Date of Award
Doctor of Philosophy
Industrial Education and Technology
Joseph C. Chen
A fuzzy-nets based in-process adaptive surface roughness control (FNASRC) system was developed in this research. The FNASRC system was able to adapt cutting parameters in-process and in a real time fashion to improve the surface roughness of machined parts when the surface roughness quality was not meeting customer requirements in the end milling operations. The FNASRC system was comprised of two sub-systems: (a) in-process surface roughness recognition (IPSRR); and (b) fuzzy-nets adaptive feed rate control (FNAFRC).;Two IPSRR sub-systems with different theories were developed and evaluated in this study. First, a multiple linear regression based in-process surface roughness recognition system (MLR-IPSRR) was developed; it had approximately 90% accuracy with 30 testing data sets. Secondly, a fuzzy-nets based in-process surface roughness recognition (FN-IPSRR) sub-system was also developed and had 94% accuracy in predicting surface roughness while the machining process was taking place using similar testing data sets. Due to superior accuracy, the FN-IPSRR sub-system was implemented into the FNASRC system along with the FNAFRC sub-system.;Furthermore, the FNAFRC sub-system was developed using a fuzzy nets theory to adapt a proper feed rate, which could produce the desired surface roughness when the original surface roughness could not meet customer requirements.;Integrating the above-mentioned two sub-systems, the FNASRC system was developed and tested. To test the system, while the machining process was taking place, the FN-IPSRR system predicted the surface roughness, which was then compared to the desired surface roughness. If the desired surface roughness was not met, then, the FNAFRC system proposed a new feed rate for the machining process. Once the feed rate was changed, and the cutting continued, the output of the surface roughness of the new feed rate was compared with the desired surface roughness. A total of 25 experimental tests have been conducted and have been found to have a 100% success rate with this FNASRC system. This research provides a positive opportunity for continued smart CNC machine development in the future.
Digital Repository @ Iowa State University, http://lib.dr.iastate.edu
Yang, Lieh-Dai, "Development of a fuzzy-nets based adapted surface roughness control (FNASRC) system in end-milling operations " (2002). Retrospective Theses and Dissertations. 971.