A Novel Route to Optimize Placement Equipment Kinematics by Coupling Capacitive Accelerometers.

Machine end-effector kinematic analysis is critical to optimizing transporting components where inertial forces are the main loads. While displacements may be measured with relatively high accuracy in transportation equipment motors, the inertial forces in the transported components are seldom optimized. This is especially relevant in electronic component placement systems, where the components have a wide range of configurations (i.e., geometry and mass) and the deployment dimensional/geometric tolerances are remarkably good. The optimization of these systems requires the monitoring of the real position of the accelerometers relative to the measurement point of interest with sufficient accuracy that allows the assembly position to be predicted instantaneously. This study shows a novel method to calibrate this equipment using triaxial accelerometers on a surface mount machine to measure the end-effector accelerations and velocities in its planar motion. The dynamic equations of the system and the method for integration are presented to address the uncertainty on the exact position of the accelerometer sensors relative to the measuring point of interest exist and allow the position correction to optimize response and accuracy.

Learning to Time: a perspective.

In the last decades, researchers have proposed a large number of theoretical models of timing. These models make different assumptions concerning how animals learn to time events and how such learning is represented in memory. However, few studies have examined these different assumptions either empirically or conceptually. For knowledge to accumulate, variation in theoretical models must be accompanied by selection of models and model ideas. To that end, we review two timing models, Scalar Expectancy Theory (SET), the dominant model in the field, and the Learning-to-Time (LeT) model, one of the few models dealing explicitly with learning. In the first part of this article, we describe how each model works in prototypical concurrent and retrospective timing tasks, identify their structural similarities, and classify their differences concerning temporal learning and memory. In the second part, we review a series of studies that examined these differences and conclude that both the memory structure postulated by SET and the state dynamics postulated by LeT are probably incorrect. In the third part, we propose a hybrid model that may improve on its parents. The hybrid model accounts for the typical findings in fixed-interval schedules, the peak procedure, mixed fixed interval schedules, simple and double temporal bisection, and temporal generalization tasks. In the fourth and last part, we identify seven challenges that any timing model must meet.