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

• Published in: Journal of Manufacturing and Materials Processing Vol. 4; no. 1; p. 11
• Main Authors: Ghosh, Angkush Kumar, Ullah, AMM Sharif, Kubo, Akihiko, Akamatsu, Takeshi, D’Addona, Doriana Marilena
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.03.2020
• Subjects: complex phenomenon; cyber-physical systems; digital twin; dna-based computing; industry 4.0; Machining; markov chain; Markov processes; Mathematical models; monte carlo simulation; semantic modeling; surface roughness; Japan
• ISSN: 2504-4494; 2504-4494
• DOI: 10.3390/jmmp4010011

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—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.