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Hybrid Modeling and Optimization of Manufacturing Combining Artificial Intelligence and Finite Element Method by RamГіn Quiza

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Published by Springer Berlin Heidelberg in Berlin, Heidelberg .
Written in English

Subjects:

  • Engineering,
  • Materials,
  • Structural control (Engineering),
  • Continuum Mechanics and Mechanics of Materials,
  • Materials Treatment Operating Procedures,
  • Computational intelligence

Book details:

Edition Notes

Statementby Ramón Quiza, Omar López-Armas, J. Paulo Davim
SeriesSpringerBriefs in Applied Sciences and Technology
ContributionsLópez-Armas, Omar, Davim, J. Paulo, SpringerLink (Online service)
The Physical Object
Format[electronic resource] :
ID Numbers
Open LibraryOL27044405M
ISBN 109783642280856

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  Read "Hybrid Modeling and Optimization of Manufacturing Combining Artificial Intelligence and Finite Element Method" by Ramón Quiza available from Rakuten Kobo. Artificial intelligence (AI) techniques and the finite element method Brand: Springer Berlin Heidelberg. This book covers various multiple-criteria decision making methods for modeling and optimization of advanced manufacturing processes. Processes such as non-conventional machining, rapid prototyping, environmentally conscious machining and hybrid machining are finally put together in a single book. 4th Hybrid Modeling Summer School 25th – 27th of September Faculty of Science and Technology, Universidade Nova de Lisboa, Portugal. Homepage and registration. Why participating? 4th edition of a well-established summer school on the topic of hybrid modelling for process development, characterization and optimization promoted by ESBES. Hybrid Modeling and Optimization of Manufacturing: Combining Artificial Intelligence and Finite Element Method (SpringerBriefs in Applied Sciences and Technology) eBook: Ramón Quiza, Omar López-Armas, J. Paulo Davim: : Kindle Store.

Get this from a library! Hybrid modeling and optimization of manufacturing: combining artificial intelligence and finite element method. [Ramón Quiza; Omar López-Armas; J Paulo Davim] -- Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes.   The above outlined features of hybrid models have the potential to save resources, reduce timelines, and improve manufacturing. If hybrid modeling could work for you, consider also checking out this article (in addition to the articles listed above) and this course as one starting point. This title includes chapters covering topics such as soft computing techniques, composite materials engineering, design and manufacturing of composite materials, numerical modeling, prediction, and optimization of the composite materials performance, development of the hybrid models, and control of the composite material performance. Hybrid Modeling and Optimization of Manufacturing: Combining Artificial Intelligence and Finite Element Method Uday S. Dixit (Indian Institute of Technology Guwahati, Guwahati, India) Source Title: International Journal of Manufacturing, Materials, and Mechanical Engineering (IJMMME) 2(4)Author: Uday S. Dixit.

Get this from a library! Modeling and optimization of advanced manufacturing processes. [Sumit Bhowmik; Jagadish; Kapil Gupta] -- This book covers various multiple-criteria decision making (mcdm) methods for modeling and optimization of advanced manufacturing processes (AMPs). Processes such as non-conventional machining, rapid. The Handbook of Research on Manufacturing Process Modeling and Optimization Strategies is a pivotal reference source for the latest research on the various manufacturing methodologies and. Optimization Vocabulary Your basic optimization problem consists of •The objective function, f(x), which is the output you’re trying to maximize or minimize. •Variables, x 1 x 2 x 3 and so on, which are the inputs – things you can control. They are abbreviated x n . Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm: /ch This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problemAuthor: Alexandros Xanthopoulos, Dimitrios E. Koulouriotis.