This study addresses the wet grinding of large stainless steel sheets, because it is difficult to subject them to dry grinding. Because stainless steel has a low thermal conductivity and a high coefficient of thermal expansion, it easily causes grinding burn and thermal deformation while dry grinding on the wheel without applying a cooling effect. Therefore, wet grinding is a better alternative. In this study, we made several types of grinding wheels, performed the wet grinding of stainless steel sheets, and identified the wheels most suitable for the process. As such, this study developed a special accessory that could be attached to a wet grinding workpiece. The attachment can maintain constant pressure, rotational speed, and supply grinding fluid during work. A set of experiments was conducted to see how some grinding wheels subjected to some grinding conditions affected the surface roughness of a workpiece made of a stainless steel sheet (SUS 304, according to Japanese Industrial Standards: JIS). It was found that the roughness of the sheet could be minimized when a polyvinyl alcohol (PVA) grinding wheel was used as the grinding wheel and tap water was used as the grinding fluid at an attachment pressure of 0.2 MPa and a rotational speed of 150 rpm. It was shown that a surface roughness of up to 0.3 μm in terms of the arithmetic average height could be achieved if the above conditions were satisfied during wet grinding. The final surface roughness was 0.03 μm after finish polishing by buffing. Since the wet grinding of steel has yet to be studied in detail, this article will serve as a valuable reference. Full article

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. Full article