One very important component next generation DM applications are advanced robotic simulation tools that go far beyond earlier off-line programming applications. These simulation tools enable production engineers to virtually simulate, validate, and commission the entire robotic workcell environment. Currently, a robotic workcell environment can be very complex, complex tooling and fixtures, and could include multiple robots doing multiple tasks, clamp automation and PLCs, a variety of sensors, conveyor automation, and even vision technology.
Manufacturers are pushing to use of robotics well beyond the single task workcell to multi-robot gardens with the capability to build complex assemblies requiring high levels of collaboration and synchronization. This necessitates to use of advanced robotic simulation technology in order for production engineers to the meet the high level of complexity associated with these assembly and build requirements.
The automotive industry, as well as other discrete industries such as defense and heavy equipment & aero-space have indentified the capability to virtually commission their production systems and assembly lines as one of the initial and most immediate benefits derived from DM technology.
Simulation of robotic represents a technology that has become an essential tool, and has steadily evolved over time for the automotive industry in the application of virtual commissioning. This directly addresses their need for faster production ramp-up and time to market, increased vehicle models, commonality of production processes across global operations, and modular plant designs all validated within the virtual environment.
Role of simulation plays a key in the field of robotics, because it permits experimentation that would otherwise be time-consuming and/or expensive. Simulation allows engineers to try ideas and construct production scenarios in a synthetic environment, dynamic while collecting virtual response data to determine the physical responses of the control system. Simulation allows the robotics control systems evolution, which depend on random permutations of the control system over many generations.
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