Big Data Analytics in Smart Manufacturing: Principles and Practices

✍ Scribed by P. Suresh, T. Poongodi, B. Balamurugan, Meenakshi Sharma

• Publisher: CRC Press/Chapman & Hall
• Year: 2022
• Tongue: English
• Leaves: 205
• Category: Library

✦ Synopsis

The significant objective of this edited book is to bridge the gap between smart
manufacturing and big data by exploring the challenges and limitations. Companies
employ big data technology in the manufacturing field to acquire data about the products.
Manufacturing companies could gain a deep business insight by tracking customer details,
monitoring fuel consumption, detecting product defects, and supply chain management.
Moreover, the convergence of smart manufacturing and big data analytics currently suffers
due to data privacy concern, short of qualified personnel, inadequate investment, long-term
storage management of high-quality data. The technological advancement makes the data
storage more accessible, cheaper and the convergence of these technologies seems to be
more promising in the recent era. This book identified the innovative challenges in the
industrial domains by integrating heterogeneous data sources such as structured data,
semi-structures data, geo-spatial data, textual information, multimedia data, social
networking data, etc. It promotes data-driven business modelling processes by adopting
big data technologies in the manufacturing industry. Big data analytics is emerging as a
promising discipline in the manufacturing industry to build the rigid industrial data
platforms. Moreover, big data facilitates process automation in the complete lifecycle of
product design and tracking. This book is an essential guide and reference since it
synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in
smart manufacturing domain. It also provides real-world scenarios and applications,
making it accessible to a wider interdisciplinary audience.

