P. C. N. Lalithya, Sreemanthula V Sai Chethana, Madireddy Dharani, Panthangi Manasa 03 04 Defect classification in industrial components is critical to ensuring product quality operational safety and cost efficiency as manufacturing industries face increasing defect rates due to highspeed production and complex processes Studies indicate that a significant percentage of industrial failures originate from undetected surface and structural defects leading to rework downtime and financial loss This research is motivated by the need for accurate and automated defect detection in machinery parts painted surfaces and welded joints including subtypes such as cracks corrosion paint blisters scratches porosity lack of fusion and weld spatter The expected outcome is a robust classification system capable of reliably identifying defect categories and subcategories across these domains Traditional defect inspection relies heavily on manual visual inspection which is timeconsuming subjective and prone to human error Such manual systems struggle with consistency scalability and realtime deployment in modern industrial environments In this work RGB images of industrial components are utilized followed by image preprocessing techniques including resizing and normalization to enhance feature consistency and model performance Existing machine learning approaches such as KNearest Neighbours KNN and Decision Tree Classifier DTC are reviewed as baseline models for defect classification The proposed approach employs Convolutional Neural Network CNNbased feature extraction combined with Logistic Regression LR for efficient and accurate classification The system outputs precise defect classification results for machinery paint and welding components including their respective defect subtypes demonstrating improved accuracy and reliability over traditional methods Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026