International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | To Design an Artificial Neural Network Based Quality Inspection System for Automobile Components |
|---|---|
| ABSTRACT | This project titled “To design an artificial neural network based quality inspection system for automobile components,” presents an advanced Quality inspection in automobile manufacturing is a critical process that ensures safety, reliability, and performance of components. Traditional inspection methods are often time-consuming, labor-intensive, and prone to human error. This paper proposes an Artificial Neural Network (ANN)-based quality inspection system to automate defect detection in automobile components. The system utilizes image processing techniques combined with ANN to classify components as defective or non-defective. The proposed model improves accuracy, reduces inspection time, and enhances productivity. Experimental results demonstrate that the ANN-based system achieves high precision and reliability compared to conventional methods. |
| AUTHOR | C.PRAKASH NARAYANAN, S.MERCY PRIYA, DHARANIDHARAN S, SOLAVARMAN J, SRIRAM P Assistant Professor, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Krishnagiri Dt., Tamil Nadu, India UG Scholars, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Krishnagiri Dt., Tamil Nadu, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404093 |
| pdf/93_To Design an Artificial Neural Network Based Quality Inspection System for Automobile Components.pdf | |
| KEYWORDS | |
| References | 1. P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 1, pp. 56–59, 2001.This paper introduced a fast and efficient method for real-time object detection.It laid the foundation for modern vision-based inspection systems. 2. Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436–444, 2015.This work explains the fundamentals of deep learning and neural networks.It supports the use of ANN for automated defect detection. 3. A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, 2012.The paper demonstrated CNN superiority in image classification tasks. It inspires ANN-based visual inspection systems. 4. R. Szeliski, Computer Vision: Algorithms and Applications, Springer, 2011.This book provides comprehensive knowledge on image processing techniques.It is useful for preprocessing and feature extraction modules. 5. T. Ojala, M. Pietikäinen, and D. Harwood, “A Comparative Study of Texture Measures,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.This study discusses texture-based feature extraction methods.It is relevant for detecting surface defects in automobile components. 6. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” IEEE CVPR, 2016.This paper introduced deep residual networks for improved learning.It supports robust ANN training for defect classification. |
