Hidden Markov model-based digital twin construction for futuristic manufacturing systems. Issue 3 (3rd May 2019)

• Record Type: Journal Article
• Authors: Ghosh, Angkush Kumar; Ullah, AMM Sharif; Kubo, Akihiko
• Is Part Of: AI EDAM. Volume 33:Issue 3(2019)
• Page Start: 317
• Page End: 331
• Publication Date: 2019-05-03
• Subjects: Complex phenomena, -- digital twin, -- hidden Markov model, -- manufacturing systems, -- surface roughness; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S089006041900012X
• Languages: English
• ISSNs: 0890-0604

Abstract

Abstract: This paper addresses the construction of digital twins (exact mirror images of real-world in cyberspace) using hidden Markov models for the futuristic manufacturing systems known as Industry 4.0. The proposed digital twin consists of two components namely model component and simulation component. The model component forms a Markov chain that encapsulates the dynamics underlying the phenomenon by using some discrete states and their transition probabilities. The simulation component recreates the phenomenon using a Monte Carlo simulation process. The efficacy of the proposed digital twin construction methodology is shown by a case study, where the digital twin of the surface roughness of a surface created by successive grinding operations is described. The developers of the cyber-physical systems will be benefitted from the outcomes of this study because these systems need the computable virtual abstractions of the manufacturing phenomena to address the issues related to the maturity index of futuristic manufacturing systems (i.e., understand, predict, decide, and adopt).