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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Taylor & Francis
2024
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| Schriftenreihe: | Computational and Intelligent Systems
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| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | 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 |
| Beschreibung: | 210 Seiten 453 gr |
| ISBN: | 9781032191102 |
Internformat
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| 100 | 1 | |a Sinwar, Deepak |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials |
| 264 | 1 | |b Taylor & Francis |c 2024 | |
| 300 | |a 210 Seiten |c 453 gr | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Computational and Intelligent Systems | |
| 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 | |
| 650 | 4 | |a bicssc / Algorithms & data structures | |
| 650 | 4 | |a bicssc / Artificial intelligence | |
| 650 | 4 | |a bicssc / Electronics engineering | |
| 650 | 4 | |a bicssc / Automotive technology & trades | |
| 650 | 4 | |a bicssc / Mechanical engineering | |
| 650 | 4 | |a bicssc / Electrical engineering | |
| 650 | 4 | |a bicssc / Mathematics & science | |
| 650 | 4 | |a bicssc / Materials science | |
| 650 | 4 | |a bisacsh / TECHNOLOGY & ENGINEERING / Industrial Engineering | |
| 650 | 4 | |a bisacsh / TECHNOLOGY & ENGINEERING / Manufacturing | |
| 700 | 1 | |a Muduli, Kamalakanta |e Sonstige |4 oth | |
| 700 | 1 | |a Singh Dhaka, Vijaypal |e Sonstige |4 oth | |
| 700 | 1 | |a Singh, Vijander |e Sonstige |4 oth | |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035484832 | |
Datensatz im Suchindex
| _version_ | 1822807199438602240 |
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| adam_text | |
| any_adam_object | |
| author | Sinwar, Deepak |
| author_facet | Sinwar, Deepak |
| author_role | aut |
| author_sort | Sinwar, Deepak |
| author_variant | d s ds |
| building | Verbundindex |
| bvnumber | BV050148470 |
| ctrlnum | (DE-599)BVBBV050148470 |
| format | Book |
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| id | DE-604.BV050148470 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-31T23:00:09Z |
| institution | BVB |
| isbn | 9781032191102 |
| language | English |
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| owner_facet | DE-29T |
| physical | 210 Seiten 453 gr |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Taylor & Francis |
| record_format | marc |
| series2 | Computational and Intelligent Systems |
| spelling | Sinwar, Deepak Verfasser aut Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials Taylor & Francis 2024 210 Seiten 453 gr txt rdacontent n rdamedia nc rdacarrier Computational and Intelligent Systems 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 Muduli, Kamalakanta Sonstige oth Singh Dhaka, Vijaypal Sonstige oth Singh, Vijander Sonstige oth |
| 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 |
| 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 |
| title_fullStr | Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials |
| title_full_unstemmed | Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials |
| 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 |
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