Solving the Issue of Discriminant Roughness of Heterogeneous Surfaces Using Elements of Artificial Intelligence
Autor: | Dagmar Měřínská, Milena Kubišová, Adam Skrobak, Miroslav Marcaník, Vladimír Pata |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Surface (mathematics)
0209 industrial biotechnology Technology Computer science 02 engineering and technology Surface finish computer.software_genre Article 020901 industrial engineering & automation perceptron Machining General Materials Science statistical analysis of measured data surface quality Microscopy QC120-168.85 Artificial neural network QH201-278.5 Statistical parameter 021001 nanoscience & nanotechnology Perceptron Engineering (General). Civil engineering (General) TK1-9971 metallic materials Discriminant Descriptive and experimental mechanics Data mining Electrical engineering. Electronics. Nuclear engineering TA1-2040 0210 nano-technology computer Cutting-plane method |
Zdroj: | Materials Materials, Vol 14, Iss 2620, p 2620 (2021) Volume 14 Issue 10 |
ISSN: | 1996-1944 |
Popis: | This work deals with investigative methods used for evaluation of the surface quality of selected metallic materials’ cutting plane that was created by CO2 and fiber laser machining. The surface quality expressed by Rz and Ra roughness parameters is examined depending on the sample material and the machining technology. The next part deals with the use of neural networks in the evaluation of measured data. In the last part, the measured data were statistically evaluated. Based on the conclusions of this analysis, the possibilities of using neural networks to determine the material of a given sample while knowing the roughness parameters were evaluated. The main goal of the presented paper is to demonstrate a solution capable of finding characteristic roughness values for heterogeneous surfaces. These surfaces are common in scientific as well as technical practice, and measuring their quality is challenging. This difficulty lies mainly in the fact that it is not possible to express their quality by a single statistical parameter. Thus, this paper's main aim is to demonstrate solutions using the cluster analysis methods and the hidden layer, solving the problem of discriminant and dividing the heterogeneous surface into individual zones that have characteristic parameters. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. [IGA/FT/2021/006 TBU] |
Databáze: | OpenAIRE |
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