Development of Machine Vision Approach for Mechanical Component Identification based on its Dimension and Pitch

TL;DR

This paper presents a fast, lightweight machine vision system for identifying and classifying bolts in assembly lines by analyzing their dimensions and pitch, achieving 98% accuracy.

Contribution

A novel, scalable vision-based method for bolt identification that includes calculating pitch and dimensions, suitable for real-time industrial applications.

Findings

Achieves 98% accuracy in bolt classification

Operates in milliseconds on minimal hardware

Successfully identifies moving components on conveyor

Abstract

In this work, a highly customizable and scalable vision based system for automation of mechanical assembly lines is described. The proposed system calculates the features that are required to classify and identify the different kinds of bolts that are used in the assembly line. The system describes a novel method of calculating the pitch of the bolt in addition to bolt identification and calculating the dimensions of the bolts. This identification and classification system is extremely lightweight and can be run on bare minimum hardware. The system is very fast in the order of milliseconds, hence the system can be used successfully even if the components are steadily moving on a conveyor. The results show that our system can correctly identify the parts in our dataset with 98% accuracy using the calculated features.