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The article discusses the analysis technique working table for a robotic complex based on the study of point data in a twodimensional measurement space. The resulting data is a two-dimensional data set. Data processing is performed iteratively, using a smoothing and interpolation method. The method is based on the use of a multicriteria objective function. Minimization of the objective function is performed simultaneously by the standard deviation of the input data from the values obtained as a result of processing and the criterion minimizing the mean square of the difference between the values generated as a result of processing. The adjustment parameter allows you to set the weight of the criterion. The use of such a filter allows you to automatically set the degree of smoothness of the output function and determine the areas that have a deviation relative to the measurement error threshold set by the operator. At the second iteration, the marked areas and the area next to them are measured with the minimum grid spacing. Areas that are not marked as locally damaged are analyzed with a reduced step (mismatched points) and reprocessed by a multi-criteria method. If new errors are found, the area is rescanned, otherwise it is accepted as acceptable for use. The resulting three-dimensional matrix makes it possible to evaluate areas with large errors (deviations) at standard displacements of the working tool and the working table, and to calculate the compensation parameters of the displacements. As field data, we used data from the analysis of deviations obtained by analyzing the desktop with a minimum grid step of measurements and a shift in three coordinates of the machine and the proposed approach. The result obtained made it possible to identify the same areas of deviations as the complete one obtained during the full measurement cycle. For various conditions, it was possible to reduce the analysis time up to 10 times, in some cases it was more than 12 hours. Tables of nature data and examples of calculation of predictive values of final analysis cycles are given.
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