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Fasteners play a critical role in securing various parts of machinery.Deformations such as dents, cracks, and scratches on the surface of fastenersare caused by material properties and incorrect handling of equipment duringproduction processes. As a result, quality control is required to ensure safeand reliable operations. The existing defect inspection method relies on manualexamination, which consumes a significant amount of time, money, and otherresources; also, accuracy cannot be guaranteed due to human error. Automaticdefect detection systems have proven impactful over the manual inspectiontechnique for defect analysis. However, computational techniques such asconvolutional neural networks (CNN) and deep learning-based approaches areevolutionary methods. By carefully selecting the design parameter values, thefull potential of CNN can be realised. Using Taguchi-based design ofexperiments and analysis, an attempt has been made to develop a robustautomatic system in this study. The dataset used to train the system has beencreated manually for M14 size nuts having two labeled classes: Defective andNon-defective. There are a total of 264 images in the dataset. The proposedsequential CNN comes up with a 96.3% validation accuracy, 0.277 validation lossat 0.001 learning rate.

Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners