Simulation of Tool Change Policies in a Flexible Manufacturing Cell
Fawaz Abdulmalek, Mehmet Savsar and Majid Aldaihani
KuwaitUniversity
College of Engineering & Petroleum
Department of Industrial and Management Systems Engineering
P.O. Box 5969Safat13060Kuwait
Abstract:- In this study, a simulation model is developed to analyze the operation of a flexible manufacturing cell (FMC), which consists of two production machines served by a part handling robot. The simulation model is validated with available exact solutions for the selected cell operations. The model is then utilized to analyze the FMC under other operational characteristics, for which analytical solutions are not possible. In particular, we analyze the effects of robot speed, machining speeds, and varying pallet capacities on cell production rate as well as machine and robot utilizations under two different tool change policies and different equipment reliability characteristics.
Keywords:-Flexible ManufacturingCells, Simulation, Production Planning, Tool Change Scheduling
- Introduction
One of the problems facing today’s production companies is the rapid change in customerdemand and the requirement to produce customized products. It has become necessary to automatethe manufacturing systems to cope with the problem of changing demand. Furthermore, in order to meet the demand for customized products and to reduce production lot sizes, the industry has adapted new techniques and production concepts by introducing flexibility into production machines so that variety of products can be manufactured on the same equipment. As indicated by Chan and Bedworth (1990), at present, the most feasible approach for automating the job shop process is through flexible manufacturing cells (FMC), which require lower investment, less risk, and also satisfy many of the benefits gained through flexible manufacturing systems (FMS), which are very expensive and generally require investments in millions of dollars. An FMC consists of a robot, one or more flexible machines including inspection, and an external material handling system such as an automated pallet for moving blanks and finished parts into and out of the cell. The robot is utilized for internal material handling which includes machine loading and unloading.
The FMC is capable of doing different operations on a variety of parts, which usually form a part family with selection by a group technology approach. The cell performance depends on several operational and system characteristics, which include, part scheduling, robot, machine and pallet characteristics. Most of the researches related to operational characteristics of FMC are directed to the scheduling aspects. Scheduling algorithms are used to determine the sequence of parts, which are continuously introduced to the cell. Chan and Bedworth (1990), Seidman (1987) Hutchinson et al.(1991) have developed models for static and dynamic scheduling in FMC. However, system characteristics, such as configuration, design, and operation of an FMC, have significant effect on its performance. Machining rate, pallet capacity, robot speed and pallet speed are important system characteristics affecting FMC performance. Several models have been developed in relation to the effects of different parameters on system performance. Henneke and Choi (1990) and Savsar and Cogun (1993) have presented stochastic models for evaluating system performance with respect to system configuration and component speeds, such as machining rate, robot and pallet speeds. Savsar (2000) and Koulamas (1992)have looked into the reliability and maintenance aspects and presented stochastic models for the FMC, which operate under stochastic environment with tool failure and replacement consideration. They have developed Markov models to study the effects of tool failures on system performance.
In this study, we present a simulation model to analyze the operation of a FMC consisting of two production machines served by a part handling robot. The simulation model is tested with available exact analytical solutions for the selected cell operations. The model is then used to analyze the FMC under other operational characteristics, for which no analytical solutions are possible. In particular, we analyze the effects of robot speed, machining speeds, and varying pallet capacities on cell production rate as well as machine and robot utilizations under two different tool change policies, namely the fixed-time tool change policy and fixed completed-part tool change policy on both machines.
- The FMC Operation
The FMC system considered in this study is illustrated in Figure 1. An automated pallet handling system delivers n blanks consisting of different parts into the cell. The robot reaches to the pallet, grips a blank; moves to the first machine; and loads the blank. While the first machine starts operation on the part, the robot reaches the pallet, grips the second part; moves to the second machine; and loads it to the machine. Next, robot reaches to the machine which finishes its operation first, unloads the finished part and loads a new part. The loading/unloading operation continues in this way with the preference given to the machine which finishes its operation first. After machining operationsof all parts on the pallet are completed, the pallet with n finished parts moves out and a new pallet with n blanks are delivered into the cell automatically. Due to part variety on the pallet,processing times as well as loading/unloading times are random, which present a complication in studying and modeling the cell performance. Savsar and Aldaihani (2004) have developed a stochastic model for a two-machine cell and obtained exact numerical solutions for fixed n. Exact solutions are applicable to the analysis of limited cases of FMC operations withcertain characteristics. Therefore, simulation modeling is preferred since FMC can be simulated under a variety of realistic conditions without any assumptions. In the next sections we present a simulation model and several experiments to investigate cell behavior.
Machine 1
Pallet In/Out
Machine 2
Robot
Figure 1. A Flexible Manufacturing Cell
- Simulation Modeling
The FMC simulation model is based on System Modeling Corporation’s ARENA 5 package, which offers flexibility in modeling and analyzing the system with stochastic behavior and other realistic characteristics. Figure 2 shows a small portion of a larger simulation network modelfor illustrative purposes. In this model the robot and the machines are represented as resources and the parts as entities. Two create modulesare used to model the pallet handling system. The “CREAT FIRST PALLET” module is used to model the movement of the first pallet into the system. Once n parts are processed, a signal is sent from Pallet Movement Signal module to the “CREAT NEXT PALLET” module to progress the next pallet into the system which will experience a Delay of EXPO (0.5) representing the handling time to move the pallet into the cell with exponentially distributed time with a mean of 0.5 minutes. Parts that come into the system will either advance to “SEIZE ROBOT FOR LOADING MC1” or “SEIZE ROBOT FOR LOADING MC2” module. The model is relatively big to fit into this paper. Only the movement of parts through machine 1 which is the same for machine 2, is illustrated here.
At the “SEIZE ROBOT FOR LOADING MC1” module, robot and machine 1 are seized and the loading of the part to the machine takes place with an exponential delay time of [EXPO (0.25)]. Next the part advances to “RELEASE ROBOT FROM MC1” module to free the robot from loading machine 1 for other activities such as unloading/loading machine 2. After releasing the robot, the part will go through another delay of EXPO (1) representing the time that the part will take to be processed on machine 1; then the robot is requested again for unloading at “SEIZE ROBOT TO UNLOAD MC1”. After the robot unloads the part at another delay, EXPO(O.25), the machine and the robot are freed. Then a check is made to see if n parts are completed by waiting to batch n parts at “PALLET DEPARTURE”. Simulation is run for an 8 hour-shift for 100 days (replications) in each experiment carried out.
Figure 2. The ARENA simulation model for the FMC
4. Case Problemsand Results
A case problem has been selected and several experiments are carried out using the simulation model. Cell performance measures are determined under varying operating conditions including tool change policies and down times. In order to test and verify the simulation model, we started with a case problem for which exact numerical solutions have been obtained by Savsar and Aldaihani (2004) using a stochastic Markov chain model for the FMC. The problem considered has the following cell parameters, which are assumed mean values for the exponential distribution that describe the related operations: Operation time per part = 1 time unit, for machines 1 and 2; Robot loading time per part = 0.25 time units, for machines 1 and 2; Robot unloading time per part = 0.25 time units, for machines 1 and 2; Pallet transfer time = 1 time units per pallet; Pallet capacity, n=4 units initially and varied later.The following five experiments have been carried out and three cell performance measures, namely, the cell production rate, machine utilizations, and robot utilizations are evaluated.
Experiment 1:Comparing simulation results to the available analytical results for the FMC, to verify and validate the simulation model
Experiment 2:Analyzing the effects of pallet transfer rate on FMC performance measures under different machining times and tool change policies.
Experiment 3:Analyzing the effects of different robot loading/unloading rates and machining rates on FMC performance measures.
Experiment 4:Effects of tool change intervals and tool change times on FMC performance.
Experiment 5:Effects of different pallet capacities or batch sizes on cell performance measures under varying tool change policies.
In order to verify and validate simulation results, we performed the first experiment on the FMC with the same parameters that were used in the analytical results reported previously. These parameters have been specified above.
Simulation results and exact numerical results for the machine and robot utilizations are shown in figure 3, while the results for the production rates are shown in figure 4. In figure 4, S means simulation and E means exact results in the legends.As it is seen from figure 3 and figure 4, FMC production rate, as well as machine and robot utilizations, increases with increasing pallet transfer rates in both simulation and numerical results. It can be observed from the figures also that simulation model gave almost the same results as obtained from analytical models reported by Savsar and Aldaihani (2004).
Figure 3. Machine/Robot Utilizations (Exper. 1) Figure 4. FMC Production Rate (Exper. 1)
Equipment utilization increases up to a certain level, with respect to increased pallet transfer rate, to about 48-51% for a pallet transfer rate of 5 pallets/minute in this case, and stays steady thereafter. Similar trend is observed in the case of production output rate, which increases to about 475 per day for a pallet transfer rate of 6 and stays steady thereafter. The simulation results were slightly less than the exact results. The same experiment was repeated with machine times of 0.5 time units, instead of 1.0 time units, and similar trends were observed. With this experiment, simulation model was verified to give sufficiently accurate results so that it could be used for other cases with no exact solutions.
