Taguchi-based ANN predictions to analyze the tensile strength of adhesive-bonded single lap joints
- Title
- Taguchi-based ANN predictions to analyze the tensile strength of adhesive-bonded single lap joints
- Creator
- Ravichandran G.; Rathnakar G.; Ratan P.; Joshy J.; Vishal S.
- Description
- The adhesive bonding method is commonly used in various industries to join different materials because of its benefits, which include a high strength-to-weight ratio, low cost, and high efficiency properties. Automotive, aerospace, marine, and construction industries are increasingly using adhesively bonded joints because the hand layup techniques involve simpler fabrication methodologies, maintenance procedures, and controllable stress distribution parameters in the overlap region, which ultimately lead to easier production of automobile, aircraft, and ship components. The objective of the present work is primarily to assess the overlap length in conjunction with the adhesive strength of glass-epoxy adherends bonded with epoxy resin. In this article, the experiments are performed on single lap-bonded joints. The parameters considered for the current work involve the length of overlap, which is maintained at 15, 25, and 35 mm, and adhesive bonding thickness that is maintained at 0.2, 0.3, and 0.5 mm, respectively. The strength of the adhesively bonded lap joint is determined using a Universal Testing Machine (Lloyd Instruments Ltd., West Sussex, United Kingdom) with a 1-20 kN capacity. The investigational outcome reveals that, as the overlap length of the adhesively bonded single lap joint increases, a substantial increase in the joint strength is observed; additionally, it is noted that, with the increase in adhesive thickness, the joint strength decreases. It was observed that the artificial neural network-predicted values from the analysis were extremely close to the experimental values, and the difference between the experimental and predicted values was very small. Copyright 2018 by ASTM International.
- Source
- Materials Performance and Characterization, Vol-7, No. 1, pp. 186-201.
- Date
- 2018-01-01
- Publisher
- ASTM International
- Subject
- Adhesive bonding; Artificial neural network approach; Single lap joint; Taguchi method; Tensile strength
- Coverage
- Ravichandran G., Department of Mechanical Engineering, Faculty of Engineering, Christ (Deemed to Be University), Mysore Rd., Bengaluru, Karnataka, 560074, India; Rathnakar G., Department of Mechanical Engineering, ATME College of Engineering, Bannur Rd., Mysore, Karnataka, 570028, India; Ratan P., Department of Mechanical Engineering, Faculty of Engineering, Christ (Deemed to Be University), Mysore Rd., Bengaluru, Karnataka, 560074, India; Joshy J., Department of Mechanical Engineering, Faculty of Engineering, Christ (Deemed to Be University), Mysore Rd., Bengaluru, Karnataka, 560074, India; Vishal S., Department of Mechanical Engineering, Faculty of Engineering, Christ (Deemed to Be University), Mysore Rd., Bengaluru, Karnataka, 560074, India
- Rights
- Restricted Access
- Relation
- ISSN: 21653992
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Ravichandran G.; Rathnakar G.; Ratan P.; Joshy J.; Vishal S., “Taguchi-based ANN predictions to analyze the tensile strength of adhesive-bonded single lap joints,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/16937.