Measurement’s noise, filtered by a type-1 neuro-fuzzy technique in quality assurance

Measurement’s noise, filtered by a type-1 neuro-fuzzy technique in quality assurance Measurements are the core of quality systems. The calibration of the measurement devices is a form of evaluating it. The variability of these measurement devices is verified to know the variation inherited in the measurement tool. Additionally, the dynamics of the actual production systems cannot be satisfied by the classic approaches of the human visual inspection. This happens because they exceed the human capacities, and this phenomenon causes the loss of reliability at the outputs of the system. This paper presents a hybrid model of adaptive neuro-fuzzy inference system (ANFIS) to evaluate quality features. Also, for this purpose, it offers knowledge-based expert system able to do the quality assurance tasks by learning and adaptation. The obtained results provide an acceptable error rate for this class of systems to run at the speed of the actual manufacturing system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Measurement’s noise, filtered by a type-1 neuro-fuzzy technique in quality assurance

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Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-0151-2
Publisher site
See Article on Publisher Site

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