In the paper we use methods of the intelligent system to predict the complexity of the network fracture of hardened specimens. We use a mathematical method of the network theory and fractal geometry in engineering, particularly in laser techniques. Moreover, using the fractal geometry, we investigate the complexity of the network fracture of the robot-laser hardened specimens, and analyze specimens hardened with different robot laser-cell parameters, such as the speed and temperature. Laser hardening is a metal-surface treatment process complementary to the conventional and induction hardening process. In this paper, we present a new method for the statistical pattern recognition using statistical techniques in analyzing the data measurements in order to extract information and take oppromate decisions in particularly mechanical engineering. To predict of the complexity of the network fracture of hardened patterns, we use multiple regression, neural network and support vector machine and to predict topographical property of hardened specimens, we use a hybrid method of machine learning.
The present work was supported by the European Social Fund of the European Union and national project VEGA No. 2/0167/16.
- Hybrid system of machine learning
- Mechanical engineering
- Pattern recognition