AUTOMATION AND ROBOTICS:COMPUTER-AIDED METHODS FOR ASSEMBLY SYSTEMS

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COMPUTER-AIDED METHODS FOR ASSEMBLY SYSTEMS

A large number of analytical models have been developed for planning manual and general assembly systems. Another objective has been to measure and evaluate the performance and productivity of assembly system design by applying simulation. Two essential technologies are explained in this section.

Layout Planning and Optimization

Nobody these days could imagine new products being designed and constructed without software tools. However, in the field of assembly line planning for new products, a lot of paperwork still has to be done. Most companies use computers only for documentation purposes and visualization of planning results. This means that many companies still use standard office tools for this field of activity and even execute the concept for the layout of assembly lines with a simple graphic tool. The high number of different tools and interfaces means that an actual data status for all project participants is impossible.

Meanwhile, some tools are available that are specially designed for the planning of assembly structures. The advantage of these tools is that they make continuous planning of assembly plants possible. All planning increments are entered into a central database that can show the actual status at any time.

The product structure is first defined in the tool, and then design proposals for the product are created by the software. The objective of these proposals is to create a consistent design for product assembly. MTM and UAS analysis are also supported by the software. The cycle time and the number of workstations are determined on the basis of the time data. The work steps for each station are also defined. After this, rough plans for each station can be made. This can be done using the database, which can be placed directly in the workstation. The database contains many common data such as workbenches, pressings, and robots. These are displayed as 3D graphics with additional parameters such as price and dimensions.

In addition, ergonomic investigations can be carried out in every workstation. The results of the time analyses and the appropriation of the parts can also be checked and optimized in the 3D model of the workstations (Figure 33).

The independent stations are finally united in a complete layout of the system, and the interlinking system and the bumpers are defined. In a final simulation of the material flow, the buffer sizes can be optimized and bottlenecks eliminated. The planning results are available as data and also as a 3D model of the assembly line. This provides all participants with enough data upon which to base their decisions (see Figure 34).

Automation and Robotics-0147

Cost analyses and efficiency calculations can also be executed with the software support. The use of software tools for the planning of assembly lines can reduce planning time and increase the quality of the results.

Simulation of Material Flow

The objective of material flow simulation is to predict the effect of actions before they are executed in order to be able to react if the results do not meet expectations. For making decisions especially in complex problems, the operations research procedure is also available. This procedure supposedly finds the most optimal solution on its own, which the simulation cannot accomplish. However, the immense number of model restrictions for complex problems with regard to model shaping and the great amount of calculation required make this practically unusable. In contrast, material flow sim- ulation requires only a reasonable amount of modeling and calculation effort.

There are basically two application fields for material flow simulation: simulation for new plans and simulation aimed at optimizing existing systems. For new plans, the simulation aims to increase the planning security by determining initially whether the system works at all, whether the cycles of the individual stations are correct, where possible bottlenecks are in the system, and how malfunctions in individual stations affect the whole system. During the subsequent planning optimization process, the effects of malfunctions mainly determine the buffer size, which can vary considerably from the static buffer deviations. Furthermore, steps can be taken to review which measures will be needed to increase capacity by 10%, 20%, and more without the necessity of further shifts. This allows a change in the level of demand during the product’s life cycle to be taken into consideration.

During operation after a product modification, new work tasks, or the assembly of a further variation on the existing system have been introduced, often a manufacturing system will no longer achieve the planned production levels and a clear deviation between target and actual production figures will become evident over a longer period of time. Due to the complexity of the manufacturing process, it is often not clear which actions would have the greatest effect. Simulation can determine, for example, what level of success an increase in the number of workpiece carriers or the introduction of an additional work cell would have. This helps in the selection of the optimal measures needed to increase productivity and the required levels of investment. Simulation of an existing system also has the major advantage that existing data pertaining to system behavior, such as actual availability, the average length of malfunctions, and the dynamic behavior of the system, can be used. These data can be determined and transposed to the model relatively easily, which makes the model very realistic.

For a simulation study to be executed, a simulation model must first be made that represents a simplified copy of the real situation. The following data are required: a scaled layout of the produc- tion, the cycle times of each of the processing steps, the logistic concept including the transportation facilities and the transportation speeds, the process descriptions, and data on the malfunction profiles, such as technical and organizational interruption times and the average length of these times. Once these data has been entered into the model, several simulations can be executed to review and, if necessary, optimize the behavior of the model.

The following example shows the possibilities of material flow simulation. In the model of an assembly system which is designed to produce 140 pieces / hr, the number of the workpiece carriers is increased. The result is a higher production level at some of the examined stations, but at some locations the higher number of workpiece carriers cause a blockage of stations that have successor stations with a higher cycle time. Thus, the increase of workpiece carriers has to be done in small steps with a separate simulation after each step to find the optimum. In this example, a 10% increase in the number of workpiece carriers and the installation of a second, parallel screwing station lead to a production increase from 120 to 150 pieces / hr.

The model reaches its optimum when each of the parameters is specifically modified and a combination of different measures is achieved. Aside from the creation of the model itself, this is the real challenge.

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