Artificial neural network-based approach for detection and classification of defects in polymeric composites using machine vision in SEM study
by Saswata Bose, Chapal Kumar Das, Deepayan Shome
International Journal of Materials and Product Technology (IJMPT), Vol. 38, No. 4, 2010

Abstract: Miscibility and adhesion of polymeric composites are adversely affected by some microstructural defects commonly found in polymeric composites. Conventionally, the above-mentioned defects are detected and classified through manual visual analysis of the scanning electron micrographs obtained via scanning electron microscopy (SEM). However, in manual visual inspection of the micrographs, inspection personnel have their own standard of detecting and classifying defects. So, it is quite possible that two different inspection personnel may classify the same defect into two different predefined classes of defects. In order to overcome this problem of misclassification, a machine vision-based approach, which utilises an artificial neural network (ANN) model, is proposed in this study for automated detection and classification of the defects commonly found in polymeric composites. Results obtained reveal that quite a reasonable level of accuracy in detection and classification of microstructural defects of polymeric composites is achieved with the proposed approach.

Online publication date: Fri, 02-Jul-2010

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Materials and Product Technology (IJMPT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com