Development of a Digital Twin for Robotic Inspection Using Computer Vision and Machine Learning

¹ Department of Computer Science Engineering, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu 602105, India
² Department of Information Technology, R.M.D Engineering College, Kavaraipettai, Tamil Nadu 601206, India
³ Department of Mechanical Engineering, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu 602105, India
⁴ Department of Mathematics, Saveetha school of Engineering, Saveetha institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu 600077, India
⁵ Department of Chemistry, Panimalar Engineering College, Chennai, Tamil Nadu 600123, India
⁶ Department of Artificial Intelligence and Robotics, School of Engineering, Dayananda Sagar University, Bengaluru, Karnataka 562112. India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 2 March 2026

Abstract

This article reveals the creation and execution of a digital twin (DT) for the robotic inspection system of mechanical parts, applying the machine vision method. The examination is carried out on the KUKA iiwa robot that has a camera for the purpose of identifying and sorting the parts that are positioned on the work area. A vision system based on a YOLO convolutional neural network (CNN) was utilized for the tasks of object detection and classification. The dataset was automatically created via the use of 3D CAD models along with domain randomization to mitigate the risk of overfitting. The simulation environment was set up in IsaacSim, and the vision system was interfaced through ROS Noetic with hand-eye calibration also included. The performance of the YOLO network evidenced an overall precision of 94.8% and recall of 87.7% in the test dataset indicating the parts' effective detection and classification. The research underlines the convergence of simulation, deep learning, robotics, and the potential for automated inspections in manufacturing sectors.