Abstract
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.
Highlights
Digital technologies are increasingly used in education, meaning that all information used in education tends to be is transformed into digital form [1,2]
The problem is solved using a traditional approach by sampling the design space with a Sobol sequence and determining the Pareto set, followed by calculating IGD+ based on the reference Pareto set
This paper presents a digital education tool for studying the performance of a multiobjective genetic algorithm used to solve the problem of optimal placement of strain gauges on the elastic element of a force sensor
Summary
Digital technologies are increasingly used in education, meaning that all information used in education tends to be is transformed into digital form [1,2] This trend is expressed by taking tests using computers, online universities, and online courses [3]. The digitization of educational tools is another critical trend [4,5] Another trend in engineering education is that engineering becomes more and more complex, and students need to learn the skills of solving optimization problems in their respective domains on knowledge. The task of developing a digital educational tool for learning how a multi-objective engineering problem can be solved using MOGA is relevant. The software must be used to solve the given problem, and performance of MOGA must be compared to that of traditional optimization methods
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