Classification of shot peening coverage based on deep learning technologies
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 inspecti...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/157853 |
| الوسوم: |
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| الملخص: | 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. |
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