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IJAT Vol.16 No.2 p. 125
doi: 10.20965/ijat.2022.p0125
(2022)

Editorial:

Special Issue on Self-Optimizing Machining Systems

Yasuhiro Kakinuma and Daisuke Kono

Keio University
Kohoku-ku, Yokohama, Japan

Kyoto University
Nishikyo-ku, Kyoto, Japan

Published:
March 5, 2022

The concept of Self-Optimizing Machining Systems (SOMS) has been proposed against the background of Industry 4.0 and the Digital Twin concept, based on cyber-physical systems. In order to improve manufacturing productivity, quality, and efficiency, each component technology related to the machining process, such as CAD/CAM, process modeling/simulation, process monitoring/control, and workpiece assessment, as well as the machine tools themselves, has been developed independently to date. However, series of processes, including the interactions among these component technologies, have finally determined the machining performance and the quality of the products. SOMS deals with the information links among these components comprehensively and plays the important role of combining these links and functionalities to optimize the overall machining system. Nevertheless, an intensive implementation and combination of these technologies has yet to become state-of-the-art in industry, while further research and development for SOMS is required for Industry 4.0 and Digital Twin.

This special issue focuses on the research trends of SOMS, especially the interaction links among machine tools, process monitoring, and work assessment. From researchers who are active on the front lines of manufacturing engineering, the latest achievements related to the development of SOMS are presented in 6 papers. On one hand, the development of sensor-integrated components is indispensable for SOMS to monitor the status of a process and feed it back to a related component in order to control the machining process and its environment. On the other hand, it can be said that visual simulation, virtual metrology, and other epoch-making, on-machine technologies for evaluating machined surfaces, as well as process optimization based on machined surface information, are strongly required.

We hope this special issue will contribute to future research and development for researchers and engineers in the field of manufacturing and machining systems.

Cite this article as:
Yasuhiro Kakinuma and Daisuke Kono, “Special Issue on Self-Optimizing Machining Systems,” Int. J. Automation Technol., Vol.16, No.2, p. 125, 2022.
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Last updated on May. 20, 2022