AI-Driven Surface Roughness Estimation for Cutting Tool Performance Using Microscope Imaging

 




 

Yap, Kai Tong (2026) AI-Driven Surface Roughness Estimation for Cutting Tool Performance Using Microscope Imaging. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

Surface roughness is a critical indicator of machining quality and cutting tool performance, yet conventional measurement methods such as contact profilometers are costly, time-consuming, and unsuitable for real-time monitoring. This study aimed to develop and evaluate a CNNbased framework for non-contact prediction of the areal surface roughness parameter

Item Type: Final Year Project
Subjects: Technology > Mechanical engineering and machinery
Technology > Electrical engineering. Electronics engineering
Science > Computer Science > Artificial intelligence
Faculties: Faculty of Engineering and Technology > Bachelor of Mechatronics Engineering with Honours
Depositing User: Library Staff
Date Deposited: 24 Jul 2026 09:44
Last Modified: 24 Jul 2026 09:44
URI: https://eprints.tarc.edu.my/id/eprint/38032
 
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