ITM Web Conf.
Volume 35, 2020International Forum “IT-Technologies for Engineering Education: New Trends and Implementing Experience” (ITEE-2019)
|Number of page(s)||13|
|Section||Modernization of Engineering Courses based on software for Computer Simulation|
|Published online||09 December 2020|
A Framework for Optimal Placement of Strain Gauges on Elastic Elements of Force Sensors Using Genetic Algorithms
Bauman Moscow State Technical University, 2nd Baumanskaya str., 5/1, 105005, Moscow, Russia
* Corresponding author: email@example.com
This paper presents a digital education tool for learning the specifics and behavior of a multi-objective genetic algorithm (MOGA) used to solve the problem of optimal placement of strain gauges on the elastic element of a force sensor. The paper formulates the problem statement and specifies how this problem can be solved using the MOGA. For the problem, the design variables are the locations of strain gauges and angles at which they are positioned. The goal functions are the output signal of the sensor and the measurement error from bending moments, which can be caused by the off-centric application of load. The solution algorithm is implemented within a framework that can be used to investigate and learn how parameters of MOGA influence its performance. The framework is used to run computational experiments for the given problem to find the optimal placement of strain gauges on the elastic element of a given force sensor. The performance of the MOGA in solving this problem is compared to that of the traditional approach.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.