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Intelligent Machining and Grinding
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Intelligent Machining and Grinding

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 9634

Special Issue Editors

Prof. Dr. Sharifu Ura

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Guest Editor
Division of Mechanical and Electrical Engineering, Kitami Institute of Technology, Kitami 090-8507, Japan
Interests: Industry 4.0; 3D Printing; sustainable product development; engineering education
Special Issues, Collections and Topics in MDPI journals
Dr. Takeshi Akamatsu

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Guest Editor
Mitsubishi Hitachi Tool Engineering, Ltd., Tokyo, Japan
Interests: high precision machining; die and mold milling; Industry 4.0; product development
Prof. Dr. Doriana Marilena D'Addona

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Guest Editor
Department of Chemical, Materials and Industrial Production Engineering, Piazzale Tecchio 80, 80125 Naples, Italy
Interests: manufacturing engineering; manufacturing processes and automation; intelligent computation for materials and manufacturing engineering reconfigurable machine tools; bio-inspired algorithms for optimization of production systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machining (e.g., turning, drilling, and milling) and grinding are subtractive manufacturing processes that have extensively been used to manufacture both ordinary and precision parts. These processes involve engineering materials, machine tools, cutting tools, jigs/fixtures, cutting fluids, surface metrology, sensors, actuators, material handling systems, part geometry and topology, human skills, and process planning. In addition, nowadays concepts called digital twins, cyber-physical systems, Internet of Things, and Big-data have become an integrated part of machining and grinding. This has created a dynamic and ever-changing premise for machining and grinding.

This special issue, in particular, welcomes papers from all over the world on the following topics (but not limited to):

Conceptual framework of intelligent machining/grinding;
Industry 4.0-based machining/grinding;
Computational intelligence (GA, ANN, Fuzzy Logic, and alike) in machining/grinding;
Artificial intelligence in machining/grinding;
Innovation/creativity in developing machining/grinding related devices;
Intelligent surface metrology for machining/grinding;
Intelligent signal processing for machining/grinding;
Intelligent machining/grinding for special materials (e.g., compositions and polymers);
Design of Experiment-based optimization of machining/grinding;
Implementing intelligent machining/grinding systems;
Machining/grinding-integrated additive manufacturing;
Sustainable machining/grinding;
Intelligent monitoring of machining/grinding;
Big-data for machining/grinding;

Prof. Dr. AMM Sharif Ullah
Dr. Takeshi Akamatsu
Dr. Doriana Marilena D'Addona
Guest Editors

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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13 pages, 11638 KiB  
Article
Optimization of Wet Grinding Conditions of Sheets Made of Stainless Steel
by Akira Mizobuchi and Atsuyoshi Tashima
J. Manuf. Mater. Process. 2020, 4(4), 114; https://doi.org/10.3390/jmmp4040114 - 7 Dec 2020
Cited by 2 | Viewed by 3285
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Intelligent Machining and Grinding)
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19 pages, 3898 KiB  
Article
Machining Phenomenon Twin Construction for Industry 4.0: A Case of Surface Roughness
by Angkush Kumar Ghosh, AMM Sharif Ullah, Akihiko Kubo, Takeshi Akamatsu and Doriana Marilena D’Addona
J. Manuf. Mater. Process. 2020, 4(1), 11; https://doi.org/10.3390/jmmp4010011 - 11 Feb 2020
Cited by 24 | Viewed by 5220
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Intelligent Machining and Grinding)
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