Simulation

Simulation programs to predict the course of processes

With complex robot movements and mutual logistic dependencies of multiple simultaneous manipulations, it is no longer possible to predict how the systems behave. Let alone we can make a sensible statement about a performance to be achieved. Depending on the complexity of the system, we use different software to understand the degree of simultaneous and associated cycle times.

With average complex motion profiles, we can suffice with our own developed simulation software. In highly complex systems of a more random nature, we use simulation programs that can predict a long-term process and where targeted disturbances can be entered. With this highly specialized branch of sport, we use the support of one of our regular partners.

Avoid mistakes and make correct decisions

A simulation is a dynamic process. From a given starting point, show a simulation how this situation changes and evolves over time. The model thus indicates the rules according to which this change takes place. Benefits of a simulation are that they take place in a controlled, well-defined environment, and that it can be performed without affecting reality. The lessons learned from a simulation can then be used to make sensible decisions in reality and avoid mistakes. It is important that the simulation is performed correctly and by an experienced company. If the model does not display a good description of reality, the starting point is incorrectly chosen. As a result, incorrect assumptions can be made or uncertainties are taken into account. This may lead to premature or incorrect conclusions. Z-tech has experience in performing dynamic and static simulations.

Dynamic simulations

In dynamic computer simulations, system changes are imitated over time. In order to be able to calculate these continuous changes well on a computer, a discreet approach is usually chosen. The time is divided into small cubes. The state of the system is calculated step by step based on iterations. The optimal size of these cubes can be determined experimentally or based on statistical methods (depending on the desired reliability). In addition to the time, some space-based models can also divide the space into small cubes. We call these cellular vending machines.

Static simulations

In static computer simulations, the change of the system is calculated in one set of calculations. The system then expires at one time from one state to another. These simulations are mostly known from the technical world and are mainly used for design applications, where changes in design depend on environmental factors (for example, in technical engineering).

Example dynamic simulation

To provide more insight into simulations, we give an example of a dynamic simulation from practice:

Purpose and demarcation

Purpose: Determine the number of robots needed to process product components in various different configurations”
Demarcation: Supply from punching machines and warehouses is 100% guaranteed

Robot picks up, if required and within range, the part that reached the end (LCFS). (Thus not according to fixed rhythm). The last robot always takes the required part according to the FCFS principle. It is assumed to have 1 pick-up position at the robot (with average cycle times of take-off positions 1 and 2)

Input and variables

Speed punching machine:
50 to 80 cycles/min

Products:
Type 1, 2, 3, 4 and 5

Distance between products:
minimum 60mm

Chance of error per robot:
‘Mispick’: 1/1000 (worst case)
‘Drop’: 1/1000 (worst case)

Pick-Range Robots:
Pick-Range Robots: 400 / 500mm (respectively 1/4 and 2/3)
Product parts and pick order
Cycle times and reach robots

Output

• Assumptions
• If robot does not recognize component 1 or 2) does not pick up correctly, the robot will not take action and will remain on the conveyor belt. Robot waits for the next part that is within range.
• If the robot catches the part and drops it is likely that it falls on 1 of the 4 jobs proportionally to the number of tires that the product may fall into
• Presentation Simulation Model
• Presentation Simulation results