is presented in this paper. To explicitly formulate concurrent activities, multiple resources sharing, precedence constraints and dynamic routing in FMS operation, we adopt timed (place) Petri nets for problem representation. The A* based heuristic search algorithm is proposed to search for an optimal event sequence to achieve minimum-time and deadlock-free discrete event control. Based on the obtained event-driven sequence, we use two levels of specification to design the optimal sequence controller. The coordination control level consists of synchronization and parallelism of different sub-systems and is specified by decision-free Petri nets (marked graphs). The local control level consists of running elementary sequences for subsystems and is specified by sequential function charts.

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) 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.

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.

In this paper, we present an analytical method to evaluate the quality performance of flexible manufacturing systems with batch operations. By using a Markov chain model, a closed formula to quantify the probability of producing a good part is derived and nonmonotonic properties in quality are investigated.

The purpose of this paper is to investigate the intensity of variables of the flexible manufacturing system (FMS) which affect flexibility in FMS. The study was performed by conducting a cross–sectional survey within manufacturing firms in India through exploratory factor analysis (EFA) and graph theory and matrix approach (GTMA). By performing EFA, factor structure is identified whereas GTMA quantified the factor structure of a set of observed variables. GTMA is a multiple attribute decision making (MADM) methodology used to find the intensity of variables in an organisation. In this paper, EFA is applied to extract the factors in FMS by The Statistical Package for Social Sciences (SPSS 20) software and evaluation of intensity of variables affecting flexibility in FMS by GTMA. Fifteen variables are identified through literature, and four factors extracted, which involves the flexibility of FMS in production flexibility, machine flexibility, product flexibility, and volume flexibility.