Integration of mechanical and manufacturing engineering with IoT : a digital transformation

Integration of mechanical and manufacturing engineering with IoT a digital transformation

This book broadly explores the latest developments of IoT and its integration into mechanical and manufacturing engineering. It details the fundamental concepts and recent developments in IoT & Industry 4.0 with special emphasis on the mechanical engineering platform for such issues as product d...

Descripción completa

Detalles Bibliográficos
Otros Autores: Rajasekar, R., editor (editor), Moganapriya, C., editor, Kumar, M. Harikrishna, editor, Satish Kumar, Patri, editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons 2023.
Materias:
Internet of things > Industrial applications.
Engineering > Data processing.
Manufacturing processes.
Mechanical engineering.
Production engineering.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009752728206719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Chapter 1 Evolution of Internet of Things (IoT): Past, Present and Future for Manufacturing Systems
  • 1.1 Introduction
  • 1.2 IoT Revolution
  • 1.3 IoT
  • 1.4 Fundamental Technologies
  • 1.4.1 RFID and NFC
  • 1.4.2 WSN
  • 1.4.3 Data Storage and Analytics (DSA)
  • 1.5 IoT Architecture
  • 1.6 Cloud Computing (CC) and IoT
  • 1.6.1 Service of CC
  • 1.6.2 Integration of IoT With CC
  • 1.7 Edge Computing (EC) and IoT
  • 1.7.1 EC with IoT Architecture
  • 1.8 Applications of IoT
  • 1.8.1 Smart Mobility
  • 1.8.2 Smart Grid
  • 1.8.3 Smart Home System
  • 1.8.4 Public Safety and Environment Monitoring
  • 1.8.5 Smart Healthcare Systems
  • 1.8.6 Smart Agriculture System
  • 1.9 Industry 4.0 Integrated With IoT Architecture for Incorporation of Designing and Enhanced Production Systems
  • 1.9.1 Five-Stage Process of IoT for Design and Manufacturing System
  • 1.9.2 IoT Architecture for Advanced Manufacturing Technologies
  • 1.9.3 Architecture Development
  • 1.10 Current Issues and Challenges in IoT
  • 1.10.1 Scalability
  • 1.10.2 Issue of Trust
  • 1.10.3 Service Availability
  • 1.10.4 Security Challenges
  • 1.10.5 Mobility Issues
  • 1.10.6 Architecture for IoT
  • 1.11 Conclusion
  • References
  • Chapter 2 Fourth Industrial Revolution: Industry 4.0
  • 2.1 Introduction
  • 2.1.1 Global Level Adaption
  • 2.2 Evolution of Industry
  • 2.2.1 Industry 1.0
  • 2.2.2 Industry 2.0
  • 2.2.3 Industry 3.0
  • 2.2.4 Industry 4.0 (or) I4.0
  • 2.3 Basic IoT Concepts and the Term Glossary
  • 2.4 Industrial Revolution
  • 2.4.1 I4.0 Core Idea
  • 2.4.2 Origin of I4.0 Concept
  • 2.5 Industry
  • 2.5.1 Manufacturing Phases
  • 2.5.2 Existing Process Planning vs. I4.0
  • 2.5.3 Software for Product Planning-A Link Between Smart Products and the Main System ERP
  • 2.6 Industry Production System 4.0 (Smart Factory).
  • 2.6.1 IT Support
  • 2.7 I4.0 in Functional Field
  • 2.7.1 I4.0 Logistics
  • 2.7.2 Resource Planning
  • 2.7.3 Systems for Warehouse Management
  • 2.7.4 Transportation Management Systems
  • 2.7.5 Transportation Systems with Intelligence
  • 2.7.6 Information Security
  • 2.8 Existing Technology in I4.0
  • 2.8.1 Applications of I4.0 in Existing Industries
  • 2.8.2 Additive Manufacturing (AM)
  • 2.8.3 Intelligent Machines
  • 2.8.4 Robots that are Self-Aware
  • 2.8.5 Materials that are Smart
  • 2.8.6 IoT
  • 2.8.7 The Internet of Things in Industry (IIoT)
  • 2.8.8 Sensors that are Smart
  • 2.8.9 System Using a Smart Programmable Logic Controller (PLC)
