Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components

TL;DR

This paper presents a vision-based inspection system that combines computer vision and neural networks to detect and predict surface defects like fractures in manufactured components, enhancing quality control in automated production lines.

Contribution

It introduces a novel system integrating OpenCV and TensorFlow for defect detection and fracture prediction, advancing traditional vision-based inspection methods.

Findings

Effective detection of surface fractures in gears

Successful prediction of surface wear and defects

Utilization of open-source frameworks for implementation

Abstract

We are proceeding towards the age of automation and robotic integration of our production lines [5]. Effective quality-control systems have to be put in place to maintain the quality of manufactured components. Among different quality-control systems, vision-based inspection systems have gained considerable amount of popularity [8] due to developments in computing power and image processing techniques. In this paper, we present a vision-based inspection system (VBI) as a quality-control system, which not only detects the presence of defects, such as in conventional VBIs, but also leverage developments in machine learning to predict the presence of surface fractures and wearing. We use OpenCV, an open source computer-vision framework, and Tensorflow, an open source machine-learning framework developed by Google Inc., to accomplish the tasks of detection and prediction of presence of surface defects such as fractures of manufactured gears.