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Abstract: In today’s technology-driven era, the demand for engineers with strong programming skills continues to rise across diverse industries. LabVIEW software stands out as a key tool in engineering, supporting applications ranging from test and measurement systems to automation and control across different sectors, like manufacturing, aerospace, and automotive. Proficiency in LabVIEW therefore enables engineers to work on a wide range of projects and applications. This article presents two distinct pedagogical approaches to teaching LabVIEW programming at the university level. The introductory course is taught using traditional direct teaching methods, with an emphasis on face-to-face teaching and classroom lectures. In contrast, the advanced course uses a flipped classroom model, where students work independently on preparatory material before participating in laboratory exercises. The latter course concludes with a large project, which the student must complete within four hours. The article provides details on the teaching methods and content of the individual courses, as well as an explanation of the assessment process for the final project. The analysis of the final project results confirms that the students have acquired the knowledge necessary to create functional LabVIEW programs with the appropriate programming style.
Keywords: LabVIEW, graphical programming, teaching, learning, flipped classroom
Published in DKUM: 24.09.2024; Views: 0; Downloads: 7
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Keywords: dynamic programming, suboptimal solution, feature selection, machine learning
Published in DKUM: 16.07.2024; Views: 139; Downloads: 8
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Abstract: Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression.
Keywords: continuous casting of steel, surface defects, automatic control, machine learning, modelling, optimisation, prediction, linear regression, genetic programming
Published in DKUM: 25.03.2024; Views: 284; Downloads: 12
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Abstract: Mobile cranes represent conventional construction machinery that is indispensable for the erection of most prefabricated buildings, especially those containing heavy components. However, it is also common knowledge that the engagement of these machines has a significant influence on the environment, various social aspects of the construction process, and its economic benefits. Optimal positioning of the mobile crane on the construction site, primarily driven by the contractor’s interest to perform assembly operations with expensive machinery as effectively as possible, considerably reduces not only the costs of engaging such a machine but indirectly also its negative impacts on construction sustainability. This paper discusses an exact nonlinear model for the optimization task. The optimization model consists of a cost objective function that is subject to various duration and positioning constraints for the mobile crane, including bounds on its degrees of freedom of movement and stop positions. Because the model formulation includes discontinuous and non-smooth expressions, nonlinear programming with discontinuous derivatives (DNLP) was employed to ensure the optimal solution was reached. The model provides the mobile crane operator with exact key information that enables the complete and optimal assembly of the building structure under consideration. Additionally, the information gained on the optimal distribution of the mobile crane rental period to assembly operations allows for a detailed duration analysis of the entire process of building structure erection, which can be used for its further improvement. An application example is given in this study to demonstrate the advantages of the proposed approach.
Keywords: construction sustainability, mobile crane, nonlinear programming with discontinuous derivatives, optimization, positioning
Published in DKUM: 18.12.2023; Views: 403; Downloads: 14
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Abstract: Linear programming is one of the widely used methods for optimising business systems, which includes organisational, financial, logistic and control subsystems of energy systems in general. It is possible to express numerous real-world problems in a form of linear program and then solve by simplex method [1]. In the development of linear programming, we are facing a number of upgrades and generalisations, as well as replenishment. Particularly interesting in recent years is an option that decision variables and coefficients are fuzzy numbers. In this case we are dealing with fuzzy linear programming. If we also include in a fuzzy linear program a generalisation with respect to Wolfe’s modified simplex method [1], we obtain a generalised fuzzy linear program (GFLP). Usenik and Žulj introduced methods for solving those programs and proved the existence of the optimal solution in [2]. In the article, the simplex algorithm which enables the determining of an optimal solution for GFLP is described. There is a numerical example at the end of the article that illustrates the algorithm.
Keywords: linear programming, fuzzy linear programming, generalised linear programming, generalised fuzzy linear programmin
Published in DKUM: 11.10.2023; Views: 296; Downloads: 5
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