Chapter presents difficulties at the optimising of design and operation of modern complex industrial systems. One of the efficient answers on problems at controlling large complex systems is artificial intelligence.. In the chapter the characteristics of this methods are presented, especially evolutionary computation and swarm intelligence.
COBISS.SI-ID: 15673110
Industrial robots are part of production systems and it is important to place them into the system according to their properties and behaviour. The information, obtained from the technical sheets of robots, about workspace (its dimensions and shape) is insufficient for designing the production system. The information about mobility is missing. To represent the behaviour of the robot in the workspace, velocity anisotropy of the robot is introduced and defined as the length of the shortest velocity ellipsoid axes, which can be constructed for any position of robot in its tool centre point. The position of a tool centre point is equivalent to the point in the workspace. A graphical representation of the 3D workspace with included velocity anisotropy is then performed and an example for a design of a robotised welding production system is given. In this example the benefits of the graphical representation of the workspace with included velocity anisotropy are presented and discussed.
COBISS.SI-ID: 14359318
Genetic programming, which is one of the most general evolutionary computation methods, was used in this paper for the modelling of tensile strength and electrical conductivity in cold formed material. No assumptions about the form and size of expressions were made in advance, but they were left to the self organization and intelligence of evolutionary process. Genetic programming does this by genetically breeding a population of computer programs using the principles of Darwinianćs natural selection and biologically inspired operations. In our research, copper alloy was cold formed by drawing using different process parameters and then tensile strengths and electrical conductivity (dependent variables) of the specimens were determined. The values of independent variables (effective strain, coefficient of friction) influence the value of the dependent variables. Many different genetic models for both dependent variables were developed by genetic programming. The accuracies of the best models were proved by a testing data set. Also, comparison between the genetic and regression models is presented in the paper. The research showed that very accurate genetic models can be obtained by the proposed method.
COBISS.SI-ID: 15266582