Work

Automated surface roughness classification using robot vision

Year: 2024

Type: article

Abstract: A robot vision system, also known as a Machine Vision (MV) system, enables automatic inspection using image processing techniques. This research focuses on the classification of turned surface images into four categories (A, B, C, and D) using deep learning algorithms with transfer learning. Images were captured under varying machining conditions (speed, feed, depth of cut) as per a full factorial experimental design. The dataset was divided with 70 % for training, 15 % for validation, and 15 % for testing the algorithms. Surface roughness parameters were analyzed using a robot vision system, comprising a Mitsubishi articulated robot with 6 degrees of freedom and a 4 kg payload, and a Cognex In-Sight 7801 camera (1.3 MP, 1280 × 1024 resolution). The performance of the models was evaluated based on average accuracy. The system demonstrated significant potential in enhancing the surface finish inspection process in high-production industries, reducing labor costs, inspection time, operator errors, and setup requirements, thereby increasing productivity and lowering production costs .

Source: Science Talks

DOI: 10.1016/j.sctalk.2024.100395

Authors: Sanjay Krishnarao Darvekar, Juttuka Yaswanth Sai Venkatesh, Abbaraju Bala Koteswara Rao, Ravi Sekhar, Pritesh Shah, Gautam Ingle

Institutions: Gayatri Vidya Parishad College of Engineering for Women, Symbiosis International University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $700

Funders: Department of Science and Technology, Ministry of Science and Technology, India, Department of Science and Technology, Government of Rajasthan