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.
In this paper, the authors present a methodology to develop a modular model of a tugger train system using Modelica language. The presented system is composed by a tugger and three passive trolleys. The model allows to estimate the path of the trolleys relative to the path of the tugger vehicle and it can be used to estimate the maximum velocities when in a curve.
A review of common vehicle models from the literature is presented. Some concepts of the Modelica language are introduced in order to support the model of the shown system.
The presented model is a simplified representation of the system for planar motion developed in Modelica language using the Open Modelica OMEdit software2. The vehicle models are modelled by custom classes and linked with the aid of blocks from the Open Modelica standard library. This model was mainly used to understand the behavior of the vehicles during U-turns, estimate the minimum turning radius and maximum velocities during the turn.
The methodology allows a modular approach combining vehicle and multibody modelling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.