Casting is a process where molten metal is poured into a mold to make products. Casting defects refer to problems in the product that happen during the casting process. These defects can cause financial losses for businesses and damage to machines. Detecting these defects through visual inspection can be costly and time-consuming. To make this process faster and more accurate, a deep learning model called Convolution Neural Network (CNN) can be used to analyze images of casting products. To understand this approach, you need to know about Python, TensorFlow, Keras, and Jupyter Notebook. CNN is a type of network architecture in deep learning that is used for image-related tasks. CNN consists of three layers: convolution layer, pooling layer, and fully connected layers. Activation functions like ReLU and sigmoid are used in artificial neural networks to introduce non-linearity into them. Dataset selection, implementation, environment setup, and model training are steps you need to follow to use CNN for detecting casting defects. The model is trained for 15 epochs, and after that, there is no significant change in both accuracy and validation loss.

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Update from: https://towardsai.net/p/l/step-by-step-guide-to-build-visual-inspection-of-casting-products-using-cnn