Deep learning-based automated 3D inspection of helical gears using voxelized CAD models and 3D convolutional autoencoders

The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate geometries and strict precision requirements. Conventional inspection methods, such as those using coordinate measuring machines or optical techn...

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Vydané v:	International journal of advanced manufacturing technology Ročník 141; číslo 7-8; s. 3695 - 3715
Hlavní autori:	Selloum, Rabia, Ameddah, Hacene, Brioua, Mourad
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.12.2025 Springer Nature B.V
Predmet:	Accuracy Algorithms Automation CAD CAE) and Design Computer aided design Computer-Aided Engineering (CAD Coordinate measuring machines Datasets Deep learning Defects Engineering Helical gears Industrial and Production Engineering Industrial applications Inspection Inspections Machine learning Manufacturing Measurement techniques Mechanical Engineering Media Management Neural networks Noise sensitivity Noise tolerance Optics Optimization Original Article Perturbation Product reliability Deep learning Helical gear inspection Precision manufacturing Convolutional autoencoder Anomaly detection Non-Destructive testing
ISSN:	0268-3768, 1433-3015
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