Computational intelligence based optimization of manufacturing process for sustainable materials:
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, ma...
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| Main Author: | |
|---|---|
| Other Authors: | , , |
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2024
|
| Edition: | First edition |
| Series: | Computational and intelligent systems series
|
| Subjects: | |
| Summary: | 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 |
| Item Description: | Chapter 1 ; Introduction to Computational Intelligence for sustainable materials; Rohit Mittal, Vibhakar Pathak and Amit Mithal; Chapter 2 ; Artificial Intelligence and IoT assisted sustainable manufacturing for industry 4.0; Mahin Anup, Gaurav Srivastava, Akruti Sinha, Devika Sapra and Deepak Sinwar; Chapter 3 ; Image Analysis Approaches for the Fault Detection to the Quality Assurance in Manufacturing Industries; Vijayakumar Ponnusamy and Dilliraj Ekambaram; Chapter 4 ; Manufacturing Data Performance Prediction and Optimization; Joshi A, Dahlia Sam, Sendilvelan S, Jayanthi K, Kanya N and Ethiraj N; Chapter 5 ; Data-Driven Optimization on the Workability and Strength Properties of M-30 Grade Concrete using MOORA; A. Anandraj, S. Vijayabaskaran and P.V. Rajesh; Chapter 6 ; Green Energy Harvesting using High Entropy Thermoelectric Alloys; Arun Raphel, Vivekanandhan. P, Kumaran.S; Chapter 7 ; Augmented and Virtual Reality Incorporation in the Manufacturing ; Industry 4.0; Kiruthiga Devi M, Kanya N, Ethiraj N and Sendilvelan S; Chapter 8 ; Modeling and Optimization of Process Parameter for Fatigue Strength Improvement by Selective Laser Melting of AlSi10Mg; Mudda Nirish and R Rajendra; Chapter 9 ; Role of Artificial Intelligence in Apparel Industry in the context of Industry 4.0 and Industry 5.0; Tulasi B and Sudhir Karanam; Chapter 10 ; Swarm Intelligence-based Automotive Manufacturing; Hiranmoy Samanta and Kamal Golui |
| Physical Description: | xiv, 196 Seiten Illustrationen 453 gr |
| ISBN: | 9781032191102 9781032191041 |
Staff View
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| 245 | 1 | 0 | |a Computational intelligence based optimization of manufacturing process for sustainable materials |c edited by Deepak Sinwar, Kamalakanta Muduli, Vijaypal Singh Dhaka, Vijander Singh |
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| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press, Taylor & Francis Group |c 2024 | |
| 300 | |a xiv, 196 Seiten |b Illustrationen |c 453 gr | ||
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| 490 | 0 | |a Computational and intelligent systems series | |
| 500 | |a Chapter 1 ; Introduction to Computational Intelligence for sustainable materials; Rohit Mittal, Vibhakar Pathak and Amit Mithal; Chapter 2 ; Artificial Intelligence and IoT assisted sustainable manufacturing for industry 4.0; Mahin Anup, Gaurav Srivastava, Akruti Sinha, Devika Sapra and Deepak Sinwar; Chapter 3 ; Image Analysis Approaches for the Fault Detection to the Quality Assurance in Manufacturing Industries; Vijayakumar Ponnusamy and Dilliraj Ekambaram; Chapter 4 ; Manufacturing Data Performance Prediction and Optimization; Joshi A, Dahlia Sam, Sendilvelan S, Jayanthi K, Kanya N and Ethiraj N; Chapter 5 ; Data-Driven Optimization on the Workability and Strength Properties of M-30 Grade Concrete using MOORA; A. Anandraj, S. Vijayabaskaran and P.V. Rajesh; Chapter 6 ; Green Energy Harvesting using High Entropy Thermoelectric Alloys; Arun Raphel, Vivekanandhan. P, Kumaran.S; Chapter 7 ; Augmented and Virtual Reality Incorporation in the Manufacturing ; Industry 4.0; Kiruthiga Devi M, Kanya N, Ethiraj N and Sendilvelan S; Chapter 8 ; Modeling and Optimization of Process Parameter for Fatigue Strength Improvement by Selective Laser Melting of AlSi10Mg; Mudda Nirish and R Rajendra; Chapter 9 ; Role of Artificial Intelligence in Apparel Industry in the context of Industry 4.0 and Industry 5.0; Tulasi B and Sudhir Karanam; Chapter 10 ; Swarm Intelligence-based Automotive Manufacturing; Hiranmoy Samanta and Kamal Golui | ||
| 520 | |a 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 | ||
| 650 | 4 | |a bicssc / Hydraulic engineering | |
| 650 | 4 | |a bicssc / Production & quality control management | |
| 650 | 4 | |a bicssc / Product design | |
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| 650 | 4 | |a bicssc / Artificial intelligence | |
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| 650 | 4 | |a bisacsh / TECHNOLOGY & ENGINEERING / Industrial Engineering | |
| 650 | 4 | |a bisacsh / TECHNOLOGY & ENGINEERING / Manufacturing | |
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| 700 | 1 | |a Singh, Vijander |4 edt | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035484832 | |
