AI Functionalities in Cobot-Based Manufacturing for Performance Improvement in Quality Control Application
More details
Hide details
Department of Mechanical and Industrial Engineering, Tallinn University of Technology, Estonia
Mechanical Engineering, TTK University of Applied Sciences, Estonia
Submission date: 2024-03-31
Final revision date: 2024-05-20
Acceptance date: 2024-05-21
Online publication date: 2024-05-27
Publication date: 2024-06-10
Corresponding author
Madis Moor   

Department of Mechanical and Industrial Engineering, Tallinn University of Technology, Tallinn, Estonia
Modern manufacturing faces vastly changing challenges. The current economic situation and technological developments in terms of Industry 4.0 (I4.0) and Industry 5.0 (I5.0) force enterprises to integrate new technologies for more efficient and higher-quality products. Artificial intelligence (AI) and Machine Learning (ML) are the technologies that make machines capable of making human-like decisions. In the long run, AI and ML can add a layer (functionality) to make IoT devices more interactive and user-friendly. These technologies are driven by data and ML uses different types of data for making decisions. Our research focuses on testing a cobot-based quality control (CBQC) system that uses smart fixture and machine vision (MV) to determine the cables inside products with similar designs, but different functionality. The products are IoT modules for small electric vehicles used for interface, connectivity, and GPS monitoring. Previous research describes the methodology of reconfiguration of existing cobot cells for quality control purposes. In this paper, we discuss the testing of the CBQC system, together with creating a pattern database, training the ML model, and adding a predictive model to avoid defects in product cable sequence. Preliminary testing is carried out in the laboratory environment which leads to production testing in SME manufacturing. Results, developments, and future work will be presented at the end of the paper.
Eurofound, 2021, Tackling Labour Shortages In EU Member States, Publications Office of the European Union, Luxembourg.
PIESKA S., KAARELA J., MÄKELÄ J., 2018, Simulation and Programming Experiences of Collaborative Robots For Small-Scale Manufacturing, 2nd International Symposium on Small-scale Intelligent Manufacturing Systems (SIMS), Cavan, Ireland, 1-4,
BATTH R.S., NAYYAR A., NAGPAL A., 2018, Internet of Robotic Things: Driving Intelligent Robotics of Future - Concept, Architecture, Applications and Technologies, 4th International Conference on Computing Sciences (ICCS), Jalandhar, 151–160,
MOOR M., VAHER K., RIIVES J., KANGRU T., OTTO T., Modern Robot Integrated Manufacturing Cell According to the Needs of Industry 4.0, Proceedings of the Estonian Academy of Sciences, Tallinn, 202, 407–412,
FRIEDMANN M., FLEISCHER J., 2022, Automated Configuration of Modular Gripper Fingers, Procedia CIRP, 106, 70–75,
MOOR M., SARKANS M., RIIVES J., OTTO T., VAÑÓ J.M., Methodology for Reconfigurable Cobot-Based Quality Control System for SME Production, International Journal of Engineering and Technology, 10.7763/IJET (accepted, to be published).
International Federation of Robotics, 2023.
GAMBAO E., 2023, Analysis Exploring Risks and Opportunities Linked to the Use of Collaborative Industrial Robots in Europe, European Parliamentary Research Service.
International Federation of Robotics, 2020, Demystifying Collaborative Industrial Robots, International Federation of Robotics, Frankfurt.
BIRGLEN L., SCHLICHT T., 2018, A Statistical Review of Industrial Robotic Grippers, Robotics and Computer-Integrated Manufacturing, 49, 88–97,
ZHANG B., XIE Y., ZHOU J., WANG K., ZHANG Z., 2022, State-of-the-Art Robotic Grippers, Grasping and Control Strategies, as Well as Their Applications in Agricultural Robots: a Review, Computers and Electronics in Agriculture, 177,
SIKDER A.K., PETRACCA G., AKSU H., ULUAGAC S., 2018, A Survey on Sensor-based Threats to Internet-of-Things (IoT) Devices and Applications,
TORCHIA M.S.M., 2019, IDC Forecasts Worldwide Spending on the Internet of Things to Reach $745 Billion in 2019, IDC.
RODRIGUES D., CARVALHO P., et al., 2022, An IoT Platform for Production Monitoring in the Aerospace Manufacturing Industry, Journal of Cleaner Production, 368,
PARSEC AUTOMATION CORP., 2023, Parsec’s 2023 State of Manufacturing Survey: Europe.
SINGH T., SINGH D., 2023, Factories of the Future: Technological Advancements in the Manufacturing Industry, Wiley.
MATERNA Z., KAPINUS M., BERAN V., SMRŽ P., ZEMČÍK P., 2018, Interactive Spatial Augmented Reality in Collaborative Robot Programming: User Experience Evaluation, 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Nanjing, China, 80–87, 2018.8525662.
BIRGLEN L., 2019, Design of a Partially-Coupled Self-Adaptive Robotic Finger Optimized for Collaborative Robots, Auton. Robot., 43, 523–538,
EL ZAATARI S., MAREI M., LI W., USMAN Z., 2019, Cobot Programming for Collaborative Industrial Tasks: an Overview, Robot. Auton. Syst., 116, 162–180,
ZHANG Y.-J., LIU L., HUANG N., RADWIN R., LI J., 2021, From Manual Operation to Collaborative Robot Assembly: an Integrated Model of Productivity and Ergonomic Performance, IEEE Robotics and Automation Letters, 6/2, 895–902,
NATIONAL INSTRUMENTS CORP, 2024, What Is Modbus Protocol and How Does It Work?, Retrieved March 12,
PROMODEL CORP, 2012, Building the Model: Advanced Elements – Variables – Local Variables, https://www.
CARNEGIE MELLON ROBOTICS ACADEMY, 2024, Global Variables, rc_cortex_v2/lesson/media_files/hp_global_variables.pdf.
Journals System - logo
Scroll to top