TY - JOUR
T1 - Self-optimizing machining systems
AU - Möhring, H. C.
AU - Wiederkehr, P.
AU - Erkorkmaz, K.
AU - Kakinuma, Y.
N1 - Funding Information:
The authors sincerely thank all the colleagues who contributed to this keynote paper and to the related CIRP CWG. We especially thank Mr. Chia-Pei Wang and Mr. Andrew Katz for their support and valuable comments. The support of the Technical University of Munich - Institute for Machine Tools and Industrial Management (TUM-iwb) is also gratefully acknowledged.
Publisher Copyright:
© 2020 CIRP
PY - 2020
Y1 - 2020
N2 - In this paper the idea of Self-Optimizing Machining Systems (SOMS) is introduced and discussed. Against the background of Industry 4.0, here the focus is the technological level of discrete workpiece production by mechanical machining processes utilizing related machine tools and equipment. Enabling technologies, principles, and methods are described that allow for the implementation of machining systems which are capable of adapting their parameters and settings autonomously, in order to optimize for productivity, quality, and efficiency in manufacturing. Following a description of the meaning and a definition of SOMS as well as a historical retrospection, the required elements of SOMS are discussed and exemplary approaches are presented. Based on sophisticated process planning, monitoring, adaptive control, simulation, artificial intelligence, and machine learning, strategies, state-of-the-art solutions for self-optimization in machining applications are introduced. Several examples showcase how different types of enabling technologies can be integrated synergistically, to improve the manufacturing of parts by SOMS. Finally, the future potential of SOMS as well as challenges and needs are summarized. The paper especially considers the results of the CIRP Cross-STC Collaborative Working Group on SOMS.
AB - In this paper the idea of Self-Optimizing Machining Systems (SOMS) is introduced and discussed. Against the background of Industry 4.0, here the focus is the technological level of discrete workpiece production by mechanical machining processes utilizing related machine tools and equipment. Enabling technologies, principles, and methods are described that allow for the implementation of machining systems which are capable of adapting their parameters and settings autonomously, in order to optimize for productivity, quality, and efficiency in manufacturing. Following a description of the meaning and a definition of SOMS as well as a historical retrospection, the required elements of SOMS are discussed and exemplary approaches are presented. Based on sophisticated process planning, monitoring, adaptive control, simulation, artificial intelligence, and machine learning, strategies, state-of-the-art solutions for self-optimization in machining applications are introduced. Several examples showcase how different types of enabling technologies can be integrated synergistically, to improve the manufacturing of parts by SOMS. Finally, the future potential of SOMS as well as challenges and needs are summarized. The paper especially considers the results of the CIRP Cross-STC Collaborative Working Group on SOMS.
KW - Machine tool
KW - Machining
KW - Process control
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U2 - 10.1016/j.cirp.2020.05.007
DO - 10.1016/j.cirp.2020.05.007
M3 - Article
AN - SCOPUS:85087776784
SN - 0007-8506
VL - 69
SP - 740
EP - 763
JO - CIRP Annals - Manufacturing Technology
JF - CIRP Annals - Manufacturing Technology
IS - 2
ER -