Industry 4.0 and Climate Change

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Editor biographies
List of contributors
1 Optimization of milling machine parameters by using Artificial Neural Network model
1.1 Introduction
1.2 Literature review
1.3 Methodology
1.3.1 Data collection from the experimental setup
1.3.2 Developing Artificial Neural Network model
1.3.3 Execution of the experiment
1.3.4 Prediction using ANN tool
1.4 Result and discussion
1.5 Conclusion
References
2 Facility layout optimization: Continuous improvement
2.1 Introduction
2.2 Literature review
2.3 Case study and methodology
2.3.1 Overview and general layout of the company
2.3.2 Gap in the current layout
2.3.2.1 Factor 1 – material flow and inventory handling
2.3.2.2 Factor 2 – productivity
2.3.2.3 Factor 3 – quality
2.3.2.4 Factor 4 – safety
2.4 Discussion and recommendations
2.5 Conclusion
References
3 Multi-criteria decision analysis applications and trends in manufacturing domain
3.1 Introduction
3.2 Applications and trends of MCDM in manufacturing decision-making
3.3 Conclusion
References
4 To implement Six Sigma to minimize defects in the manufacturing of draft gear of railway wagon
4.1 Introduction
4.2 Business process mapping (SIPOC diagrams)
4.2.1 Purpose
4.2.2 Definitions
4.3 Define phase
4.3.1 SIPOC logic
4.4 Measure phase
4.4.1 DPMO
4.4.2 Control chart
4.5 Analyze phase
4.5.1 Root cause analysis
4.5.2 Defect name: Scab
4.6 Improve phase
4.7 Control phase
4.8 Result and discussion
4.9 Conclusion
References
5 Changeover time reduction through SMED approach: Case study of an Indian steel processing centre
5.1 Introduction
5.2 Literature review
5.3 Methodology
5.3.1 Principles of SMED
5.4 Case study
5.4.1 Background
5.4.2 Analysis
5.4.3 Summary of recommendations
5.5 Results and discussion
5.6 Conclusion
References
6 SWOT analysis – on maintenance frameworks for SMEs
6.1 Introduction
6.1.1 Requirement of a maintenance framework
6.1.2 Framework comparison (conceptual basis)
6.2 SWOT analysis of maintenance frameworks
6.2.1 Group A frameworks
6.2.2 Group B frameworks
6.2.3 Group C frameworks
6.2.4 Group D frameworks
6.3 Conclusion
References
7 Development of maintenance framework for SMEs by an ISM approach
7.1 Introduction
7.2 Framework design requirement
7.3 Development philosophy of maintenance framework
7.3.1 Various elements of the proposed maintenance framework for SMEs
7.3.2 Development of key elements with sub-elements for the proposed framework for SMEs
7.4 ISM
7.4.1 Problem identification
7.4.2 Elements identifications
7.4.3 Structure-based decision
7.4.4 To Identify pair-wise relation
7.4.5 Development of SSIM (Self-Structural Interpretive Matrix)
7.4.6 Initial reachability matrix development
7.4.7 Incorporating transitivity and developing final reachability matrix
7.4.8 Partitioning of reachability and antecedent sets
7.4.9 Development of conical matrix
7.4.10 Digraph development
7.5 Conclusion
References
8 Multi-objective parametric optimization of wire electric discharge machining for Die Hard Steels using supervised machine learning techniques
8.1 Introduction
8.2 Research methodology
8.2.1 Dataset description
8.2.2 Supervised machine learning models
8.2.2.1 LASSO regression
8.2.2.2 K-Nearest Neighbors regression
8.2.2.3 Support Vector Regression
8.2.2.4 Artificial Neural Network regression
8.3 Results and discussion
8.3.1 LASSO regression
8.3.2 K-Nearest Neighbors regression
8.3.3 Support Vector Regression
8.3.4 Artificial Neural Network regression
8.4 Conclusion
References
9 Investigation of dragline productivity
9.1 Introduction
9.2 Dragline
9.3 Methodology
9.3.1 Current maintenance scenario
9.3.2 Electric supply
9.4 Analysis of dragline
9.4.1 Analysis of the data
9.4.2 Calculation of repair cost
9.5 Result and discussion
9.6 Conclusion
References
10 Lean administration in the Order-to-Cash process
10.1 Introduction
10.2 Methodology
10.3 Implementation
10.3.1 Phase I: understanding the As-Is scenario through swim lane and Kaizen bursts
10.3.2 Phase II: identic fi ation of activities/process steps as VA, NVA, and W
10.3.3 Phase III: setting targets and defining future-state map
10.4 Results and inferences
10.5 Conclusion
References
11 Modelling and analysis of Lean Six Sigma framework along with its environmental impact on the business process: A review
11.1 Introduction
11.2 Literature review
11.2.1 Six Sigma and Lean manufacturing in the context of Sustainability
11.2.2 Integrated impact of Green manufacturing and Lean Six Sigma
(LSS) on the manufacturing industries’ environmental performance
11.2.3 LSS framework advancements with time
11.3 Findings and discussions
11.4 Conclusion and recommendations
References
12 Optimum order allocation in a multi-supplier environment using linear programming model: Case study on heavy industry in India
12.1 Introduction
12.2 Literature review
12.3 Methodology
12.4 Case study
12.5 Conclusion
References
13 Formulation of an optimal ordering policy with quadratic demand: Weibull distribution deterioration and partial backlogging
13.1 Introduction
13.2 Assumptions and notations
13.3 Mathematical formulation
13.4 Numerical illustration
13.5 Sensitivity analysis
13.6 Conclusions
References
14 An optimal replenishment policy with exponential declining demand: Weibull distribution deterioration and partial backlogging
14.1 Introduction
