Siemens Aktiengesellschaft

Yi Huang

Volker Tresp

Andreas Graichen

Siegmund Düll

Mikhail Roshchin

Clemens Otte

Jonas Eriksson

Ingo Thon

Jerry Fornander

Felix Buggenthin

Mitchell Joblin

Axel Reitinger

Victor Balanica

Sunil Viswanathan

Vincent Sidenvall

Michael Caelers

Maximilian Nickel

Xueyan Jiang

Kiryl Batsiukov

Martin C Müller

The present invention provides an enhanced setup of a 3D-printing device, especially to a laser powder bed fusion 3D-printing device. It is for this purpose, that data relating to previously printed products are stored in a database. When a new product is to be printed, the features of the new product are matched with features of previously printed products stored in the database. Accordingly, a suggestion for setting-up the 3D-printing device based on corresponding previously printed products and their setup parameters can be automatically determined and applied to the 3D-printing device.

Method and apparatus for controlling a 3D-printing device and 3D-printing device

Systems and methods are provided for deriving a prediction from existing data by utilizing information extraction and machine learning, wherein both approaches can be optimized independently from each other. Optionally, deductive reasoning may also be combined with information extraction and machine learning and may as well be optimized independently from the other two functionalities. The two or three functionalities may utilize at least one set of data and may (at least partially) process various sets of data. The combined approach may produce significantly improved results, and may be implemented in various technical fields, applications and use cases involving, e.g., data mining or processing of huge amounts of data. The disclosed systems and methods may be applicable for all kinds of technical systems, e.g., medical, genetic research, or industry and automation systems.

Deriving predictions from data using information extraction and machine learning

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Markt Schwaben, Germany

Yi Huang