Fasteners play a critical role in securing various parts of machinery.
Deformations such as dents, cracks, and scratches on the surface of fasteners
are caused by material properties and incorrect handling of equipment during
production processes. As a result, quality control is required to ensure safe
and reliable operations. The existing defect inspection method relies on manual
examination, which consumes a significant amount of time, money, and other
resources; also, accuracy cannot be guaranteed due to human error. Automatic
defect detection systems have proven impactful over the manual inspection
technique for defect analysis. However, computational techniques such as
convolutional neural networks (CNN) and deep learning-based approaches are
evolutionary methods. By carefully selecting the design parameter values, the
full potential of CNN can be realised. Using Taguchi-based design of
experiments and analysis, an attempt has been made to develop a robust
automatic system in this study. The dataset used to train the system has been
created manually for M14 size nuts having two labeled classes: Defective and
Non-defective. There are a total of 264 images in the dataset. The proposed
sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss
at 0.001 learning rate.

研究中使用基于深度学习和 CNN 技术的缺陷检测方法，同时设计参数和 Taguchi 实验以建立自动化检测系统来提高紧固件标准化质量控制。