Increasing labor and raw material costs and rigorous customer requirements make it essential that sawmill production processes be accurate and efficient. This requires that production be well diagnosed and monitored. Fuzzy-neural expert systems can improve productivity and product quality by optimization of bandsaw feed speed. An original, online method of achieving optimal settings of a fuzzy-neural network has been developed. Results of cutting experiments using several wood species show that the fuzzy-neural system developed performs well in online feed speed optimization during bandsawing, while maintaining saw deviation within specified limits. After the learning process, the cutting time was sequentially reduced from 54 to 23 seconds, compared to 26 seconds of corresponding non-controlled cut with fixed feed speed. The new methodology can be easily integrated with existing production systems.
Online adaptive control of bandsaw feed speed using a fuzzy-neural system
Sandak J;
2003
Abstract
Increasing labor and raw material costs and rigorous customer requirements make it essential that sawmill production processes be accurate and efficient. This requires that production be well diagnosed and monitored. Fuzzy-neural expert systems can improve productivity and product quality by optimization of bandsaw feed speed. An original, online method of achieving optimal settings of a fuzzy-neural network has been developed. Results of cutting experiments using several wood species show that the fuzzy-neural system developed performs well in online feed speed optimization during bandsawing, while maintaining saw deviation within specified limits. After the learning process, the cutting time was sequentially reduced from 54 to 23 seconds, compared to 26 seconds of corresponding non-controlled cut with fixed feed speed. The new methodology can be easily integrated with existing production systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
