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

• Autorzy: Moor Madis , Sarkans Martins , Kangru Tavo , Otto Tauno , Riives Juri

Identyfikatory

DOI

10.36897/jme/189169

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

AI functionality; collaborative robot; machine vision; adaptive gripper

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2024
• Tom: Vol. 24, No. 2
• Strony: 44--55
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Moor Madis

• Department of Mechanical and Industrial Engineering, Tallinn University of Technology, Estonia
• Mechanical Engineering, TTK University of Applied Sciences, Estonia

• https://orcid.org/0000-0002-6050-7116

autor

Sarkans Martins

• Department of Mechanical and Industrial Engineering, Tallinn University of Technology, Estonia

autor

Kangru Tavo

• Mechanical Engineering, TTK University of Applied Sciences, Estonia

• https://orcid.org/0000-0002-9320-0567

autor

Otto Tauno

• Department of Mechanical and Industrial Engineering, Tallinn University of Technology, Estonia

• https://orcid.org/0000-0003-2349-0340

autor

Riives Juri

• Department of Mechanical and Industrial Engineering, Tallinn University of Technology, Estonia

Bibliografia

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