Defect Sample

Defect sample analysis focuses on identifying and classifying defects across diverse domains, from software code to 3D-printed objects and manufactured wafers, aiming to improve quality control and predictive maintenance. Current research emphasizes the use of machine learning, particularly deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), often incorporating transfer learning and techniques to address imbalanced datasets. These advancements are significantly impacting various industries by enabling automated defect detection, improving process efficiency, and reducing costs associated with faulty products or software failures.