Theses and Dissertations
ORCID
https://orcid.org/0000-0003-3798-7040
Issuing Body
Mississippi State University
Advisor
Shmulsky, Rubin
Committee Member
Stokes, Beth
Committee Member
Burgreen, Greg
Date of Degree
12-9-2022
Document Type
Graduate Thesis - Open Access
Major
Sustainable Bioproducts
Degree Name
Master of Science (M.S.)
College
College of Forest Resources
Department
Department of Sustainable Bioproducts
Abstract
The current method for estimating wood failure is highly subjective. Various techniques have been proposed to improve the current protocol, but none have succeeded. This research aims to use deep learning/semantic segmentation using SegNet architecture to estimate wood failure in four types of three-ply plywood from mechanical shear strength specimens. We trained and tested our approach on custom and commercial plywood with bio-based and phenol-formaldehyde adhesives. Shear specimens were prepared and tested. Photographs of 255 shear bonded areas were taken. Forty photographs were used to solicit visual estimates from five human evaluators, and the remaining photographs were used to train the machine learning models. Twelve models were trained with the combination of four image sizes and three dataset splits. In comparison to visual estimates, the model trained on 512 × 512 image size with 90/10 dataset split had a mean absolute error (MAE) of 6%, which was the best among the literature.
Sponsorship
Mississippi State University
Recommended Citation
Ferreira Oliveira, Ramon, "Assessing wood failure in plywood by deep learning/semantic segmentation" (2022). Theses and Dissertations. 5661.
https://scholarsjunction.msstate.edu/td/5661
Included in
Artificial Intelligence and Robotics Commons, Wood Science and Pulp, Paper Technology Commons