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2 Journal of Machine Engineering

2 2024

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Reconstructing a Manufacturing Laboratory to a Learning Factory: a TTK UAS Case Study

100%

Kangru T. , Moor M. , Rudz A. , Jänes A.

tom Vol. 24, No. 3

133--140

The rapidly developing manufacturing industry constantly needs top specialists to ensure sustainability (resource optimisation, production efficiency, sustainable products) and to implement the latest know-how (digitalisation, big data analytics, artificial intelligence). Those requirements, in turn, place higher demands on universities, curricula, teaching staff and, above all, laboratories to teach the concept of a smart factory. TTK University of Applied Sciences (TTK UAS) has come to an understanding that renovation of existing production laboratories is unavoidable. Keeping this in mind, a study needs to be conducted to investigate best practices and strategies to develop a new concept that best suits TTK UAS. In this article, the authors examine how to renovate and update the existing university laboratories (production, measurement, CAD/CAM) using simulation software with a Learning Factory concept in mind while still ensuring research development capability. Using the case-study methodology, factory automation simulation software, and a new pedagogical approach, the TTK UAS industrial engineering laboratories are functioning as a cluster, achieving higher learning and R&D efficiency.

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

88%

Moor M. , Sarkans M. , Kangru T. , Otto T. , Riives J.

tom 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.