Methods of test for adhesives. Adhesively bonded joints: mechanical tests

Monitoring the bondline integrity of adhesively bonded joints is one of the most critical concerns in the design of aircraft structures to date. Due to the lack of confidence on the integrity of the bondline both during fabrication and service, the industry standards and regulations require assembling the primary airframe structure using the inefficient “black-aluminum” approach, that is, drill holes and use fasteners. Furthermore, state-of-the-art non-destructive evaluation and structural health monitoring approaches are not yet able to provide mature solutions on the issue of bondline integrity monitoring. Therefore, the objective of this work is the introduction and feasibility investigation of a novel bondline integrity monitoring method that is based on the use of piezoelectric sensors embedded inside adhesively bonded joints in order to provide an early detection of bondline degradation. The proposed approach incorporates (1) micro-sensors embedded inside the adhesive layer leaving a minimal footprint on the material, (2) numerical and analytical modeling of the electromechanical impedance of the adhesive bondline, and (3) electromechanical impedance–based diagnostic algorithms for monitoring and assessing the bondline integrity. The experimental validation and assessment of the proposed approach is achieved via the design and fabrication of prototype adhesively bonded lap joints with embedded piezoelectric sensors and a series of mechanical tests under various static and dynamic (fatigue) loading conditions. The obtained results demonstrate the potential of the proposed approach in providing increased confidence on the use of adhesively bonded joints for aerospace structures.

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Assessing the quality of adhesive bonded joints using an innovative neural network approach

International Journal of Structural Integrity ◽

10.1108/ijsi-01-2014-0003 ◽

2014 ◽

Vol 5 (3) ◽

pp. 187-201 ◽

Cited By ~ 2

Author(s):

Christos Vasilios Katsiropoulos ◽

Evangelos D. Drainas ◽

Spiros G. Pantelakis

Keyword(s):

Neural Network ◽

Fracture Toughness ◽

Ultrasonic Inspection ◽

Mechanical Tests ◽

Bonded Joints ◽

Adhesively Bonded ◽

Severity Factor ◽

Adhesively Bonded Joints ◽

Content Type

Purpose – The purpose of this paper is to assess the quality of adhesively bonded joints using an alternative artificial neural networks (ANN) 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 G IC 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 ANN which was developed within the present work. Findings – Using the developed neural network (NN) 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. Originality/value – The innovation of the paper stands on the feasibility of an alternative approach for assessing the quality of adhesively bonded joints using and ANNs, thus minimizing the necessary testing effort.

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