Automatic shape variations for optimization and innovation

León, Noel; Cueva, Jose Maria; Villarreal, Cesar; Hutron, Sergio; Campero, German

doi:10.1007/978-0-387-75456-7_18

Cited by 8 publications

(1 citation statement)

References 5 publications

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“…Finally, the selection separates cases into optimization and innovation. Optimization is based on the changes in the parametric design until no further performance improvements are possible, whereas innovation requires a novel approach carried out through qualitative changes and not just the variation of parametric values (Leon et al 2007). Based on the premise of searching for invention systems, cases of pure optimization were removed from the analyses.…”

Section: State-of-the-art Applicationsmentioning

confidence: 99%

Machine invention systems: a (r)evolution of the invention process?

Vasilescu

Filzmoser

2020

AI & Soc

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Current developments in fields such as quantum physics, fine arts, robotics, cognitive sciences or defense and security indicate the emergence of creative systems capable of producing new and innovative solutions through combinations of machine learning algorithms. These systems, called machine invention systems, challenge the established invention paradigm in promising the automation of – at least parts of – the innovation process. This paper’s main contribution is twofold. Based on the identified state-of-the-art examples in the above mentioned fields, key components for machine invention systems and their relations are identified, creating a conceptual model as well as proposing a working definition for machine invention systems. The differences and delimitations to other concepts in the field of machine learning and artificial intelligence, such as machine discovery systems are discussed as well. Furthermore, the paper briefly addresses the social and societal implications and limitations that come with the adoption of the technology. Because of their revolutionizing potential, there are widespread implications to consider from ethical and moral implications to policymaking and societal changes, like changes in the job structure. The discussion part approaches some of these implications, as well as solutions to some of the proposed challenges. The paper concludes by discussing some of the systemic benefits that can be accessed through machine invention.

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Section: State-of-the-art Applicationsmentioning

confidence: 99%

Machine invention systems: a (r)evolution of the invention process?

Vasilescu

Filzmoser

2020

AI & Soc

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Optimization with Genetic Algorithms and Splines as a way for Computer Aided Innovation

Albers

León-Rovira²,

Aguayo

et al.

The International Federation for Information Processing

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This paper describes the conceptual foundations to construct a method on Computer Aided Innovation for product development. It begins with a brief recap of the different methodologies and disciplines that build its bases. Evolutionary Design is presented and explained how the first activities in Genetic Algorithms (GAs) helped to produce computer shapes that resembled a creative behavior. A description of optimization processes based on Genetic Algorithms is presented, and some of the genetic operators are explained as a background of the creative operators that are intended to be developed. A summary of some Design Optimization Systems is also explained and its use of splined profiles to optimize mechanical structures. The approach to multi-objective optimization with Genetic Algorithms is analyzed from the point of view of Pareto diagrams. It is discussed how the transition from a multi-objective optimization conflict to a solution with the aim of an ideal result can be developed means the help of TRIZ (Theory of Inventive Problem Solving), complementing the discipline of Evolutionary Design. Similarities between Genetic Algorithms and TRIZ regarding ideality and evolution are identified and presented. Finally, a brief presentation of a case study about the design of engine crankshafts is used to explain the concepts and methods deployed. The authors have been working on strategies to optimize the balance of a crankshaft using CAD and CAE software, splines, Genetic Algorithms, and tools for its integration [1] [2].

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Feature-based spline optimization in CAD

Weiß

2010

Struct Multidisc Optim

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Automatic shape variations for optimization and innovation

Cited by 8 publications

References 5 publications

Machine invention systems: a (r)evolution of the invention process?

Machine invention systems: a (r)evolution of the invention process?

Optimization with Genetic Algorithms and Splines as a way for Computer Aided Innovation

Feature-based spline optimization in CAD

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