@article {104080,
	title = {Deep learning-based automated 3D inspection of helical gears using voxelized CAD models and 3D convolutional autoencoders},
	journal = {The International Journal of Advanced Manufacturing Technology },
	volume = {141},
	year = {2025},
	pages = {3695{\textendash}3715},
	abstract = {
	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 techniques, are often time-consuming and lack adaptability to subtle deviations. Recent deep learning approaches show promise but are typically limited to point-based or scan-to-scan comparisons, which remain sensitive to noise and alignment errors. We propose a voxel-based 3D inspection framework that integrates an XGBoost-guided perturbation model with a 3D convolutional autoencoder (3D CNN-AE). CAD-derived gear models are systematically perturbed with controlled Gaussian deformations to emulate tolerances, defects, and sensor noise, then voxelized for autoencoder training. This enables robust learning of nominal gear geometry distributions. Extensive experiments conducted against PointNet++, a Variational Autoencoder, and a GAN-based reconstruction model demonstrate that our method consistently achieves superior performance across various metrics, including PSNR, SSIM, accuracy, precision, recall, and F1-score. The results highlight the potential of voxel-based learning with data-driven perturbation for scalable and high-accuracy inspection in industrial applications.

},
	url = {https://link.springer.com/article/10.1007/s00170-025-16892-y},
	author = {Selloum, Rabia and Ameddah, Hacene and Mourad Brioua}
}
