Title :
In-process Pokayoke system in unmanned manufacturing cells
Author_Institution :
Dept. of Ind. Educ. & Technol., Iowa State Univ., Ames, IA, USA
Abstract :
An in-process Pokayoke (IP) system has been developed in unmanned manufacturing cells (UMCs) to approach a zero defect rate based on fuzzy systems and neural networks approaches. The IP system is a real-time approach to detect a tooling defect in an UMC. The IP system consists of two components: (1) the fuzzy-nets classifier (FNC), which maps a state vector into a recommended action using fuzzy pattern recognition, and (2) the fuzzy-nets adaptor (FNA), which maps a state vector and it failure signal into a scalar grade that indicates state integrity. The FNA also produces the output active value, p, to upgrade FNS mapping according to the variation of the input state. The performance of the IP system was examined for an end milling operation
Keywords :
fuzzy neural nets; industrial control; quality control; end milling operation; failure signal; fuzzy pattern recognition; fuzzy systems; fuzzy-nets adaptor; fuzzy-nets classifier; in-process Pokayoke system; neural networks; scalar grade; state integrity; tooling defect; unmanned manufacturing cells; zero defect rate; Condition monitoring; Cutting tools; Decision making; Fuzzy systems; Machine tools; Machining; Manufacturing processes; Milling; Process control; Teeth;
Conference_Titel :
Industrial Technology, 1994., Proceedings of the IEEE International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
0-7803-1978-8
DOI :
10.1109/ICIT.1994.467097
