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.
Producing high-quality end-production metal parts with additive manufacturing for applications like aerospace and medical requires very tightly controlled processes and materials. New standards and guidelines for machines and processes, materials, and printed parts are underway from bodies such as ASTM International.
Engineers at the University of San Diego’s Jacobs School of Engineering have designed biobatteries on commercial tattoo paper, with an anode and cathode screen-printed on and modified to harvest energy from lactate in a person’s sweat.
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