Date of Award
6-2025
Degree Name
Master of Science
Department
Engineering Design, Manufacturing and Management Systems
First Advisor
Robert Tuttle, Ph.D.
Second Advisor
Sam N. Ramrattan, Ph.D.
Third Advisor
Lee J. Wells, Ph.D.
Access Setting
Masters Thesis-Open Access
Abstract
This thesis presents an in-depth investigation into the classification and prediction of surface defects in aluminum sand castings using a combination of experimental trials and machine learning techniques. Key process variables such head height, pouring temperature, resin %, sand type and process were systematically varied to study their influence on defects like veining, and burn-on. Comparative analysis indicated superior performance of the deep learning model in handling nonlinear interactions between process parameters. The study emphasizes the importance of integrating data-driven models with foundry practices to enhance surface quality, reduce rework, and support intelligent decision-making in casting operations.
Recommended Citation
Shinde, Nachiket Laxman, "Measurement and Process Relation for Cast Surface Defects" (2025). Masters Theses. 5472.
https://scholarworks.wmich.edu/masters_theses/5472