  • 2.8.10 Software
  • 2.8.11 Augmented Reality (AR)/Virtual Reality (VR)
  • 2.8.12 Gateway for the Internet of Things
  • 2.8.13 Cloud
  • 2.8.14 Applications of Additive Manufacturing in I4.0
  • 2.8.15 Artificial Intelligence (AI)
  • 2.9 Applications in Current Industries
  • 2.9.1 I4.0 in Logistics
  • 2.9.2 I4.0 in Manufacturing Operation
  • 2.10 Future Scope of Research
  • 2.10.1 Theoretical Framework of I4.0
  • 2.11 Discussion and Implications
  • 2.11.1 Hosting: Microsoft
  • 2.11.2 Platform for the Internet of Things (IoT): Microsoft, GE, PTC, and Siemens
  • 2.11.3 A Systematic Computational Analysis
  • 2.11.4 Festo Proximity Sensor
  • 2.11.5 Connectivity Hardware: HMS
  • 2.11.6 IT Security: Claroty
  • 2.11.7 Accenture Is a Systems Integrator
  • 2.11.8 Additive Manufacturing: General Electric
  • 2.11.9 Augmented and Virtual Reality: Upskill
  • 2.11.10 ABB Collaborative Robots
  • 2.11.11 Connected Vision System: Cognex
  • 2.11.12 Drones/UAVs: PINC
  • 2.11.13 Self-Driving in Vehicles: Clear Path Robotics
  • 2.12 Conclusion
  • References
  • Chapter 3 Interaction of Internet of Things and Sensors for Machining
  • 3.1 Introduction
  • 3.2 Various Sensors Involved in Machining Process
  • 3.2.1 Direct Method Sensors.
  • 3.2.2 Indirect Method Sensors
  • 3.2.3 Dynamometer
  • 3.2.4 Accelerometer
  • 3.2.5 Acoustic Emission Sensor
  • 3.2.6 Current Sensors
  • 3.3 Other Sensors
  • 3.3.1 Temperature Sensors
  • 3.3.2 Optical Sensors
  • 3.4 Interaction of Sensors During Machining Operation
  • 3.4.1 Milling Machining
  • 3.4.2 Turning Machining
  • 3.4.3 Drilling Machining Operation
  • 3.5 Sensor Fusion Technique
  • 3.6 Interaction of Internet of Things
  • 3.6.1 Identification
  • 3.6.2 Sensing
  • 3.6.3 Communication
  • 3.6.4 Computation
  • 3.6.5 Services
  • 3.6.6 Semantics
  • 3.7 IoT Technologies in Manufacturing Process
  • 3.7.1 IoT Challenges
  • 3.7.2 IoT-Based Energy Monitoring System
  • 3.8 Industrial Application
  • 3.8.1 Integrated Structure
  • 3.8.2 Monitoring the System Related to Service Based on Internet of Things
  • 3.9 Decision Making Methods
  • 3.9.1 Artificial Neural Network
  • 3.9.2 Fuzzy Inference System
  • 3.9.3 Support Vector Mechanism
  • 3.9.4 Decision Trees and Random Forest
  • 3.9.5 Convolutional Neural Network
  • 3.10 Conclusion
  • References
  • Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry
  • 4.1 Introduction
  • 4.2 Need For IoT in Automobile Field
  • 4.3 Fault Diagnosis in Automobile
  • 4.4 Automobile Security and Surveillance System in IoT-Based
  • 4.5 A Vehicle Communications
  • 4.6 The Smart Vehicle
  • 4.7 Connected Vehicles
  • 4.7.1 Vehicle-to-Vehicle (V2V) Communications
  • 4.7.2 Vehicle-to-Infrastructure (V2I) Communications
  • 4.7.3 Vehicle-to-Pedestrian (V2P) Communications
  • 4.7.4 Vehicle to Network (V2N) Communication
  • 4.7.5 Vehicle to Cloud (V2C) Communication
  • 4.7.6 Vehicle to Device (V2D) Communication
  • 4.7.7 Vehicle to Grid (V2G) Communications
  • 4.8 Conclusion
  • References
  • Chapter 5 IoT for Food and Beverage Manufacturing
  • 5.1 Introduction
  • 5.2 The Influence of IoT in a Food Industry.
  • 5.2.1 Management
  • 5.2.2 Workers
  • 5.2.3 Data
  • 5.2.4 IT
  • 5.3 A Brief Review of IoT's Involvement in the Food Industry
  • 5.4 Challenges to the Food Industry and Role of IoT
  • 5.4.1 Handling and Sorting Complex Data
  • 5.4.2 A Retiring Skilled Workforce
  • 5.4.3 Alternatives for Supply Chain Management
