Self-optimizing machining systems

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. (c) 2020 CIRP. Published by Elsevier Ltd. All rights reserved.