Abstract
The development of microelectromechanical systems based on magnetohydrodynamic for micro-robot applications requires precise control of the micro-flow behavior. The micro-flow channel design and its performance under the influence of the Lorentz force is a critical challenge, the mathematical model of each magnetohydrodynamic device design must be experimentally validated before to be employed in the fabrication of microelectromechanical systems. For this purpose, the present article proposes the enhancement of a particle image velocimetry measurement process in a customized machine vision system. The particle image velocimetry measurements are performed for the micro-flow velocity profile mathematical model validation of a magnetohydrodynamic stirrer prototype. Data mining and filtering have been applied to a raw measurement database from the customized machine vision system designed to evaluate the magnetohydrodynamic stirrer prototype. Outlier’s elimination and smoothing have been applied to raw data to approximate the particle image velocimetry measurements output to the velocity profile mathematical model to increase the accuracy of a customized machine vision system for two-dimensional velocity profile measurements. The accurate measurement of the two-dimensional velocity profile is fundamental owing to the requirement of future enhancement of the customized machine vision system to construct the three-dimensional velocity profile of the magnetohydrodynamic stirrer prototype. The presented methodology can be used for measurement and validation in the design of microelectromechanical systems micro-robot design and any other devices that require micro-flow manipulation for tasks such as stirring, pumping, mixing, networking, propelling, and even cooling.
Highlights
The most common industrial and civil applications that use robots for automation are the automotive industry, food industry, and manufacturing industries of diverse products, to mention some
Some special applications require micro-robots, such as micromachining process, the electrical testing process through microprobes, micro-pumping, micro-mixing, and a lot of biomedical, biological, and environmental applications including drug delivery, biomedical assays, microfluidics analyses, cell culturing, micro-assembly, micro-surgery, micro-manipulation, and others.[2]. All of these applications have been benefited with the advances in technologies in the last two decades, especially with the development of microelectromechanical systems (MEMS) for a wide range of different functions, among which stands out the microfluidic devices, such as stirring, pumping, mixing, networking, propelling, and even cooling
For the development of MEMS based on MHD for micro-robot applications which require precise control of micro-flow behavior, a technique was developed for the enhancement of a particle image velocimetry (PIV) measurement process in a customized machine vision system
Summary
The most common industrial and civil applications that use robots for automation are the automotive industry, food industry, and manufacturing industries of diverse products, to mention some. Considering the relevance of MHD applications in microfluidics, nonintrusive experimental methods and analysis technique have been sought to MHD quantitative flow computation, for which it is common to use the opensource MATLAB tool “PIVlab” created by William Thielicke[42] under the MATLAB platform This tool is a graphical user interface (GUI) that allows the PIV to perform flow analysis, through digital image processing that calculates the velocity distribution in a frame sequence, and it is used to calculate multiple parameters of the flow. This additional section can work independently as a tool for analysis and smoothing of 2-D velocity profiles of magnetohydrodynamic flow since it is possible to load the data files previously obtained from an image sequence processed by the PIVlab
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