Features

• The readers will get an overview about the smart manufacturing system which enables optimized manufacturing processes and benefits the users by increasing overall profit.
• The researchers will get insight about how the big data technology leverages in finding new associations, factors and patterns through data stream observations in real time smart manufacturing systems.
• The industrialist can get an overview about the detection of defects in design, rapid response to market, innovative products to meet the customer requirement which can benefit their per capita income in better way.
• Discusses technical viewpoints, concepts, theories, and underlying assumptions that are used in smart manufacturing.
• Information delivered in a user-friendly manner for students, researchers, industrial experts, and business innovators, as well as for professionals and practitioners.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Editors
Contributors
1. Machine Learning Techniques and Big Data Analytics for Smart Manufacturing
1.1 An Overview of Smart Manufacturing
1.1.1 Upsides and Downsides of Smart Manufacturing
1.2 Machine Learning in Smart Manufacturing
1.2.1 Supervised Machine Learning in Smart Manufacturing
1.2.2 Unsupervised Machine Learning in Smart Manufacturing
1.3 Big Data Analysis in Smart Manufacturing
1.3.1 Infrastructure
1.3.2 Architecture
1.4 Comparative Study of Smart Manufacturing
1.5 Applications Used in Smart Manufacturing
1.5.1 Distinct Examination for Item Quality Assessment
1.5.2 Symptomatic Investigation for Shortcoming Appraisal
1.5.3 Prescient Examination for Deformity Anticipation
1.6 Challenges of Machine Learning in Smart Manufacturing
1.7 Advantage of Machine Learning in Smart Manufacturing
1.7.1 Deep Learning Model for Smart Manufacturing
1.7.2 Smart Manufacturing of Industrial IoT Robotics
1.7.3 Smart Factory Production
1.7.4 Data Clustering-Based ML
1.7.5 Imbalanced Data and Comparative Analysis in Smart Manufacturing
1.7.6 Human to Machine Applications for Smart Industry
1.8 Future of Smart Manufacturing
1.8.1 Smart 3D Printing Techniques Using AI and Cloud
1.8.2 Blockchain Secured Industry 4.0
1.8.3 Smart Transportation System
1.8.3.1 Safety and Security in Autonomous Vehicles
1.8.4 Augmented Reality in AI-Based Education System
1.9 Conclusion
References
2. Data-Driven Paradigm for Smart Manufacturing in the Context of Big Data Analytics
2.1 Introduction
2.2 Historical Background
2.3 Smart Manufacturing
2.4 The DT
2.5 Big Data
2.6 Data-Driven Paradigm
2.7 Conclusion
References
3. Data-Driven Models in Machine Learning: An Enabler of Smart Manufacturing
3.1 Introduction
3.2 3D Printing Process
3.2.1 3D Printing - Advantages
3.2.2 3D Printing - Disadvantages
3.2.3 3D Printing - Beneficiary Industries
3.2.4 3D Printing Techniques
3.2.4.1 Powder Bed Fusion
3.2.4.2 Selective Laser Sintering and Melting
3.2.4.3 Electron Beam Melting
3.2.4.4 Photo-Polymerization
3.2.4.5 Stereolithography
3.2.4.6 Digital Light Processing
3.2.4.7 Inkjet: Binder Jetting
3.2.4.8 Inkjet: Material Jetting
3.2.4.9 Material Extrusion
3.2.4.10 Selective Deposition Lamination (SDL)
3.3 Need for Parametric Analysis and Optimization in 3D Printing
3.4 ML Technique - Overview
3.4.1 Reasons for Adoption of ML in 21st Century
3.4.2 Popular Techniques of ML Applied in AM
3.4.2.1 Linear Regression
3.4.2.2 Artificial Neural Networks
3.4.3 Applications of ANN in 3D Printing
3.5 ML in Additive Manufacturing Industry - State of Art
3.6 Case Studies for the Experimental Data
3.6.1 Case Study I
3.6.2 Case Study II
3.7 Comparison of ML Analysis to Statistical Analysis Tools
3.8 Challenges Associated for Ml Applications to 3D Printing
3.8.1 Big Data Challenges
3.8.2 Scope of Issue Addressal/Advanced Techniques
3.8.2.1 Data Augmentation
3.8.2.2 Transfer Learning
3.8.3 Few-Shot Learning
3.9 Conclusions
References
4. Local Time Invariant Learning from Industrial Big Data for Predictive Maintenance in Smart Manufacturing
4.1 Portfolio of Predictive Maintenance and Condition Monitoring
4.1.1 Characteristics of Industry 4.0
4.1.2 Industry 4.0: Revolution or Evolution?
4.2 Condition Monitoring and Predictive Maintenance
4.2.1 Taxonomy of Maintenance Activities in Industries
4.2.1.1 Preventive Maintenance
4.2.1.2 Predictive Maintenance
4.3 Role of Predictive Maintenance in Smart Manufacturing
4.4 Niche of Big Data in Smart Manufacturing
4.4.1 Significance of RUL in Mechanical Machineries
4.5 Local Time Invariant Learning Through BGRU
4.5.1 Gated Recurrent Unit
4.5.2 Bidirectional GRU
4.6 RUL Prediction Through BGRU from Mechanical Big Data
4.7 Exploration of the Experimental Results
4.8 Conclusion
References
5. Integration of Industrial IoT and Big Data Analytics for Smart Manufacturing Industries: Perspectives and Challenges
5.1 Introduction
5.1.1 Industry Automation System
5.1.2 Industrial Automation Types
5.1.2.1 Fixed Automation System
5.1.2.2 Programmable Automation System
5.1.2.3 Soft Automation System
5.1.2.4 Integrated Automation System
5.2 Industry 4.0 Revolution
5.2.1 International Standards of Industry 4.0
5.3 IoT Components and Its Protocols
5.3.1 Things
5.3.2 Gateways
5.3.3 Cloud Gateway
5.3.4 Data Lake
5.3.5 Data Analytics
5.3.6 Machine Learning
5.3.7 Control Applications
5.3.8 User Applications
5.4 M2M Communication in Smart Manufacturing
