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Automated quality control is realized quite effectively through our direct machine control model. This enables us to further close the process control loop between sensing and dispensing, allowing the robot to compensate in real-time for process variations. Trainable sensor models can perform signature analysis and allow precision sensors to deal with production variations and still prevent false negatives so that the positives are only those parts with valid problems.
This concept addresses something that is an issue with common, off the shelf (COTS) SCARA or 6DOF robots: dealing with process variability. Without the automation having the capability to adjust automatically to variations in parts, materials, and environment, you need to either accept looser tolerances or greater reject counts. With adaptable automation, there is now a governing automated ombudsman that can fix some of these issues, resulting in higher yields.
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