Machining Phenomenon Twin Construction for Industry 4.0: A Case of Surface Roughness

Autor: Amm Sharif Ullah, Doriana M. D’Addona, Akihiko Kubo, Takeshi Akamatsu, Angkush Kumar Ghosh
Přispěvatelé: Angkush Kumar Ghosh, AMM Sharif Ullah, Akihiko, Kubo, Takeshi, Akamatsu, D'Addona, DORIANA MARILENA
Jazyk: angličtina
Rok vydání: 2020
Předmět:
0209 industrial biotechnology
Computer science
Computational intelligence
02 engineering and technology
Surface finish
cyber-physical systems
Industrial and Manufacturing Engineering
complex phenomenon
020901 industrial engineering & automation
Machining
Component (UML)
digital twin
0202 electrical engineering
electronic engineering
information engineering
Surface roughness
industry 4.0
Abstraction (linguistics)
lcsh:T58.7-58.8
semantic modeling
Markov chain
Mechanical Engineering
Process (computing)
monte carlo simulation
dna-based computing
markov chain
Mechanics of Materials
surface roughness
Industry 4.0
cyber-physical systems
digital twin
surface roughness
complex phenomenon
semantic modeling
Monte Carlo simulation
DNA-based computing
Markov chain
020201 artificial intelligence & image processing
lcsh:Production capacity. Manufacturing capacity
Algorithm
Zdroj: Journal of Manufacturing and Materials Processing, Vol 4, Iss 1, p 11 (2020)
Journal of Manufacturing and Materials Processing
Volume 4
Issue 1
ISSN: 2504-4494
Popis: Industry 4.0 requires phenomenon twins to functionalize the relevant systems (e.g., cyber-physical systems). A phenomenon twin means a computable virtual abstraction of a real phenomenon. In order to systematize the construction process of a phenomenon twin, this study proposes a system defined as the phenomenon twin construction system. It consists of three components, namely the input, processing, and output components. Among these components, the processing component is the most critical one that digitally models, simulates, and validates a given phenomenon extracting information from the input component. What kind of modeling, simulation, and validation approaches should be used while constructing the processing component for a given phenomenon is a research question. This study answers this question using the case of surface roughness&mdash
a complex phenomenon associated with all material removal processes. Accordingly, this study shows that for modeling the surface roughness of a machined surface, the approach called semantic modeling is more effective than the conventional approach called the Markov chain. It is also found that to validate whether or not a simulated surface roughness resembles the expected roughness, the outcomes of the possibility distribution-based computing and DNA-based computing are more effective than the outcomes of a conventional computing wherein the arithmetic mean height of surface roughness is calculated. Thus, apart from the conventional computing approaches, the leading edge computational intelligence-based approaches can digitize manufacturing processes more effectively.
Databáze: OpenAIRE
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