A Framework for Optimal Placement of Strain Gauges on Elastic Elements of Force Sensors Using Genetic Algorithms
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 th...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2020-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2020/05/itmconf_itee2020_04010.pdf |
| Summary: | 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. |
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| ISSN: | 2271-2097 |