Abstract
Today customers are demanding more diversified products, with higher quality and shorter delivery times. It has led to the adoption of flexible manufacturing systems (FMS). The quality control of the manufacturing process in FMS is a critical factor, requiring flexible and intelligent quality control systems. Quality control windows (QCW) is an adequate framework for development of automated quality control systems. QCW is formed by five steps: observation, evaluation, diagnostic, decision and implementation. The most important step is evaluation, where quality control charts indicate out of control situations and possible underlying causes. This work studied the performance of three fuzzy neural networks (radial basis functions network, RBF, fuzzy artmap, and a new network, RBF fuzzy-artmap) in the identification of six different patterns in quality control charts. The new RBF fuzzy-artmap network presented the best performance of classification (78.8%), while allowing on-line incremental learning.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
