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Special Issue on Self-Optimizing Machining Systems

Authors :
Yasuhiro Kakinuma
Daisuke Kono
Source :
International Journal of Automation Technology. 16:125-125
Publication Year :
2022
Publisher :
Fuji Technology Press Ltd., 2022.

Abstract

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.

Details

ISSN :
18838022 and 18817629
Volume :
16
Database :
OpenAIRE
Journal :
International Journal of Automation Technology
Accession number :
edsair.doi...........8055cc500c48322a80aa4848188bb05f
Full Text :
https://doi.org/10.20965/ijat.2022.p0125