College

Buchtel College of Arts and Sciences

Date of Last Revision

2022-05-03 07:30:26

Major

Mechanical Engineering

Honors Course

4600 461 - 012

Number of Credits

2

Degree Name

Bachelor of Science

Date of Expected Graduation

Spring 2022

Abstract

Adhesively bonded joints have an advantage in joining dissimilar engineering materials due to their high structural efficiency and being lightweight. These joints are either between two opposite laminates or between a composite laminate and a metal structure. The aerospace and automotive industries have seen an increase in utilizing these adhesive joints in their engineering applications. Joint strength along with the failure mode (adhesive, delamination, etc.) is the most important parameter to evaluate when understanding the capability of the adhesive joint. In this paper, a regression and a classification machine learning (ML) model are utilized to predict the failure load and the failure mode of single lap adhesive joints. This work compiled 103 single lap joint samples with different geometric and material parameters. An Artificial Neural Network (ANN) model and a Random Forest (RF) model were developed to accurately predict the joint’s failure load and failure mode. These models allow us to explore the complex, mathematical relationship between the input parameters and the overall joint strength.

Research Sponsor

KT Tan

First Reader

Alper Buldum

Second Reader

Manigandan Kannan

Honors Faculty Advisor

Scott Sawyer

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.