Le, Chi Hieu ORCID: 0000-0002-5168-2297, Le, Dang Thang, Arey, Daniel, Gheorghe, Popan, Chu, Anh My, Duong, Xuan Bien, Nguyen, Trung Thanh, Truong, Trong Toai, Prakash, Chander, Zhao, Shi-Tian, Mahmud, Jamaluddin, Gao, James ORCID: 0000-0001-5625-3654 and Packianather, Michael (2020) Challenges and conceptual framework to develop heavy-load manipulators for smart factories. International Journal of Mechatronics and Applied Mechanics, 8 (2). pp. 209-216. ISSN 2559-4397 (Print), 2559-6497 (Online)

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Abstract

Industry 4.0 has been one of the emerging topics in recent years, covering a wide range of concepts and applications as well as political, economic and technological views. Manufacturing is becoming smarter and smarter at all levels, moving toward the concept of Smart Factory (SF), based on the advancements of digital transformation technologies, including Artificial Intelligence (AI) and bigdata analytics, and abilities to learn, configure and execute with cognitive intelligence of smart machines and automation systems. However, the SF adoption in practice, especially in Small and Medium-sized Enterprises (SMEs), is still in the early stage. In addition, there are growing demands of product personalisation, mass-customisation and diversification. Therefore, the involvement of humans is still importantly required in many production processes in SF models, where smart machines, smart manipulators, collaborative robots and Automated guided vehicles (AGVs) are required to co-work with humans, leading to an important concern of safety, reliability, productivity and quality of smart manufacturing systems. In this paper, challenges and a proposed conceptual framework to develop smart heavy-load manipulators are presented, with the focus on the cost-effectiveness and applicability in industrial practices of SF for SMEs.

Item Type:	Article
Additional Information:	The International Journal of Mechatronics and Applied Mechanics is a peer-reviewed, open-access journal, published twice a year, in July and November.
Uncontrolled Keywords:	Smart factory, industry 4.0, manipulators, cobots, automated guided vehicles
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Design, Manufacturing and Innovative Products Research Theme Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	31 Aug 2021 14:48
URI:	http://gala.gre.ac.uk/id/eprint/29752

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