Open AccessThe use of interpolation methods for modelling multifactor processes based on an experiment planning matrix
Author(s) -
E. V. Konopatskiy,
I. V. Seleznev,
А. А. Бездитный
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2182/1/012005
Subject(s) - interpolation (computer graphics) , mathematical optimization , computer science , matrix (chemical analysis) , process (computing) , multivariate statistics , mathematical model , mathematics , algorithm , trilinear interpolation , multivariate interpolation , statistics , artificial intelligence , machine learning , bilinear interpolation , motion (physics) , materials science , computer vision , composite material , operating system
This article is dedicated to the development of experimental data processing tools based on the geometric theory of multidimensional interpolation. At the same time, a scientific problem was solved, which is dedicated to the analytical determination of models of multifactor processes while maintaining the existing approach to experiment planning This makes it possible not only to use interpolation methods for the mathematical description of new experiments, but also to use the experimental data obtained earlier to improve their mathematical interpretation. The paper provides two examples that confirm the effectiveness of the proposed approach to constructing models of multivariate processes using multivariate interpolation methods. The first of the above examples contains two geometric models of the stress-strain state of metal polyhedral bent struts, which are presented in the form of ruled response hypersurfaces passing through 8 predetermined points in the 4-dimensional space. The second one is dedicated to the construction of a 2-factor process process using a 2- parameter parabolic geometric interpolant with subsequent optimization by methods of mathematical analysis of a function of two variables. As a result, the optimization of the aerated concrete manufacturing process was carried out to achieve the maximum values of the ultimate strength in compression after heat and moisture treatment.