Title: Artificial intelligence enabled additive manufacturing system using 5G and industrial IoT

Authors: Rudresh Deepak Shirwaikar; Aditya Tandon; K. Sathesh Kumar; M.V. Aditya Nag; Bobin Cherian Jos; Bos Mathew Jos

Addresses: Department of ISE, BMSIT&M, Doddaballapur Main Road, Avalahalli, Yelahanka, Bengaluru, Karnataka, India ' Department of CS&E, Krishna Engineering College, Ghaziabad, India ' School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India ' Department of Mechanical Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad 500043, Telangana State, India ' Mechanical Engineering Department, Mar Athanasius College of Engineering, Kothamangalam, Kerala, 686666, India ' Department of Electrical and Electronics Engineering, Mar Athanasius College of Engineering, Kothamangalam, Kerala, 686666, India

Abstract: There are numerous types of additive manufacturing equipment, ranging from basic RepRap machinery to sophisticated fused metal depositing systems. Lightweight items, lower errors, minimum tool costs, optimum components usage, and feasible manufacturing method are depending on field implementation. Smart agents powered by AI can help minimise the number of people needed to raise AM productivity with the simultaneous improvement in source usage. The present state of AI-enabled AM item enhancement is explored in this paper. Artificial intelligence (AI), digitalised reality, internet of things (IoT), blockchain, driverless cars, and future innovations are all dependent on 5G's lightning-fast connectivity and minimal latency. The launch of 5G is much more beyond a generational shift; it ushers in a new period of opportunities for the IT sector. Based on the original objectives and expectations of both domains, the goal of this article is to develop a method that integrates AI with additive manufacturing systems.

Keywords: artificial intelligence; additive manufacturing; 5G; industrial internet of things; I-IoT.

DOI: 10.1504/IJESMS.2022.126304

International Journal of Engineering Systems Modelling and Simulation, 2022 Vol.13 No.4, pp.235 - 240

Received: 25 Jun 2021
Accepted: 16 Aug 2021
Published online: 19 Oct 2022 *

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