Genetic Learning of Fuzzy Expert Systems for Decision Support in the Automated Process of Wooden Boards Cutting

• Main Authors: Yaroslav MATSYSHYN, Volodymyr MAYEVSKYY, Mykhailo MYSYK, Alechandro DEL RIO
• Format: Article
• Language: English
• Published: Editura Universitatii Transilvania din Brasov 2014-03-01
• Series: Pro Ligno
• Online Access: http://www.proligno.ro/en/articles/2014/1/matsyshyn_final.pdf
• ISSN: 1841-4737; 2069-7430

Sawing solid wood (lumber, wooden boards) into blanks is an important technological operation, which has significant influence on the efficiency of the woodworking industry as a whole. Selecting a rational variant of lumber cutting is a complex multicriteria problem with many stochastic factors, characterized by incomplete information and fuzzy attributes. About this property by currently used automatic optimizing cross-cut saw is not always rational use of wood raw material. And since the optimization algorithms of these saw functions as a “black box”, their improvement is not possible. Therefore topical the task of developing a new approach to the optimal cross-cutting that takes into account stochastic properties of wood as a material from biological origin. Here we propose a new approach to the problem of lumber optimal cutting in the conditions of uncertainty of lumber quantity and fuzziness lengths of defect-free areas. To account for these conditions, we applied the methods of fuzzy sets theory and used a genetic algorithm to simulate the process of human learning in the implementation the technological operation. Thus, the rules of behavior with yet another defect-free area is defined in fuzzy expert system that can be configured to perform specific production tasks using genetic algorithm. The author's implementation of the genetic algorithm is used to set up the parameters of fuzzy expert system. Working capacity of the developed system verified on simulated and real-world data. Implementation of this approach will make it suitable for the control of automated or fully automatic optimizing cross cutting of solid wood.