5.5 IoT in Smart Manufacturing
5.5.1 Advanced Analysis
5.5.2 Inventory Monitoring
5.5.3 Remote Process Monitoring
5.5.4 Abnormality Reporting
5.6 Big Data Analytics in Smart Manufacturing
5.6.1 Self-Service Systems
5.6.1.1 Elimination of bottlenecks
5.6.1.2 Predictive Maintenance
5.6.1.3 Automation Production Management
5.6.1.4 Predictive Demand
5.7 Convergence of IIoT and Big Data Analytics
5.8 Smart Manufacturing in Industries
5.8.1 Building Blocks of Smart Manufacturing
5.8.1.1 Flat
5.8.1.2 Data-Driven
5.8.1.3 Sustainable
5.8.1.4 Agile
5.8.1.5 Innovative
5.8.1.6 Current
5.8.1.7 Profitable
5.8.2 IIoT Implementation
5.9 Smart Manufacturing in MSMEs
5.9.1 Smart Manufacturing in Large-Scale Industry
5.9.2 Intelligent Robots for Smart Manufacturing
5.9.2.1 Industrial Robots
5.9.2.2 Collaborative Robots
5.10 Challenges in Integrating Industrial IoT and Big Data Analytics
5.10.1 Privacy
5.10.2 Cyber Security
5.10.3 Scalability
5.10.4 Connectivity and Communication
5.10.5 Efficiency
5.11 Research Scope in IIoT
5.11.1 Energy Management
5.11.2 Heterogeneous QoS
5.11.3 Resource Management
5.11.4 Data Offloading Decision
5.12 Conclusion
References
6. Multimodal Architecture for Emotion Prediction in Videos Using Ensemble Learning
6.1 Introduction
6.2 Related Work
6.3 Dataset Acquisition
6.3.1 Dataset
6.3.2 Data Pre-Processing
6.4 System Design
6.4.1 System Pipeline
6.4.2 Convolutional Neural Network
6.4.2.1 Input Layer
6.4.2.2 Convolutional Layer
6.4.2.3 Dense Layer
6.4.2.4 Output Layer
6.4.3 Audio Feature Extraction
6.4.4 Support Vector Machine
6.4.5 Multi-Layer Perceptron
6.4.6 Ensemble Learning
6.5 System Implementation
6.5.1 Emotion Prediction from Videos: CNN Model Training
6.5.2 Emotion Prediction from Audio: SVM-MLP Training
6.5.3 Combining the Video and Audio Using Ensemble Learning
6.6 Result and Analysis
6.6.1 Testing the CNN Model
6.6.2 Testing the SVM and MLP Model
6.6.3 Testing the Ensemble Model
6.7 Conclusion
References
7. Deep PHM: IoT-Based Deep Learning Approach on Prediction of Prognostics and Health Management of an Aircraft Engine
7.1 Introduction
7.2 Overview of Prognostics and Health Management
7.3 Steps Involved in PHM
7.3.1 Data Acquisition
7.3.2 Data Pre-processing
7.3.3 Detection
7.3.4 Diagnostics
7.3.5 Prognostics
7.3.6 Decision-Making
7.3.7 Human-Machine Interface
7.4 PHM in Aerospace Industry
7.4.1 Sensors Used in the Gas Turbofan Engine
7.4.1.1 Temperature Sensors
7.4.1.2 Total Air Gas Temperature Sensor
7.4.1.3 Exhaust Gas Temperature Sensor
7.4.1.4 Vibration Sensors
7.4.1.5 Speed Sensors
7.4.1.6 Fuel Sensors for Flow
7.4.1.7 Altimeter Sensors
7.5 Dataset Description
7.5.1 Long Short-Term Memory
7.5.2 Experimental Analysis on C-MAPPS
7.5.2.1 Performance Metric Selection
7.5.2.2 Result Analysis
7.6 Conclusion
References
8. A Comprehensive Study on Accelerating Smart Manufacturers Using Ubiquitous Robotic Technology
8.1 Introduction
8.2 Related Works
8.3 Smart Manufacturing Systems
8.3.1 Why Do We Need Ubiquitous Robotics?
8.4 Ubiquitous Robotics
8.4.1 System Design
8.4.2 Part-Based Hardware Measure
8.4.3 Concept for Ubiquitous Industrial Robot Work Cell
8.5 Ubiquitous Computing
8.5.1 Advantages of Ubiquitous Computing
8.6 Conclusion
8.7 Future Scope
References
9. Machine Learning Techniques and Big Data Tools in Design and Manufacturing
9.1 Introduction
9.2 Literature Survey
9.2.1 Analytics in Climate Big Data
9.2.2 Problem and Challenges
9.3 Contribution
9.4 Proposed Method
9.5 Big Data Analysis
9.5.1 Benefits of Big Data Analytics
9.5.2 Data Understanding
9.5.3 Data Preparation
9.6 Feature Selection
9.6.1 FA-Based Feature Selection
9.7 Exploratory Analysis
9.8 Classification
9.8.1 NB
9.8.2 Multiple Regression-Logistic Regression
9.8.3 XGBoost Classifier
9.9 Evaluation
9.9.1 Methods and Modeling
9.10 Result and Discussion
9.10.1 Performance Analysis
9.11 Conclusion
References
10. Principle Comprehension of IoT and Smart Manufacturing System
10.1 Introduction to IoT
10.1.1 History and Evolution of IoT
10.2 IoT Platforms and Operating System
10.2.1 IoT Platforms
10.2.1.1 Hardware
10.2.1.2 Connectivity
10.2.1.3 Best Communication Option
10.2.1.3.1 IoT Connectivity Options
10.2.2 Operating System
10.2.2.1 Ubuntu Core
10.2.2.2 Contiki
10.2.2.3 Android Things
10.2.2.4 RIOT
10.3 Security in IoT Protocols and Technologies
10.3.1 IoT Network Protocols
10.3.1.1 Internet Protocol Network
10.3.1.1.1 HTTP (Hyper Text Transfer Protocol)
10.3.1.1.2 LoRaWAN (Longe-Range Wide Area Network)
10.3.1.2 Non-Internet Protocols Network
10.3.2 Security
10.4 Applications of IoT
10.4.1 Media
10.4.2 Transportation
10.4.3 Manufacturing
10.4.4 Building and Home Automation
10.4.5 Energy Management
10.4.6 Infrastructure Monitoring
10.4.7 Medical and Healthcare System
10.4.8 Environment Monitoring
10.5 IoT Application Fields
10.6 IoT Enabling Technologies
10.6.1 Cloud Computing
10.6.2 Big Data
10.6.3 Wireless Sensors Network
10.6.4 Communication Protocols
10.6.4.1 MQTT
10.6.4.2 CoAP
10.6.4.3 AMQP
10.6.5 Embedded System
10.7 IoT for Smart Manufacturing
10.7.1 IIoT Impact in Manufacturing
10.7.2 Supply Chain
10.7.3 Remote and Third-Party Operations
10.7.4 Remote Production Control
10.7.5 Predictive Maintenance
10.8 IoT Industrial Use Cases
10.8.1 ABB - Smart Robotics
10.8.2 Connected Vehicle
10.8.3 Asset Tracking
10.8.4 Smart Metering
10.9 Conclusion
References
Index