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(Ebook) Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials by Deepak Sinwar (editor), Kamalakanta Muduli (editor), Vijaypal Singh Dhaka (editor), Vijander Singh (editor) ISBN 9781032191041, 103219104X

  • SKU: EBN-51589808
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$ 32 $ 40 (-20%)

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4.7

32 reviews
Instant download (eBook) Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials after payment.
Authors:Deepak Sinwar (editor), Kamalakanta Muduli (editor), Vijaypal Singh Dhaka (editor), Vijander Singh (editor)
Pages:196 pages.
Year:2023
Editon:1
Publisher:CRC Press
Language:english
File Size:39.71 MB
Format:pdf
ISBNS:9781032191041, 103219104X
Categories: Ebooks

Product desciption

(Ebook) Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials by Deepak Sinwar (editor), Kamalakanta Muduli (editor), Vijaypal Singh Dhaka (editor), Vijander Singh (editor) ISBN 9781032191041, 103219104X

The text comprehensively discusses computational models including artificial neural networks, agent-based models, and decision field theory for reliability engineering. It will serve as an ideal reference text for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, computer engineering, and materials science.Discusses the development of sustainable materials using metaheuristic approaches. Covers computational models such as agent-based models, ontology, and decision field theory for reliability engineering. Presents swarm intelligence methods such as ant colony optimization, particle swarm optimization, and grey wolf optimization for solving the manufacturing process. Include case studies for industrial optimizations. Explores the use of computational optimization for reliability and maintainability theory. The text covers swarm intelligence techniques including ant colony optimization, particle swarm optimization, cuckoo search, and genetic algorithms for solving complex industrial problems of the manufacturing industry as well as predicting reliability, maintainability, and availability of several industrial components.
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