14.2 Assumptions and notations
14.3 Model development and analysis
14.4 Numerical example
14.5 Sensitivity analysis
14.6 Conclusions
References
15 Smart materials advancements, applications and challenges in the shift to Industry 4.0
15.1 Introduction
15.2 Shape memory alloys
15.3 Applications of Nitinol
15.3.1 Aerospace
15.3.2 Actuators
15.3.3 Medical applications
15.4 Challenges
15.5 Conclusion
15.6 Future prospects
References
16 Virtual Try On – a study on the changing dimensions of
jewellery retailing through augmented reality
16.1 Introduction
16.2 Statement of the problem
16.3 Objectives of the study
16.4 Research methodology
16.5 Operational definitions of key terms
16.5.1 Virtual Try On
16.5.2 Augmented reality
16.5.3 Personalized marketing
16.6 Discussion and analysis
16.6.1 Virtual Try On centred marketing strategies of Indian branded jewellery retailers
16.7 Future research directions
16.7.1 User’s attitude and satisfaction with Virtual Try On
applications of luxury brands
16.7.2 Data privacy and Virtual Try On applications
16.7.3 Virtual Try On and its implications in smart retailing
16.8 Conclusion
References
17 Analysis of the barriers of blockchain adoption in Land Record System
17.1 Introduction
17.2 Review of literature
17.2.1 Barriers of blockchain usage for land records
17.2.2 Proposed framework for the land registry system
17.2.2.1 New transaction block
17.2.2.2 Smart land title contract
17.2.2.3 Algorithm for pre-agreement
17.3 Research methodology
17.4 Case study
17.5 Discussion
17.5.1 Theoretical implications
17.5.2 Practical implications
17.6 Conclusion
References
18 The concept of Industry 4.0: Role of ergonomics and Human Factors
18.1 Introduction
18.2 Advancement in technologies in industries with regard to technology advancement components
18.2.1 Ergonomics
18.3 Evolution of Industry 1.0 to 4.0
18.3.1 First Industrial Revolution
18.3.2 Industrial Revolution
18.3.3 Third Industrial Revolution
18.3.4 Fourth Industrial Revolution (Industry 4.0)
18.3.4.1 Key concepts
18.4 Ergonomics and Human Factors (HFs)
18.5 Case study: CEIT Ergonomics Analysis Application (CERAA)
18.6 Conclusions and future scope
References
19 Carbon nanotubes as an advanced coating material for cutting tool in sustainable production in Industry 4.0
19.1 Introduction
19.2 Synthesis of CNT
19.2.1 Physical Vapor Deposition (PVD) techniques
19.2.1.1 Pulse laser deposition
19.2.1.2 Arc discharge
19.2.2 Chemical Vapor Deposition (CVD) techniques
19.3 Purification of CNTs
19.4 Properties of CNTs
19.4.1 Physical properties
19.4.2 Electrical properties
19.4.3 Thermal properties
19.5 Applications of CNTs
19.5.1 Genetic engineering
19.5.2 Aerospace and automotive industry
19.5.3 Electronics and chip manufacturing
19.6 Conclusions and future scope
References
20 Integrating AI with Green Manufacturing for process industry
20.1 Introduction
20.2 Challenges of process safety in the context of Green Manufacturing (GM)
20.3 Framework of process safety for GM in the process industry
20.3.1 Artificial Intelligence
20.3.1.1 Integration of information via Knowledge Graph
20.3.1.2 Risk assessment and decision-making by using Bayesian Network
20.3.1.3 Early warning by using Deep Learning
20.4 Result and discussion
20.5 Conclusion
References
21 Sustainable recycling methods for different types of eco-friendly cutting fluids and their characteristics: An impetus for circular economy
21.1 Introduction
21.2 Eco-friendly cutting fluid
21.2.1 Vegetable-based cutting fluids
21.2.2 Minimum Quality Lubrication (MQL)
21.2.3 Bio-oil cutting fluid
21.2.4 Cryogenic cutting fluids
21.2.5 Nanofluids
21.3 Recycling methods
21.4 Conclusions
21.5 Future scope
21.6 Results and discussions
References
22 Sustainable automobiles: Major obstacles on the path of electrifying mobility in India, existing barriers and challenges
22.1 Introduction
22.2 Literature review
22.3 Growth and strategy
22.4 Barriers to Electric Vehicles (EVs) in the Indian market
22.4.1 Skill gap
22.4.2 Cost constraint
22.4.3 Consumer perception
22.4.4 Raw materials
22.4.5 Battery life
22.4.6 Driving range
22.4.7 Duration of charging
22.4.8 Safety regulations
22.4.9 Environmental factors
22.4.10 Government policies
22.4.11 Charging infrastructure
22.5 Discussing the present scenario
22.6 Conclusion and results
References
23 Development of heuristic DSS for supply chain architecture
23.1 Introduction
23.2 General optimization objectives of the supply chain network
23.3 Optimization models
23.4 Statement of an optimization problem
23.5 Literature review on multi-stage supply chain architecture models
23.5.1 Motivation for the study
23.6 Problem statement and research objectives
23.6.1 Research objectives
23.7 Optimization of three-stage supply chain architecture using NLIW-PSO algorithm
23.7.1 Particle development in PSO algorithm of three-stage supply chain architecture
23.7.2 Velocity determination and position modification equations used for the optimization of SCN architecture
23.7.3 Structure of NLIW-PSO algorithm
23.7.4 Development of initial set particles in the PSO algorithms
23.7.5 Generation of initial velocities in the proposed PSO algorithms
23.8 Performance analysis of SCN architecture
23.8.1 Experimental design
23.8.2 Pilot studies for parameters settings
23.8.3 Results and discussions
23.8.4 Supply Chain Setting (SCS)
23.9 Conclusion
Index