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| spelling | Computational intelligence based optimization of manufacturing process for sustainable materials edited by Deepak Sinwar, Kamalakanta Muduli, Vijaypal Singh Dhaka, Vijander Singh First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2024 xiv, 196 Seiten Illustrationen 453 gr txt rdacontent n rdamedia nc rdacarrier Computational and intelligent systems series Chapter 1 ; Introduction to Computational Intelligence for sustainable materials; Rohit Mittal, Vibhakar Pathak and Amit Mithal; Chapter 2 ; Artificial Intelligence and IoT assisted sustainable manufacturing for industry 4.0; Mahin Anup, Gaurav Srivastava, Akruti Sinha, Devika Sapra and Deepak Sinwar; Chapter 3 ; Image Analysis Approaches for the Fault Detection to the Quality Assurance in Manufacturing Industries; Vijayakumar Ponnusamy and Dilliraj Ekambaram; Chapter 4 ; Manufacturing Data Performance Prediction and Optimization; Joshi A, Dahlia Sam, Sendilvelan S, Jayanthi K, Kanya N and Ethiraj N; Chapter 5 ; Data-Driven Optimization on the Workability and Strength Properties of M-30 Grade Concrete using MOORA; A. Anandraj, S. Vijayabaskaran and P.V. Rajesh; Chapter 6 ; Green Energy Harvesting using High Entropy Thermoelectric Alloys; Arun Raphel, Vivekanandhan. P, Kumaran.S; Chapter 7 ; Augmented and Virtual Reality Incorporation in the Manufacturing ; Industry 4.0; Kiruthiga Devi M, Kanya N, Ethiraj N and Sendilvelan S; Chapter 8 ; Modeling and Optimization of Process Parameter for Fatigue Strength Improvement by Selective Laser Melting of AlSi10Mg; Mudda Nirish and R Rajendra; Chapter 9 ; Role of Artificial Intelligence in Apparel Industry in the context of Industry 4.0 and Industry 5.0; Tulasi B and Sudhir Karanam; Chapter 10 ; Swarm Intelligence-based Automotive Manufacturing; Hiranmoy Samanta and Kamal Golui 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 bicssc / Hydraulic engineering bicssc / Production & quality control management bicssc / Product design bicssc / Algorithms & data structures bicssc / Artificial intelligence bicssc / Electronics engineering bicssc / Automotive technology & trades bicssc / Mechanical engineering bicssc / Electrical engineering bicssc / Mathematics & science bicssc / Materials science bisacsh / TECHNOLOGY & ENGINEERING / Industrial Engineering bisacsh / TECHNOLOGY & ENGINEERING / Manufacturing (DE-588)4143413-4 Aufsatzsammlung gnd-content Sinwar, Deepak edt Muduli, Kamalakanta (DE-588)1326589474 edt Singh Dhaka, Vijaypal edt Singh, Vijander edt Erscheint auch als Online-Ausgabe 978-1-003-25771-4 |
| spellingShingle | Sinwar, Deepak Computational intelligence based optimization of manufacturing process for sustainable materials bicssc / Hydraulic engineering bicssc / Production & quality control management bicssc / Product design bicssc / Algorithms & data structures bicssc / Artificial intelligence bicssc / Electronics engineering bicssc / Automotive technology & trades bicssc / Mechanical engineering bicssc / Electrical engineering bicssc / Mathematics & science bicssc / Materials science bisacsh / TECHNOLOGY & ENGINEERING / Industrial Engineering bisacsh / TECHNOLOGY & ENGINEERING / Manufacturing |
| subject_GND | (DE-588)4143413-4 |
| title | Computational intelligence based optimization of manufacturing process for sustainable materials |
| title_auth | Computational intelligence based optimization of manufacturing process for sustainable materials |
| title_exact_search | Computational intelligence based optimization of manufacturing process for sustainable materials |
| title_full | Computational intelligence based optimization of manufacturing process for sustainable materials edited by Deepak Sinwar, Kamalakanta Muduli, Vijaypal Singh Dhaka, Vijander Singh |
| title_fullStr | Computational intelligence based optimization of manufacturing process for sustainable materials edited by Deepak Sinwar, Kamalakanta Muduli, Vijaypal Singh Dhaka, Vijander Singh |
| title_full_unstemmed | Computational intelligence based optimization of manufacturing process for sustainable materials edited by Deepak Sinwar, Kamalakanta Muduli, Vijaypal Singh Dhaka, Vijander Singh |
| title_short | Computational intelligence based optimization of manufacturing process for sustainable materials |
| title_sort | computational intelligence based optimization of manufacturing process for sustainable materials |
| topic | bicssc / Hydraulic engineering bicssc / Production & quality control management bicssc / Product design bicssc / Algorithms & data structures bicssc / Artificial intelligence bicssc / Electronics engineering bicssc / Automotive technology & trades bicssc / Mechanical engineering bicssc / Electrical engineering bicssc / Mathematics & science bicssc / Materials science bisacsh / TECHNOLOGY & ENGINEERING / Industrial Engineering bisacsh / TECHNOLOGY & ENGINEERING / Manufacturing |
| topic_facet | bicssc / Hydraulic engineering bicssc / Production & quality control management bicssc / Product design bicssc / Algorithms & data structures bicssc / Artificial intelligence bicssc / Electronics engineering bicssc / Automotive technology & trades bicssc / Mechanical engineering bicssc / Electrical engineering bicssc / Mathematics & science bicssc / Materials science bisacsh / TECHNOLOGY & ENGINEERING / Industrial Engineering bisacsh / TECHNOLOGY & ENGINEERING / Manufacturing Aufsatzsammlung |
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