Machine learning algorithms for deeper understanding and better design of composite adhesive joints

Author:

Kaiser Isaiah,Richards Natalie,Ogasawara Toshio,Tan K.T.ORCID

Publisher

Elsevier BV

Subject

Materials Chemistry,Mechanics of Materials,General Materials Science

Reference41 articles.

1. Failure classification of porous additively manufactured parts using deep learning;Johnson;Comput. Mater. Sci.,2022

2. Effect of adhesive thickness, adhesive type and scarf angle on the mechanical properties of scarf adhesive joints;Liao;Int. J. Solid. Struct.,2013

3. Effect of material, geometry, surface treatment and environment on the shear strength of single lap joints;da Silva;Int. J. Adhes. Adhes.,2009

4. Toughening effect in adhesive joints comprising a CFRP laminate and a corrugated lightweight aluminum alloy;Morano;Mater. Today Commun.,2022

5. Application of artificial neural networks to predict the bond strength of FRP-to-concrete joints;Mashrei;Constr. Build. Mater.,2013

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