Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Weidong Li, Yuchen Liang, Yiding Liu

Research output: Contribution to journalArticlepeer-review

6 Downloads (Pure)

Abstract

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. Tostrengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samplesand re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporatedinto the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.
Original languageEnglish
Article number101817
Number of pages12
JournalAdvanced Engineering Informatics
Volume54
Issue number101817
Early online date24 Nov 2022
DOIs
Publication statusE-pub ahead of print - 24 Nov 2022

Fingerprint

Dive into the research topics of 'Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation'. Together they form a unique fingerprint.

Cite this