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AI functionalities in cobot-based manufacturing for performance improvement in quality control application

Moor Madis, Sarkans Martins, Kangru Tavo, Otto Tauno, Riives Juri

Journal of Machine Engineering

2024

Vol. 24, No. 2

44-55

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.

Optimisation of decision-making process in industrial robot selection

Kangru Tavo, Riives Jüri, Otto Tauno, Kuts Vladimir, Moor Madis

Journal of Machine Engineering

2020

Vol. 20, No. 1

70--81

The successful selection process of industrial robots (IRs) for today’s Cyber-Physical Systems is an important topic and there are different possibilities to solve the task. The primary task is to estimate the existing IR selection systems according to the suitability analysis and to highlight the main positive features and problematic areas. The objective of the reverse task is to carry out the sensitivity analysis of the existing robot-based manufacturing systems. The matching of these two approaches helps decision makers to develop the main principles of IR selection in today’s multidimensional and fast-changing economic world.