Fuzzy-nets-based in-process surface roughness adaptive control system in end-milling operations

A fuzzy-nets-based in-process adaptive surface roughness control (FN-ASRC) system was developed to be 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 FN-ASRC system was comprised of two sub-systems: (1) fuzzy-nets in-process surface roughness recognition (FN-IPSRR); and (2) fuzzy-nets adaptive feed rate control (FN-AFRC) sub-system. 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 FN-AFRC 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. This proposed FN-ASRC system has been demonstrated to be successful using 25 experimental tests with 100% success rate.

[1]  Shi-Jer Lou,et al.  In-Process Surface Roughness Recognition (ISRR) System in End-Milling Operations , 1999 .

[2]  Pau-Lo Hsu,et al.  FUZZY ADAPTIVE-CONTROL OF MILLING PROCESSES , 1992 .

[3]  Reza Langari,et al.  A Neuro-Fuzzy System for Tool Condition Monitoring in Metal Cutting , 2001 .

[4]  Sounak Kumar Choudhury,et al.  In-process tool wear estimation in milling using cutting force model , 2000 .

[5]  Jacob Chen,et al.  A Fuzzy-Net-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations , 2001 .

[6]  Joseph C. Chen,et al.  A fuzzy-nets in-process (FNIP) system for tool-breakage monitoring in end-milling operations , 1997 .

[7]  Yung C. Shin,et al.  In-process control of surface roughness due to tool wear using a new ultrasonic system , 1996 .

[8]  Eiji Usui,et al.  An evaluation approach of machine tool characteristics with adaptive prediction , 1996 .

[9]  Dong Young Jang,et al.  Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning , 1996 .

[10]  S. J. Lou,et al.  In-process surface recognition of a CNC milling machine using the fuzzy nets method , 1997 .

[11]  Joseph C. Chen An effective fuzzy-nets training scheme for monitoring tool breakage , 2000, J. Intell. Manuf..

[12]  Richard F. Reiss,et al.  Anatomy Of Automation , 1962 .

[13]  Igor Grabec,et al.  Application of a neural network to the estimation of surface roughness from ae signals generated by friction process , 1995 .

[14]  Lieh-Dai Yang,et al.  Development of a fuzzy-nets based adapted surface roughness control (FNASRC) system in end-milling operations , 2002 .