Autonomous Machine Tools – Vision or Soon Reality?Prof. Dr.-Ing. Berend Denkena |
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The presentation is based on the automation stages of autonomous driving and their adaptation to machine tools.Examples are used to discuss key technologies that will be required for autonomous production in the future.One focus is on innovative sensor technologies and the use of sensor data to enable autonomous machine tools. Topics such as process monitoring, extended process control using additional actuators and semi-autonomous process planning approaches using intelligent planning algorithms are covered. Furthermore, it is discussed how learning of machine tools can be achieved. This is followed by an example of a semi-autonomous production cell for medical implants developed in collaboration with industrial partners. Finally, an attempt is made to answer the question of if or when we will be able to manufacture autonomously. |
Smart Manufacturing with a Decentralized and Distributed RegimeProf. Xun Xu |
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In an effort to take on the unprecedented challenges of volatile global demand, together with the mandate of sustainable digital transformation, manufacturing systems need to be robust, agile, and smart with sometimes extreme ASAP-delivery capabilities. Contemporary digital capabilities and advances over widespread networks promise new ways of meeting the challenges. These capabilities are for example high-power computation, cloud and edge computing services, and lower-cost sensing, many of which have been captured in Industry 4.0 in the form of the Industrial Internet, Cyber-physical Production Systems, and Digital Twins. This talk intends to address manufacturing control and manufacturing intelligence in a distributed and decentralised manner for smart manufacturing where distributed and autonomously acting components, machines, service robots, and other systems work collectively to give adaptability, flexibility, and even self-healing and self-learning characteristics. The concept and related enabling technologies also contribute to a factory-factory co-manufacturing paradigm, i.e. cloud manufacturing. Distributed control of manufacturing equipment at the field level is a challenge; so is distributed decision-making and intelligence for a manufacturing system. When machine control with real-time requirements is distributed among the network-connected nodes, what machining error does the control architecture induce, and how to quantify the error? When a disturbance occurs in a manufacturing system where multiple facilities are present, how does the system adapt to the disturbance for continued production? These are some of the questions that this talk intends to shed light on some possible solutions.
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