Simulations strategies using Delmia Quest.

Mohora, Cristina ; Anania, Dorel ; Calin, Oana Andreea 等

1. INTRODUCTION

New trends in intelligent manufacturing like the simulation techniques are making from the different scenarios analysed a viable alternative to traditional product development. The simulation process could be used for dynamic analysis before implementation and in this case costs and stocks should be reduced at minimum. The manufacturing simulation techniques can contribute essentially in developing of high performance production systems (Anania et al., 2008).

The product development using computer aided simulation methods assures the time decreasing between the product's conception and the market launch, decrease the manufacturing and development costs and increase the products' quality (Anania & Mohora, 2008). For existing products, the simulation process could improve product's performance and quality. Simulation techniques could evaluate and compare different technological and manufacturing scenarios and identify the best technical solutions for specified situations (Ispas et al.,2008).

The paper will present the simulation of an assembly process for two types of parts (P1, P2) in manufacturing system.

The main steps are:

* Manufacturing system modelling.

* Manufacturing system simulation.

* Data Analysis.

* Manufacturing system's optimisation.

[FIGURE 1 OMITTED]

The first part [P.sub.1] could be made using a lathe process (lathe time [t.sub.1] = 20minutes) or a milling process (milling time [t.sub.2] = 42 minutes). For the second part [P.sub.2] the milling process time is t=10 minutes. At the end, the final piece is obtained by assembling [P.sub.1] with [P.sub.2]. The technological activities are presented in figure 1.

Simulation data analysis process could estimate the performance of the production parameters such as: productivity, the waiting times, machine repair times, and parts' working time in system. Also is very useful to see the material flows, the parts manufacturing and the products assembling process. Manufacturing process analysis using simulation data offers information about design decisions and queues points.

The paper presents an original and detailed modelling and simulation analysis of the manufacturing system using two steps. The first step is to present the modelling and simulation techniques in manufacturing field. The second step is the integration of discrete event system concept in production flow modelling. Simulation of discrete events is suitable for the study of the material flow and of parts and work stations distribution.

2. MANUFACTURING PROCESS MODELING AND SIMULATION

Modelling and simulation process are used to develop a manufacturing system with new, efficient and performed production strategies. Simulation process is an analysis tool which predicts the effect of changes of performances under varying sets of situations or parameters (Ispast et al., 2002).

The most important simulation objects in production field are shown in table 1. A successful simulation process must include all these objects.

The main problem in manufacturing simulation process is to build the system model by an accurate approximation and to choose the right technical solution.

[FIGURE 2 OMITTED]

We are going to use for manufacturing simulation the FIFO and Line Balance strategies in Delimia Quest. Manufacturing simulation process provides details on:

* Machine utilization.

* Average waiting time.

* Number of parts in the manufacturing system.

* Machine queue behavioural.

* Queue obtained in FIFO, LIFO.

We have modelled and simulated a manufacturing system which consists of two machines--a lathe and a milling machine. After simulation process we obtain the result that after milling operation for 5,5 [P.sub.1] parts the output buffer fills the capacity (figure 2).

In conclusion the FIFO strategy applied to this model is not the best solution. We decided to change the manufacturing strategy using Line Balance. After that we had simulated again the manufacturing system. The new strategy improved the manufacturing flow by combining the processing of [P.sub.1] and [P.sub.2] in an optimised way on the milling machines at determined periods of time.

In this case the output buffer did not fill to capacity as it was in the first studied case. After the manufacturing process was finished the system had a higher number of assembled products.

3. THE AVANTAGES OF INTEGRATING SIMULATION TECHNIQUES IN MANUFACTURING FIELD

The manufacturing strategy based on simulation analysis and modelling presented in this paper is a way to lower costs in the same conditions of production.

The presented case study may be considered as a coherent and global model (figure 3). The parameters that describe adequately the manufacturing system optimization are:

* The manufacturing operations: describes the type of the parts [P.sub.1], [P.sub.2] for which machine (the lathe and the milling machine) can be used in the manufacturing process at the established time.

* Production efficiency: measures the percentage of high quality products, in this case the assembled products.

* Processing time: is the necessary time for current processing of every machine. We already know this periods of time (part [P.sub.1] - lathe time [t.sub.1] = 20min, milling time [t.sub.2] = 42minutes and for the part P2 the lathe time [t.sub.2] = 10 minutes).

* The integration of simulation analysis in manufacturing optimization could improve efficiency without incurring any financial investment and also reducing the costs.

[FIGURE 3 OMITTED]

4. CONCLUSIONS

Simulation aided computer process is a new scientific method used for developing different manufacturing scenarios for a new product. In this paper is presented an implementation of the simulation technologies in the manufacturing systems for a simple production system. We noticed that the integration of the simulation software significantly reduced the design-cycle time and increase the productivity.

The scenario analysis based on simulation languages can be used to generate one or more variants of a manufacturing system in order to optimize the system. Simulation can be a great aid in evaluating alternative production systems. In particular, manufacturing scenario analysis may qualify the effect of a decision or determine whether a given decision has a significant effect. So different manufacturing scenarios can be studied and finally the best solution will be obtained. Using simulation for the optimization strategy is a way to a lower cost in the same conditions of production.

Simulation helps the modern industry to achieve significant advantages including:

* Better manufacturing resources utilization by eliminating bottlenecks.

* The lead times to market decreasing.

* Improves the productivity of existing manufacturing systems.

* Improves customer services with existing production resources.

Using the simulation data we had optimised the first modelled manufacturing system. At the end of simulation we could improve and optimise the manufacturing system.

5. REFERENCES

Anania D., Zapciu M.&Mohora C. (2008). Modelling of the PC MILL 100 Machine tool and milling process using DELMIA V5R17. The 19th International DAAAM Symposium "Intelligent Manufacturing & Automation: Focus on Next Generation of Intelligent Systems and Solutions" 22-25th October 2008, ISSN 1726-9679, ISBN 978-3-901509-68-1, ISI Proceedings.

Anania D. & Mohora C. (2008). Research concerning for a machine structure assembly. Recent Advances in Visualisation, Imagining and simulation, WSEAS, ISSN 1790-2769, ISBN- 978-960-474-022-2.

Ispas C, Mohora C., Tilina D. & Paraschiv M. (2008). Researches and solutions for optimising technical problem with TRIZ theory. The 19th International DAAAM Symposium "Intelligent Manufacturing & Automation: Focus on Next Generation of Intelligent Systems and Solutions" 22-25th October 2008, ISSN 1726-9679, ISBN 978-3-901509-68-1.

Ispas, C., Mohora, C. & Calin, O. (2002) Simulation tool of manufacturing optimisation. Pakistan Journal of Applied Scinces nr. 2. Pakistan, ISSN 1607-8926.

Mohora C., Cotet, C.&Patrascu, G.(2001). Simularea sistemelor de producfie--Simularea proceselor, fluxurilor materiale si informationale. (Simulation of production systems--process and materials and information flow simulation) Editura Agir. Editura Academiei Romane ISBN 973-27-0868-9 si Editura Agir ISBN 973-8130-69-7, Bucuresti

Tab. 1. Simulation objects

 Simulation
 objects Observations

System Establishes how well is functioning a
evaluation production system with technological criteria.

Compilation Implies to compare two competitive
 production systems designed for a technical
 function.

Prediction Estimates the performances of a production
 system working in a certain set of conditions
 such as time, productivity.

Optimization Determines precisely a technical factors
 combination, which will produce the best
 response of the production system.

Blocking Indicates the obstruction points inside the
points production system and the possibilities to
analysis eliminate them.