Application of AI/ML based Image Analytics in Auto Component Fracture Analysis

This paper introduces an innovative digital solution for the categorization and analysis of fractures in Auto components, leveraging Artificial Intelligence and Machine Learning (AI/ML) technologies. The proposed system automates the fracture analysis process, enhancing speed, reliability, and accessibility for users with varying levels of expertise. The platform enables users to upload images of fractured parts, which are then processed by an AI/ML engine. The engine employs an image classification model to identify the type of fracture and a segmentation model to detect and analyze the direction of the fracture. The segmentation model accurately predicts cracks in the images, providing detailed insights into the direction and progression of the fractures. Additionally, the solution offers an intuitive interface for stakeholders to review past analyses and upload new images for examination. The AI/ML engine further examines the origin of the fracture, its progression pattern, and the structures formed during the fracture’s development. The results are stored as attributes within the system, facilitating easy review and future analysis. A notable feature of the system is its ability to learn from user feedback. If the model provides an incorrect failure recommendation, users can correct it. These corrections are then used to retrain the model, improving its accuracy over time. This feedback loop ensures that the system continually evolves and adapts to new data, enhancing its reliability and performance. This innovative approach not only streamlines the fracture analysis process but also ensures that the insights gained are comprehensive and easily accessible. By integrating advanced AI/ML techniques, the solution represents a significant advancement in the field of fracture analysis, offering a robust tool for both novice and experienced users. The system’s adaptability and user-friendly interface make it a valuable asset for improving the accuracy and efficiency of fracture analysis in Auto Components.