Bob Percell, president of CAMotion, wants to put motion control within the reach of manufacturing processes that involve repetitive operations, but don't need expensive robotics. The company is using software algorithms developed at the Georgia Institute of Technology as the foundation of a motion-control system that will help manufacturers reduce labor involved with routine-inspection and material-handling tasks. Two types of algorithms are used in the CAMotion software. The first is a vibration-control algorithm that plans the robotic-axis trajectory. By damping out vibration, it allows use of lighter and less-expensive components. A learning algorithm helps the equipment improve its own performance. "Once the machine makes the moves through about five iterations, it learns the open loop, gets more accurate, and reduces the dynamic error by a factor of ten," says Purcell. The software also combines machine vision, encoders, and accelerometers for helping the system know its own location relative to the work. Percell says that software combined with smaller components can reduce automation costs from 10 to 30% in many applications.
The company says it anticipates high-definition video for home security and other uses will be the next mature technology integrated into the IoT domain, hence the introduction of its MatrixCam devkit.
Siemens and Georgia Institute of Technology are partnering to address limitations in the current additive manufacturing design-to-production chain in an applied research project as part of the federally backed America Makes program.
Most of the new 3D printers and 3D printing technologies in this crop are breaking some boundaries, whether it's build volume-per-dollar ratios, multimaterials printing techniques, or new materials types.
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