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Deep learning based gasket fault detection: a CNN approach.

S Arumai Shiney¹, R Seetharaman², V J Sharmila³

¹Department of Computer Science and Engineering, S.A. Engineering College, Chennai, India. arumaishiney@saec.ac.in.

Scientific Reports

|February 8, 2025

Summary

This study introduces an automated gasket inspection system using deep learning and convolutional neural networks (CNNs). The system accurately detects misaligned or incorrectly installed gaskets, enhancing manufacturing quality control and product reliability.

Keywords:

CNN Deep learning Gasket Gasket inspection Quality Control Radiator

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Area of Science:

Manufacturing Engineering
Computer Vision
Artificial Intelligence

Background:

Gasket inspection is crucial for product quality control.
Manual inspection methods are time-consuming and prone to errors.
Automated solutions are needed to improve efficiency and accuracy.

Purpose of the Study:

To develop an automated system for detecting misaligned or incorrectly fitting gaskets.
To leverage deep learning, specifically Convolutional Neural Networks (CNNs), for gasket inspection.
To enhance the reliability and efficiency of quality control in manufacturing.

Main Methods:

Utilized deep learning algorithms for feature extraction and classification.
Developed a CNN architecture comprising convolution, batch normalization, ReLU, and max pooling layers.
Trained the system on radiator images to identify gasket installation defects.

Main Results:

The developed CNN-based system demonstrated high accuracy in identifying misaligned gaskets.
The system effectively automates the detection of incorrectly fitting gaskets.
The results indicate strong potential for practical application in industrial settings.

Conclusions:

The automated gasket inspection system offers a reliable and efficient quality control mechanism.
Implementation can significantly reduce product defects and improve overall product reliability.
The deep learning approach provides a robust solution for manufacturing quality assurance.