Inthe second experiment, machining times were exponential with a mean of 1 minute and robot loading/unloading time was exponential with a mean of 0.25 minutes. A tool change operation was carried on the machines after every 50 parts and the tool change time was exponential with a mean of 5 minutes. FMC performance was evaluated with respect to pallet transfer rate at fixed pallet capacity of 4 units. Figure 5 and figure 6 show the results of simulation for this experiment. Note that it is not possible to obtain analytical results for the FMC system under the mentioned operational characteristics since no exact models are available in this case.Steady results are obtained for a pallet transfer rate of 5 per minute. Machine utilizations reached to about 41%, robot utilization reached to about 44% and the production rate achieved per day was about 390 units at 6 pallets/minute.
In the third experiment, we have investigated the effects of robot speed, or loading/unloading time, at fixed machining speeds, on cell performance measures. Figures 7-8 illustrate the results for the machine and robot utilizations and FMC production under different robot speeds. Pallet transfer rate was fixed at 2 pallets/minute; machining times at 1.0 minutes; and pallet capacity at 10 parts. For a fixed machining speed, decreasing robot speed resulted in increased robot utilizations, decreased machine utilizations, and decreased production output rate. In case of fixed robot speed, not shown in the figures, reducing machining speed resulted in opposite effects.
Figure 5. Utilization Results (Experiment 2)
Figure 6. Production Results (Experiment 2)
In the fourth experiment, effects of different tool change intervals and tool change times on cell performance measures are investigated. Machining rates, robot loading/unloading rates, pallet transfer rates and pallet capacity were fixed as in experiment three. First, tool change intervals were changed from 30 to 70 parts for both machines and the tool change time was
Figure 7. Effects of Robot LoadingTime (3a)
Figure 8. Effects of Robot Loading(Exp. 3b)
Figure 9. Effects of Tool Change Intervals(4a)
fixed at 5 minutes. Next tool change interval was fixed at 50 parts and tool change time was changed between 3 and 10 minutes.Figures 9 and 10 illustrate the results for the first case, while figures 11 and 12 illustrate the results for the second case for the equipment utilizations and production output rates.In case of increasing tool change times from 3 to 10 minutes, machine utilizations decrease from 35% to about 30% and robot utilization from 70% to 57%. Production output decreases from 326 to 270 units per shift; a decrease of 17% in the production output rate.
Figure 10. Effects of Tool Change Intervals (4b)
Figure 11. Effects of Tool Change Times (4c)
In the fifth experiment, two tool change policies, one based on number of parts and the other based on fixed time tool-change interval, are compared under different pallet capacity and illustrated in figures 13-15.In case of fixed-part tool change intervals, tool change operations are carried out after machining of every 50 parts on each machine. Tool change intervals would be variable in this case since machining times are variable for each part. As it is seen in figure 13, robot utilization increases from 50% to 65% as
Figure 12. Effects of Tool Change Times(4d)
the pallet capacity increases from 2 to 10 units, while machine utilizations increase from 26% to 34% for machine 1 and to 32% for machine 2. Machine 1 has higher utilizations since it is given higher priority in loading and unloading. In the case of fixed-time tool change intervals, tools are changed every 50 minutes on each machine resulting in fixed time intervals for tool change operations. Robot and machine utilizations are shown in figure 14 for this case. The results are almost the same for the fixed-part tool change interval policy. Figure 15 compares production output rate due to two tool change policies under different pallet capacities. Fixed-parts tool change policy performed slightly better than fixed-time policy for all pallet capacities and the production output reaches to a steady level at a pallet capacity of 10 parts.
5. Conclusions
In today’s manufacturing environment, demand for products is changing continuously and production systems must be able to respond to the fast changes in customer preferences and requirements. Manufacturing systems, which have enough flexibility can respond to these changes quickly and offer advantages over non-flexible systems. FMC systems have been developed for the purpose of machining a variety of products on the same equipment with little or no extra time required for change-over from one product type to another.
In this paper, we have presenteda simulation model that could guide engineers and managers responsible in designing and operating FMC systems. Several experiments are carried out to illustrate possible investigations that can be carried out before designing or during operation of FMC systems. The simulation results show that FMC performance measures are sensitive to robot speeds, machining rates, pallet rates, pallet capacity and the tool change policies utilized. Pallet capacity or batch size, which can easily be altered with little cost, has a significant effect on cell production output. Tool change times, even when reduced slightly, have significant effects in increasing production output rates. The simulation model developed in this paper can be further extended to study several other types of FMC systems with different characteristics, such as different operation time distributions and different tool change policies.