Jürgen Gausemeier – författare

"Virtual Reality & Augmented Reality in Industry" collects the proceedings of the 2nd Sino-German Workshop on the same topic held in Shanghai on April 16-17, 2009. The papers focus on the latest Virtual Reality (VR) / Augmented Reality (AR) technology and its application in industrial processes and presents readers with innovative methods, typical case studies and the latest information on VR/AR basic research results and industrial applications, such as 3D rendering, innovative human-machine design, VR/AR methodology and new tools for assisting in industry, virtual assembly, virtual factory, training and education, etc.

The book is intended for computer scientists, IT engineers as well as researchers in Mechanical Engineering.

Dr. Dengzhe Ma and Dr. Xiumin Fan are both professors at Shanghai Jiao Tong University, China; Dr.-Ing. Jürgen Gausemeier is a professor of Computer-Integrated Manufacturing at the Heinz Nixdorf Institute, University of Paderborn, Germany; Dipl.-Ing. Michael Grafe is a senior engineer in the Product Engineering Research Group at the Heinz Nixdorf Institute, University of Paderborn.

Intelligent technical systems, which combine mechanical, electrical and software engineering with control engineering and advanced mathematics, go far beyond the state of the art in mechatronics and open up fascinating perspectives. Among these systems are so-called self-optimizing systems, which are able to adapt their behavior autonomously and flexibly to changing operating conditions. Self-optimizing systems create high value for example in terms of energy and resource efficiency as well as reliability.

The Collaborative Research Center 614 "Self-optimizing Concepts and Structures in Mechanical Engineering" pursued the long-term aim to open up the active paradigm of self-optimization for mechanical engineering and to enable others to develop self-optimizing systems.

This book is directed to researchers and practitioners alike. It provides a design methodology for the development of self-optimizing systems consisting of a reference process, methods, and tools. The reference process is divided into two phases the domain-spanning conceptual design and the domain-specific design and development. For the conceptual design a holistic approach is provided. Domain-specific methods and tools developed especially for the design and development of self-optimizing systems are described and illustrated by application examples. This book will enable the reader to identify the potential for self-optimization and to develop self-optimizing systems independently.

Intelligent technical systems, which combine mechanical, electrical and software engineering with methods from control engineering and advanced mathematics, go far beyond the state of the art in mechatronics and open up fascinating perspectives. Among these systems are so-called self-optimizing systems, which are able to adapt their behavior autonomously and flexibly to changing operating conditions. The Collaborative Research Center 614 "Self-optimizing concepts and structures in mechanical engineering" pursued the long-term aim to enable others to develop dependable self-optimizing systems. Assuring their dependability poses new challenges. However, self-optimization also offers the possibility to adapt the system''s behavior to improve dependability during operation.

The aim of this book is to provide methods and techniques to master the challenges and to exploit the possibilities given by self-optimization. The reader will be able to develop self-optimizing systems that fulfill and surpass today’s dependability requirements easily.

This book is directed to researchers and practitioners alike. It gives a brief introduction to the holistic development approach for self-optimizing mechatronic systems and the steps required to assure a dependable product design starting with the very early conceptual design phase. A guideline to select suitable methods for each step and the methods themselves are included. Each method is individually introduced, many examples and full references are given.

Das Buch beschreibt die Ergebnisse des Querschnittsprojekts „Selbstoptimierung“ des vom BMBF geförderten Spitzencluster-Projekts „Intelligente Technische Systeme OstWestfalenLippe“ (kurz: it’s OWL).

Die Entwicklung der Informations- und Kommunikationstechnik eröffnet neue Perspektiven für intelligente technische Systeme, die hohen Kundennutzen stiften. Diese Systeme sind in der Lage, sich ihrer Umgebung und den Wünschen ihrer Anwender im Betrieb anzupassen. Für den Innovationssprung von der Mechatronik hin zu selbstoptimierenden Systemen wird Expertise aus den Gebieten Maschinelles Lernen, mathematische Optimierungsverfahren, Regelungstechnik und Condition Monitoring benötigt.

Zielsetzung des Cluster-Querschnittsprojekts Selbstoptimierung war ein Instrumentarium zur Planung und Entwicklung von selbstoptimierenden Produkten und Produktionssystemen. Dieses soll die Entwickler in den Unternehmen bei der Realisierung von Ansätzen der Selbstoptimierung praxisgerecht unterstützen. Das vorliegende Buch stellt das erarbeitete Instrumentarium mit den entsprechenden Schwerpunkten im Detail vor. Dabei werden die wesentlichen Grundlagen der jeweiligen Fachgebiete gezeigt und die methodische Umsetzung anhand von Praxisbeispielen aus dem Spitzencluster erläutert.

Vor dem Hintergrund von sich schnell verändernden Rahmenbedingungen wird die Fähigkeit fertigender Unternehmen auf kurzfristige Auftragsschwankungen zu reagieren in zunehmenden Maß entscheidend für den zukünftigen Fortbestand und Erfolg produzierender Unternehmen. Ein Beitrag der Industrie 4.0 diese Kompetenz weiter zu stärken besteht in vernetzten Fertigungsanlagen und intelligenten Systemen zur Unterstützung von Planungsprozessen, auf deren Grundlagen jede Auftragsänderung im Unternehmen abgebildet wird.