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Title: Classification of shot peening coverage based on deep learning technologies
Authors: Koh, Ren Wei
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Koh, R. W. (2022). Classification of shot peening coverage based on deep learning technologies. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: B2166-211
Abstract: Shot peening is a cold working process widely used in the aerospace industry, currently, the shot peening process involves mainly an inspector performing visual inspection using a 10x magnifier and comparing the mental visual with an image reference. The state-of-art-technology for coverage inspection involves deep learning. However, there have been no implementing of an automated coverage inspection system using Deep Learning technology. Hence, this paper aims to explore and evaluate different convolutional neural network architecture to evaluate the functionality and performance of a vision inspection system.
Schools: School of Electrical and Electronic Engineering 
Organisations: A*STAR, Advanced Remanufacturing and Technology Centre
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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