@article{oai:kitami-it.repo.nii.ac.jp:02000234,
 issue = {1},
 journal = {Journal of Manufacturing and Materials Processing},
 month = {Feb},
 note = {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 eective 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 eective 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 eectively},
 title = {Machining Phenomenon Twin Construction for Industry 4.0: A Case of Surface Roughness},
 volume = {4},
 year = {2020}
}