Assessing the quality of adhesive bonded joints using an innovative neural network approach. Issue 3 (28th August 2014)

• Record Type: Journal Article
• Authors: Katsiropoulos, Christos Vasilios; Drainas, Evangelos D; Pantelakis, Sp.
• Editors: Rodopoulos, ChrisAlexander; Guagliano, Mario
• Is Part Of: International journal of structural integrity. Volume 5:Issue 3(2014)
• Publication Date: 2014-08-28
• Subjects: Structural engineering -- Research -- Periodicals; 620.11205
• DOI: 10.1108/IJSI-01-2014-0003
• Languages: English
• ISSNs: 1757-9864

Abstract

Abstract : Purpose: The quality assessment of adhesively bonded joints using an alternative artificial neural networks approach. Design/methodology/approach: Following the necessary surface pre-treatment and bonding process, the coupons were investigated for possible defects using C-Scan ultrasonic inspection. Afterwards, the Damage Severity Factor-DSF theory was applied in order to quantify the existing damage state. A series of GIc mechanical tests was then conducted so as to assess the fracture toughness behavior of the bonded samples. Finally, the data derived both from the NDT tests (DSF) and the mechanical tests (Fracture Toughness Energy) were combined and used to train the Artificial Neural Network which was developed within the present work. Findings: Using the developed Neural Network the bonding quality, in terms not only of defects but also of Fracture Toughness behavior, can be accessed through NDT testing, minimizing the need for mechanical tests only in the initial material characterization phase. Research limitations/implications: By improving and adopting this innovative technique, there is a great potential for time and cost savings in the aircraft development cycle. Practical implications: By improving and adopting this innovative technique, there is a great potential for time and cost savings in the aircraft development cycle. Originality/value: The innovation of the paper stands on the feasibilirty of an alternative approach for assessing the quality of adhesively bonded joints using and artificial neural networks, thus minimizing the necessary testing effort.