All issues

The article describes an improved paired comparison method, which systematizes in tables the rules of logical conclusions and formulas of checking indices for comparison of technical systems. To achieve this goal, the authors formulate rational rules of logical conclusions in making a paired comparison of the systems. In addition, for the purpose of consistency check of the results of the assessment, the authors introduce parameters such as «the number of scores gained by one system» and «systems’ quality index»; moreover, they design corresponding calculation formulas. For the purposes of practical application of this method to design computer programs, the authors propose to use formalized variants of interconnected tables: a table for processing and systematization of expert information, a table of possible logical conclusions based on the results of comparison of a set number of technical systems and a table of check values in the paired comparison method used in quality assessment of a definite number of technical systems. These tables allow one to organize procedures of the information processing in a more rational way and to predominantly exclude the influence of mistakes on the results of quality assessment of technical systems at the stage of data input. The main positive effect from the implementation of the paired comparison method is observed in a considerable reduction of time and resources needed to organize experts work, process expert information, and to prepare and conduct distant interviews with experts (on the Internet or a local computer network of an organization). This effect is achieved by a rational use of input data of the quality of the systems to be assessed. The proposed method is applied to computer programs used in assessing the effectiveness and stability of large technical systems.

This paper demonstrates the advantages of using artificial intelligence algorithms for the design of experiment theory, which makes possible to improve the accuracy of parameter identification for an elastostatic robot model. Design of experiment for a robot consists of the optimal configuration-external force pairs for the identification algorithms and can be described by several main stages. At the first stage, an elastostatic model of the robot is created, taking into account all possible mechanical compliances. The second stage selects the objective function, which can be represented by both classical optimality criteria and criteria defined by the desired application of the robot. At the third stage the optimal measurement configurations are found using numerical optimization. The fourth stage measures the position of the robot body in the obtained configurations under the influence of an external force. At the last, fifth stage, the elastostatic parameters of the manipulator are identified based on the measured data.

The objective function required to finding the optimal configurations for industrial robot calibration is constrained by mechanical limits both on the part of the possible angles of rotation of the robot’s joints and on the part of the possible applied forces. The solution of this multidimensional and constrained problem is not simple, therefore it is proposed to use approaches based on artificial intelligence. To find the minimum of the objective function, the following methods, also sometimes called heuristics, were used: genetic algorithms, particle swarm optimization, simulated annealing algorithm, etc. The obtained results were analyzed in terms of the time required to obtain the configurations, the optimal value, as well as the final accuracy after applying the calibration. The comparison showed the advantages of the considered optimization techniques based on artificial intelligence over the classical methods of finding the optimal value. The results of this work allow us to reduce the time spent on calibration and increase the positioning accuracy of the robot’s end-effector after calibration for contact operations with high loads, such as machining and incremental forming.