The Paradigm of Self-optimization

Dellnitz M, Dumistrescu R, Flasskamp K, Gausemeier J, Korf S, Porrmann M (2014)
In: Design Methodology for Intelligent Technical Systems – Develop Intelligent Technical Systems of the Future. Gausemeier J, Rammig F-J, Schäfer W (Eds); Lecture notes in mechanical engineering. Berlin Heidelberg: Springer: 1-25.

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Sammelwerksbeitrag | Veröffentlicht | Englisch
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Abstract / Bemerkung
Machines are ubiquitous. They produce, they transport. Machines facilitate and assist with work. The increasing fusion of mechanical engineering with information technology has brought about considerable benefits. This situation is expressed by the term mechatronics, which means the close interaction of mechanics, electrics/electronics, control engineering and software engineering to improve the behavior of a technical system. The integration of cognitive functions into mechatronic systems enables systems to have inherent partial intelligence. The behavior of these future systems is formed by the communication and cooperation of the intelligent system elements. From an information processing point of view, we consider these distributed systems to be multi-agent-systems. These capabilities open up fascinating prospects regarding the design of future technical systems. The term self-optimization characterizes this perspective: the endogenous adaptation of the system’s objectives due to changing operational conditions. This resuls in an autonomous adjustment of system parameters or system structure and consequently of the system’s behavior. In this chapter self-optimizing systems are described in detail. The long term aim of the Collaborative Research Centre 614 ”Self-Optimizing Concepts and Structures in Mechanical Engineering” is to open up the active paradigm of self-optimization for mechanical engineering and to enable others to develop these systems. For this, developers have to face a number of challenges, e.g. the multidisciplinarity and the complexity of the system. This book povides a design methodology that helps to master these challenges and to enable third parties to develop self-optimizing systems by themselves.
Erscheinungsjahr
Buchtitel
Design Methodology for Intelligent Technical Systems – Develop Intelligent Technical Systems of the Future
Seite(n)
1-25
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Dellnitz M, Dumistrescu R, Flasskamp K, Gausemeier J, Korf S, Porrmann M. The Paradigm of Self-optimization. In: Gausemeier J, Rammig F-J, Schäfer W, eds. Design Methodology for Intelligent Technical Systems – Develop Intelligent Technical Systems of the Future. Lecture notes in mechanical engineering. Berlin Heidelberg: Springer; 2014: 1-25.
Dellnitz, M., Dumistrescu, R., Flasskamp, K., Gausemeier, J., Korf, S., & Porrmann, M. (2014). The Paradigm of Self-optimization. In J. Gausemeier, F. - J. Rammig, & W. Schäfer (Eds.), Lecture notes in mechanical engineering. Design Methodology for Intelligent Technical Systems – Develop Intelligent Technical Systems of the Future (pp. 1-25). Berlin Heidelberg: Springer. doi:10.1007/978-3-642-45435-6_1
Dellnitz, M., Dumistrescu, R., Flasskamp, K., Gausemeier, J., Korf, S., and Porrmann, M. (2014). “The Paradigm of Self-optimization” in Design Methodology for Intelligent Technical Systems – Develop Intelligent Technical Systems of the Future, Gausemeier, J., Rammig, F. - J., and Schäfer, W. eds. Lecture notes in mechanical engineering (Berlin Heidelberg: Springer), 1-25.
Dellnitz, M., et al., 2014. The Paradigm of Self-optimization. In J. Gausemeier, F. - J. Rammig, & W. Schäfer, eds. Design Methodology for Intelligent Technical Systems – Develop Intelligent Technical Systems of the Future. Lecture notes in mechanical engineering. Berlin Heidelberg: Springer, pp. 1-25.
M. Dellnitz, et al., “The Paradigm of Self-optimization”, Design Methodology for Intelligent Technical Systems – Develop Intelligent Technical Systems of the Future, J. Gausemeier, F.-J. Rammig, and W. Schäfer, eds., Lecture notes in mechanical engineering, Berlin Heidelberg: Springer, 2014, pp.1-25.
Dellnitz, M., Dumistrescu, R., Flasskamp, K., Gausemeier, J., Korf, S., Porrmann, M.: The Paradigm of Self-optimization. In: Gausemeier, J., Rammig, F.-J., and Schäfer, W. (eds.) Design Methodology for Intelligent Technical Systems – Develop Intelligent Technical Systems of the Future. Lecture notes in mechanical engineering. p. 1-25. Springer, Berlin Heidelberg (2014).
Dellnitz, Micael, Dumistrescu, Roman, Flasskamp, Kathrin, Gausemeier, Jürgen, Korf, Sebastian, and Porrmann, Mario. “The Paradigm of Self-optimization”. Design Methodology for Intelligent Technical Systems – Develop Intelligent Technical Systems of the Future. Ed. Jürgen Gausemeier, Franz-Josef Rammig, and Wilhelm Schäfer. Berlin Heidelberg: Springer, 2014. Lecture notes in mechanical engineering. 1-25.

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