  • 5.4.4 Implementation of IoT in Food and Beverage Manufacturing
  • 5.4.5 Pilot
  • 5.4.6 Plan
  • 5.4.7 Proliferate
  • 5.5 Applications of IoT in a Food Industry
  • 5.5.1 IoT for Handling of Raw Material and Inventory Control
  • 5.5.2 Factory Operations and Machine Conditions Using IoT
  • 5.5.3 Quality Control With the IoT
  • 5.5.4 IoT for Safety
  • 5.5.5 The Internet of Things and Sustainability
  • 5.5.6 IoT for Product Delivery and Packaging
  • 5.5.7 IoT for Vehicle Optimization
  • 5.5.8 IoT-Based Water Monitoring Architecture in the Food and Beverage Industry
  • 5.6 A FW Tracking System Methodology Based on IoT
  • 5.7 Designing an IoT-Based Digital FW Monitoring and Tracking System
  • 5.8 The Internet of Things (IoT) Architecture for a Digitized Food Waste System
  • 5.9 Hardware Design: Intelligent Scale
  • 5.10 Software Design
  • References
  • Chapter 6 Opportunities: Machine Learning for Industrial IoT Applications
  • 6.1 Introduction
  • 6.2 I-IoT Applications
  • 6.3 Machine Learning Algorithms for Industrial IoT
  • 6.3.1 Supervised Learning
  • 6.3.2 Semisupervised Learning
  • 6.3.3 Unsupervised Learning
  • 6.3.4 Reinforcement Learning
  • 6.3.5 The Most Common and Popular Machine Learning Algorithms
  • 6.4 I-IoT Data Analytics
  • 6.4.1 Tools for IoT Analytics
  • 6.4.2 Choosing the Right IoT Data Analytics Platforms
  • 6.5 Conclusion
  • References
  • Chapter 7 Role of IoT in Industry Predictive Maintenance
  • 7.1 Introduction
  • 7.2 Predictive Maintenance
  • 7.3 IPdM Systems Framework and Few Key Methodologies.
  • 7.3.1 Detection and Collection of Data
  • 7.3.2 Initial Processing of Collected Data
  • 7.3.3 Modeling as Per Requirement
  • 7.3.4 Influential Parameters
  • 7.3.5 Identification of Best Working Path
  • 7.3.6 Modifying Output With Respect Sensed Input
  • 7.4 Economics of PdM
  • 7.5 PdM for Production and Product
  • 7.6 Implementation of IPdM
  • 7.6.1 Manufacturing with Zero Defects
  • 7.6.2 Sense of the Windsene INDSENSE
  • 7.7 Case Studies
  • 7.7.1 Area 1-Heavy Ash Evacuation
  • 7.7.2 Area 2-Seawater Pumps
  • 7.7.3 Evaporators
  • 7.7.4 System Deployment Considerations in General
  • 7.8 Automotive Industry-Integrated IoT
  • 7.8.1 Navigation Aspect
  • 7.8.2 Continual Working of Toll Booth
  • 7.8.3 Theft Security System
  • 7.8.4 Black Box-Enabled IoT
  • 7.8.5 Regularizing Motion of Emergency Vehicle
  • 7.8.6 Pollution Monitoring System
  • 7.8.7 Timely Assessment of Driver's Condition
  • 7.8.8 Vehicle Performance Monitoring
  • 7.9 Conclusion
  • References
  • Chapter 8 Role of IoT in Product Development
  • 8.1 Introduction
  • 8.1.1 Industry 4.0
  • 8.2 Need to Understand the Product Architecture
  • 8.3 Product Development Process
  • 8.3.1 Criteria to Classify the New Products
  • 8.3.2 Product Configuration
  • 8.3.3 Challenges in Product Development while Developing IoT Products (Data-Driven Product Development)
  • 8.3.4 Role of IoT in Product Development for Industrial Applications
  • 8.3.5 Impacts and Future Perspectives of IoT in Product Development
  • 8.4 Conclusion
  • References
  • Chapter 9 Benefits of IoT in Automated Systems
  • 9.1 Introduction
  • 9.2 Benefits of Automation
  • 9.2.1 Improved Productivity
  • 9.2.2 Efficient Operation Management
  • 9.2.3 Better Use of Resources
  • 9.2.4 Cost-Effective Operation
  • 9.2.5 Improved Work Safety
  • 9.2.6 Software Bots
  • 9.2.7 Enhanced Public Sector Operations
  • 9.2.8 Healthcare Benefits.
  • 9.3 Smart City Automation.

Ejemplares similares