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Virtual machine tools models in assembling systems material flow management.


Before starting a manufacturing process it is advisable to run a simulation for the production cycle. In this way one can have valuable information regarding productivity, the necessary number of machines, the personal involved in the production cycle and both the manufacturer and the beneficiary will know an approximate production cost.

The main purpose of this paper is to determine the necessary time for assembling the main shaft with the pulley wheel. In order to obtain the data necessary for simulating this assembly the first step was to use the material flow simulation for the two parts, separately. Thus we determined the production time and productivity for the shaft and the pulley wheel.

We define diffused manufacturing systems as architectures based at least 2 work points surrounded & assisted by transport, transfer & deposit facilities (Sanchez, 2001).

When taking in consideration concentrated systems we can define them as being a single assembling point, receiving parts from different working points.

We agree here with the thesis that within the class of stochastic simulation models, one further distinction is necessary: simulations can be either terminating (sometimes called finite) or non-terminating in nature, with specific algorithms for each category.

In our case the simulation is finite; we considered a finite period of time, a month, with 22 working days, 16 working hours a day.


In figure 1 we can see how the optimized virtual model for the main shaft production looks (Anghel et al., 2007).

The working points symbolized in the model represent the machines used in the production cycle: debtor machine (D1), lathe machine (S1), milling machine (F1), boring and grinding machine (MAR1), each of them having an operator supervising the production cycle. After doing a number of optimizations in order to eliminate the material flow concentrators that appeared we obtained a productivity of 322 pieces per month (these means a necessary production time of 65.4 minutes).


In the same way we will build a virtual model describing the manufacturing process for the pulley wheel. Taken in consideration the operations required for manufacturing the pulley wheel we could establish the machine-tools used. These are, as symbolised in figure 2: a lathe machine (S2), milling machine (F2), boring and grinding machine (MAR2). For each machine there is an operator present with the role of supervising the operation. All parts are collected in a buffer.

For transporting blanks among working points we used conveyor belts.

After running a material flow simulation in the preliminary architecture we could see that the milling machine is a material flow concentrator do to the big manufacturing time that the blank spends on this working point. These is also do to the fact that the blank passes on quickly from S2 (the manufacturing time on this machine is 7.6 minutes).

Because of these we started an optimization that consisted in increasing the number of the working points. We decided to add another conveyor belt and another milling machine.



By adding another machine and transport belt the total cost will increase and it is advisable to do a NPV analyses to see if the investment is justified.

After doing a number of optimizations we obtained a productivity of 629 pulley wheels per month (these means a necessary production time of 33.6 minutes).


In this chapter we will build the model for the assembly between the two manufactured parts, the main shift and the pulley wheel.

In figure 3 we can see the preliminary architecture for the assembly unit, symbolized M1. The two parts ready to be assembled are symbolized P1 (shaft) and P2 (pulley wheel) and are deposited in B1 and B2 buffers. From buffer the parts are transported by conveyor belts to the assembly unit and from there to another buffer.

For the assembly process it is also possible to take the parts directly from the buffers in which they are deposit after the manufacturing process is finished. We choose not to do that, but to ship them in different buffers from which conveyor belts will take one P1 part and P2 part in the same time. The reason for this is given by the big difference between the manufacturing times of the two parts. The main shaft take almost twice as much time compared to the pulley wheel to be manufactured, so if we tried to assembly them directly from the buffers that they were initially deposited we wouldn't obtain a very good production site.

In this situation the assembly unit will be always waiting for shafts (P1) and the conveyor transporting pulley wheels (P2) will be continuously blocked with parts.


In figure 4 we run a material flow simulation for the assembly site.


Three manufacturing systems interconnected in order to obtain a main shaft & pulley wheel assembly are acting like a virtual enterprise nodes (Tichkiewitch et al., 2006). The first manufacturing system is a diffused one and produces a main shaft from a milling machine. The second manufacturing system is also a diffused one and produces pulley wheel (Cotet, et al., 2007). The manufactured parts provided by those production sites are assembled on a third concentrated manufacturing system (Cotet et al., 2007).

The entire virtual manufacturing architecture where the 3 production sites are acting like partners in the same production network represents our case study used to illustrate an algorithm for integrating virtual machine tools models in manufacturing systems material flow management.

The model integrates material flow and process simulation in order to increase manufacturing system productivity. This kind of simulation shows important aspect regarding the manufacturing process: eventual material flow concentrators, how the presence of the operator affects the production cycle (for example: if we have two milling machines we will need two operators in order to operate these machines if we want to obtain an increasing of the production).

Another important aspect of this simulation is that we can determine the necessary number of machines for an optimum production site.

After doing a number of optimizations in both manufacturing sites, this consisted in adding machines in order to eliminate material flow concentrators from we managed to obtain a productivity of 322 pieces per month for the shaft's execution and 629 pieces for the pulley wheel. This means 65.4 minutes for the shaft and 33.6 minutes for obtaining the pulley wheel.


Anghel, F., Cazacu, D. A., Bucur, C. C. & Cotet, C. E. (2007). Classical versus numerical control machines in discrete material flow simulation based manufacturing architectures optimization, 18th International DAAAM Symposium DAAAM 2007, pag. 025-026, ISSN 1726-9679, Zadar, Croatia.

Cotet, C. E., Dragoi, G. & Carutasu, G. (2007). Material Flow & Process Synchronous Simulation In Concentrate Manufacturing Systems, Annals of DAAAM for 2007 & Proceedings of The 18th International DAAAM SYMPOSIUM, intelligent Manufacturing & Automation: Focus on Creativity, Responsibility and Ethics of Engineers", pag. 180-181, ISSN 1726-9679, ISBN 3-901509-58-5, Zadar, Croatia

Cotet, C. E., Dragoi, G. & Abaza, B. F. (2007). Multipolar Synchronous Material Flow & Process Simulation In Difussed Manufacturing Systems, Annals of DAAAM for 2007 & Proceedings of The 18th International DAAAM SYMPOSIUM, intelligent Manufacturing & Automation: Focus on Creativity, Responsibility and Ethics of Engineers", 2007, pag. 179-180, ISSN 1726-9679, ISBN 3-901509-58-5, Zadar, Croatia

Sanchez, S. M. (2001). ABC's of output analysis, Proceedings of the 2001 Winter Simulation 2001, Peters, B.A., Smith, J.S., Medeiros D.J. (Ed.), CD-ROM, Presses Association for Computing Machinery (ACM), New York

Tichkiewitch, S.; Radulescu, B. & Dragoi, G. (2006). Knowledge management for a cooperative design system, Advances in Design, ElMaraghy & Hoda A. Eds., pp. 97-110, Springer Verlag, ISBN 1-84628-004-4

ANGHEL, F[lorina]; CAZACU, D[ragos] A[lexandru] & DOBRESCU, T[iberiu] *

* Supervisor, Mentor
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Author:Anghel, Florina; Cazacu, Dragos Alexandru; Dobrescu, Tiberiu
Publication:Annals of DAAAM & Proceedings
Date:Jan 1, 2008
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