The Research On Intelligent Choice Methods Of Micron Wood Fiber Cutting Parameters

As a kind of high quality wood product, the application fields of wood fiber are continuously extending and the requirement for the processing precision is becoming higher and higher, which promoted the transformation of processing mode from traditional extensive type to modern precision control one. At present, it has formed a rigorous theoretical analysis of micron wood fiber cutting technology, which demonstrated the feasibility of cutting process. But, wood cutting is a complex process resulted by various factors combination, and micron scale processing belongs to the category of micro machining. For such a process, the selection of cutting parameters is very important. So this paper presented some micron wood fiber cutting parameters intelligent choice methods.Intelligent control technology has been widely used in industrial production and all aspects of human life. Giving machines men’s wisdom so as to simplify mankind’s producing activities and living behaviors, which is the inevitable trend of science development and important symbol of social progress. Referring to some related knowledge of intelligent control, the paper put forward two cutting parameters intelligent choice methods respectively based on fuzzy theory and artificial neural network. They suited for different application conditions and could provide scientific as well as reasonable parameters for micron wood fiber cutting and realized intelligent processing.The paper analyzed wood cutting process in detail and explained the influence effects of some key factors, which determined the input and output of the cutting parameters intelligent choice process. It evaluated cutting performance of wood with the fuzzy comprehensive evaluation method of fuzzy theory, and divided cutting grade. With the help of the evaluation results, it designed cutting parameters intelligent choice method based on fuzzy clustering analysis. Fuzzy clustering clustered the wood in the same grade and gave the most relevant woods in cutting performance, which could be used as the basis of selecting cutting parameters. With the help of the fitting ability to complex relationships of artificial neural network, it realized the self-learning of cutting parameters by sample training. In order to verify the accuracy of the cutting parameters, the paper proposed a kind of micron wood fiber diameter measurement method that based on edge information. These functions were integrated in the form of application system, and provided simple operating experience to users.Taking the actual production conditions into consideration, the experiment simulated two cases that had less manufacture data as well as more one. It did cutting parameter selection by using these two methods under different cases and instructed machining. Detect micron wood fiber diameter that processed and compare with predetermined cutting depth parameter to judge the rationality of the cutting parameters. Experimental results showed that the proposed two cutting parameter choice methods could provide scientific as well as accurate parameters for micron wood fiber cutting process. It also analyzed their respective advantages according to the theory characteristics that the two methods used. The research laid the foundation for further research on processing technology improvement, production quality guarantee, productivity increase, production costs saving and so on.