| Title |
Identification of Defects in Casting Products by using a Convolutional Neural Network |
| Authors |
(Dilliraj Ekambaram) ; (Vijayakumar Ponnusamy) |
| DOI |
https://doi.org/10.5573/IEIESPC.2022.11.3.149 |
| Keywords |
Casting quality inspection; CNN; Industry 4.0; Machine learning |
| Abstract |
The main perspective when ensuring dependability in speculations over accuracy in casting parts is a project quality confirmation process that is both careful and meticulous under Industry 4.0. When thorough and extensive casting project examination strategies merge with expanded metal project quality standards, casting production, augmented visual inspections, ensemble process modification and execution are improved. In this paper, we use publicly available casting image datasets for visual inspection, which classify defective and non-defective casting. Inspired by the convolutional neural network (CNN), we propose two-stage convolution for modeling, with DenseNet for classifying casting products. Through experimentation, we achieved an F1-score of 99.54% with a processing time of 454ms using a CPU for classification of casting product inspections. The modified modeling of the CNN in this work helps to improve optimization, compared to other basic machine learning mechanisms that measure quality. |