| Description |
1 online resource (211 p.) |
| Series |
Computational and Intelligent Systems Series |
|
Computational and Intelligent Systems Series
|
| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- Chapter 1: Introduction to computational intelligence for sustainable materials -- 1.1 Computational intelligence -- 1.2 A reduction in the carbon footprint using CI Techniques -- 1.3 Computational sustainability research -- 1.4 Techniques for computational intelligence optimization -- 1.4.1 Grey wolf optimization -- 1.4.1.1 Applications of GWO in sustainability -- 1.4.2 Ant Colony Optimization (ACO) -- 1.4.2.1 Application of ACO in sustainability |
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1.4.3 Applications of the Monarch Butterfly Optimization method in sustainability -- 1.4.4 Application of Harris Hawks Optimization (HHO) in sustainability -- 1.4.4.1 Optimal power flow taking environmental emissions -- 1.5 Discussion and conclusion -- References -- Chapter 2: Artificial intelligence and IoT-assisted sustainable manufacturing for Industry 4.0 -- 2.1 Introduction -- 2.2 Related works -- 2.3 Sustainable manufacturing -- 2.3.1 Need for sustainable manufacturing -- 2.3.2 The role of IoT in sustainable manufacturing -- 2.3.3 Role of AI in sustainable manufacturing -- 2.4 Industry 4.0 |
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2.4.1 IoT in Industry 4.0 -- 2.4.2 AI in Industry 4.0 -- 2.5 The benefits of using IoT in industries -- 2.5.1 Increased efficiency -- 2.5.2 Predictive maintenance -- 2.5.3 Real-time data monitoring -- 2.5.4 Reduces cost -- 2.6 Benefits of using AI in industries -- 2.6.1 Direct automation -- 2.6.2 Continuous (24×7) production -- 2.6.3 Safety -- 2.6.4 Lower operational costs -- 2.6.5 Greater efficiency -- 2.6.6 Quality control -- 2.6.7 Quick decision making -- 2.7 Conclusion -- References -- Chapter 3: Image analysis approaches for fault detection in quality assurance in manufacturing industries |
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3.1 Introduction -- 3.1.1 Variety of defects and their classifications -- 3.1.2 Summary of various machine learning algorithms -- 3.1.3 Summary of various deep learning (DL) algorithms -- 3.1.3.1 Convolutional neural network (CNN) -- 3.1.3.2 Generative adversarial -- 3.1.3.3 Recurrent neural network (RNNs) -- 3.1.3.4 Radial basis function networks (RBFNs) -- 3.1.3.5 Long short-term memory networks (LSTMs) -- 3.1.3.6 Self-organizing maps (SOMs) -- 3.1.3.7 Restricted Boltzmann machines (RBMs) -- 3.1.3.8 Multilayer perceptron (MLPs) -- 3.1.3.9 Autoencoders -- 3.1.3.10 Deep belief networks (DBNs) |
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3.2 Various approaches to detect defects in casting products -- 3.2.1 Various deep learning approaches -- 3.2.1.1 CNN with MVGG19 network -- 3.2.1.2 Photometric stereo algorithm with customed segmentation network -- 3.2.1.3 Motif discovery with CNN -- 3.2.1.4 EfficientNet-B0 with CNN -- 3.2.1.5 Multi-optical image fusion (MOIF) with CNN -- 3.2.1.6 BoDoC methodology with support vector machine -- 3.2.1.7 SCN with ResNet-101 and Darknet-53 -- 3.2.1.8 Wasserstein generative adversarial nets (WGANs) with CNN -- 3.2.2 Discriminations of various approaches to defect detection |
| Notes |
Description based upon print version of record |
|
3.2.3 Pros and cons of each approach |
| Subject |
Manufacturing processes -- Data processing
|
|
Computational intelligence.
|
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Green products.
|
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Computational intelligence.
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Green products.
|
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Manufacturing processes -- Data processing.
|
| Form |
Electronic book
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| Author |
Muduli, Kamalakanta
|
|
Singh Dhaka, Vijaypal
|
|
Singh, Vijander
|
| ISBN |
9781000932966 |
|
1000932966 |
|
